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Flux Balance Analysis of Plasmodium falciparum Metabolism
By
Farhan Raja
A thesis submitted in conformity with the requirements
for the degree of Master of Science Graduate Department of Biochemistry
University of Toronto
© Copyright by Farhan Raja (2010)
ii
Flux Balance Analysis of Plasmodium falciparum Metabolism
Farhan Raja
Master of Science, 2010
Graduate Department of Biochemistry
University of Toronto
Abstract
Plasmodium falciparum is the causative agent of malaria, one of the world‟s most
prevalent infectious diseases. The emergence of strains resistant to current therapeutics
creates the urgent need to identify new classes of antimalarials. Here we present and
analyse a constraints-based model (iMPMP427) of P. falciparum metabolism. Consisting
of 427 genes, 513 reactions, 457 metabolites, and 5 intracellular compartments,
iMPMP427 is relatively streamlined and contains an abundance of transport reactions
consistent with P. falciparum’s observed reliance on host nutrients. Flux Balance
Analysis simulations reveal the model to be predictive in regards to nutrient transport
requirements, amino acid efflux characteristics, and glycolytic flux calculation, which are
validated by a wealth of experimental data. Furthermore, enzymes deemed to be
essential for parasitic growth by iMPMP427 lend support to several previously
computationally hypothesized metabolic drug targets, while discrepancies between
essential enzymes and experimentally annotated drug targets highlight areas of malarial
metabolism that could benefit from further research.
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Acknowledgements
I would like to thank my supervisor (Dr. John Parkinson), my committee members (Dr.
Lynne Howell and Dr. Radhakrishnan Mahadevan), and all the members of the Parkinson
Lab.
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Table of Contents
Abstract………………….………………………………………………………………………… ii
Acknowledgements ……………………………………………………………………………... iii
Table Of Contents…….………………………………………………………………………….. iv
List Of Figures……………………………………………………………………………………. vi
List Of Tables……………………………………………………………………………………... vii
Abbreviations……………………………………………………………………………………… viii
Chapter 1: Introduction and Background…………………………………………………… 1
1.1 Malaria and Plasmodium falciparum………………………………………………………... 1
1.1.1 Malaria………………………………………………………………………………. 1
1.1.2 Plasmodium lifecycle ……………………………………………………………… 1
1.1.3 Erythrocyte membrane permeability and nutrient transport…………………… 4
1.1.4 Antimalarial drugs and drug targets .…………………………………………….. 5
1.1.5 The need for new antimalarials…………………………………………………… 7
1.2 Study of Metabolism …………………………………………………………………………... 8
1.2.1 Traditional metabolic network research………………………………………….. 8
1.2.2 Metabolic reconstructions in post-genomic era…………………………………. 9
1.2.3 Computational analysis of metabolic networks ………………………………… 11
1.2.3.1 Flux Balance Analysis of biochemical systems…………………… 12
1.2.3.2 Metabolic research using FBA…………………………………………. 15
1.3 Drug Discovery………………………………………………………………………………….. 16
1.3.1 Traditional drug discovery…………………………………………………………. 16
1.3.2 Drug screening and development………………………………………………… 16
1.3.3 Drug development in the post-genomic era……………………………………… 18
1.3.4 Drug discovery using FBA of metabolic reconstructions……………………….. 20
1.4 Project Objective……………………………………………………………………………….. 21
Chapter 2: Flux Balance Analysis of Plasmodium falciparum Metabolism…………….. 22
2.1 Overview…………………………………………………………………………………………. 22
v
2.2 Methods………………………………………………………………………………………….. 24
2.2.1 Metabolic reconstruction…………………………………………………………... 24
2.2.2 Flux Balance Analysis (FBA)……………………………………………………… 25
2.2.3 Biomass equation…………………………………………………………………... 26
2.2.4 Transport and reaction constraints……………………………………………….. 26
2.2.5 Nutrient transport and metabolic enzyme deletion……………………………... 28
2.3 Results and Discussion……………………………………………………………………….. 29
2.3.1 Reconstruction of Plasmodium falciparum metabolic network………………… 29
2.3.1.1 Reconstruction statistics and network overview……………………… 29
2.3.1.2 Comparison to other metabolic reconstructions……………………… 33
2.3.2 Metabolic characteristics…………………………………………………………… 35
2.3.2.1 Simulated growth environments……………………………………….. 35
2.3.2.2 Essential nutrients……………………………………………………….. 37
2.3.2.3 Optimal growth nutrients………………………………………………… 40
2.3.2.4 Amino acid transport variability…………………………………………. 43
2.3.3 Glycolytic flux……………………………………………………………………….. 46
2.3.4 Metabolic enzyme inhibitions ……………………………………………………... 48
2.3.5 Incorporation of other genome-scale data sets………………………………….. 53
Chapter 3: Conclusions and Future Work ………………………………………………….. 57
3.1 Conclusions …………………………………………………………………………………….. 57
3.2 Future Work ……………………………………………………………………………………. 57
References………………………………………………………………………………………… 62
Appendicies……………………………………………………………………………………...... 68
Appendix I: Metabolic reconstruction network reactions……………………………………….. 69
Appendix II: Derivation of biomass equation ……………………………………………………. 97
Appendix III: Nutrient simulation environments…………………………………………………. 103
Appendix IV: Predicted essential enzymes and annotated drug target datasets……………. 104
Appendix V: Classification of annotated drug target discrepancies…………………………… 107
vi
List of Figures
Figure 1. Overview of P. falciparum lifecycle.
Figure 2. Transport of nutrients through P. falciparum-erythrocytic system.
Figure 3. Statistics of P. falciparum metabolic network reconstruction iMPMP427.
Figure 4.
Schematic outline of P. falciparum metabolic reconstruction iMPMP427.
Figure 5. Comparisons of selected metabolic reconstructions.
Figure 6. Impact of nutrient transport constraints on parasite growth.
Figure 7. Flux variability analysis for transport fluxes associated with amino acids
and nitrogen species.
Figure 8. Glycolytic flux in P. falciparum.
Figure 9. Overlap of computationally predicted metabolic drug targets and those
that have been annotated as drug targets based on experimental evidence
Figure 10. Mappings of genome-scale data onto bipartite visualization of iMPMP427
vii
List of Tables
Table 1. Nutrients required for Plasmodium growth.
Table 2. Serum nutrients required for optimum P. falciparum growth.
viii
Abbreviations 1,2-DAG 1,2-diacylglycerol
5,10-MTHF 5,10-methenyltetrahydrofolate
1,3-bisG 1,3-bisphospho-D-glycerate
2PG 2-phosphoglycerate
3PG 3-phosphoglycerate
AcCoA acetyl coenzyme A
AcylCoA acyl coenzyme A
ADN adenosine
ADP adenosine diphosphate
aKG alpha-ketoglutarate
AMP adenosine monophosphate
ASP L-aspartate
ATP adenosine triphosphate
CARBM carbamoyl phosphate
CHOLP choline phosphate
CoA coenzyme A
CTP cytidine triphosphate
CYTS cysteine
DHAP dihydroxyacetone phosphate
DHFR dihydrofolate reductase
DHPS dihydrofolate synthase
DOLP-Man dolichyl phosphate D-mannose
DPP dimethylallyl diphosphate
dTTP deoxythymidine triphosphate
EM erythrocytic membrane
ETC electron transport chain
FBA flux balance analysis
FRC-1,6P beta-D-fructose 1,6-bisphosphate
FRC6P beta-D-Fructose 6-phosphate
G3P glyceraldehyde 3-phosphate
GDP guanosine diphosphate
gDW grams dry weight of malaria cell
GLA glyceraldehyde
GLC alpha-D-glucose
GLC6P alpha-D-glucose 6-phosphate
GLU L-glutamate
GLY L-glycine
GMP guanosine monophosphate
GPI glycosylphosphatidylinositol anchor
GSH glutathione
GSSG glutathione disulfide
GTP guanosine triphosphate
h hour
Hb human erythrocellular hemoglobin
HC homocysteine
HMBD 1-hydroxy-2-methyl-2-butenyl-4-diphosphate
ix
HYP hypoxanthine
IMP inosine monophosphate
iMPMP427 metabolic reconstruction of P. falciparum covering 427 genes
INO inosine
INS 1-phosphatidyl-D-myo-inositol
IPP isopentenyl diphosphate
LAC L-lactate
LP linear programming
METH methionine
mmol millimoles
MPMP Malarial Parasite Metabolic Pathways
N-Gly precursors for N-linked protein glycosylation
NIC nicotinamide
NID nicotinate
NPP new permeation pathway
ORT orotate
ORT5p orotidine 5'-phosphate
PANT pantothenate
PC phosphatidylcholine
PE phosphatidylethanolamine
PEP phosphoenolpyruvate
PPM parasitic plasma membrane
PS phosphatidylserine
PVM parasitophorous vacuole membrane
PYR pyruvate
R5P ribose 5-phosphate
RBC red blood cell
RIB riboflavin
ROI reactive oxidative intermediates
SAHC S-adenosylhomocysteine
SAM S-adenosylmethionine
SER L-serine
snGLY3P sn-glycerol 3-phosphate
SOR sorbitol
SPM sphingomyelin
SUC succinate
TCA tricarboxylic acid cycle
THF tetrahydrofolate
THM thiamine
TP toxopyrimidine
UDGNAG UDP-N-acetylglucosamine
UDP uridine diphosphate
UMP uridine monophosphate
UTP uridine triphosphate
XMP xanthine monophosphate
1
CHAPTER 1 Introduction and Background
1.1 Malaria and Plasmodium falciparum
1.1.1 Malaria
Malaria, an infectious disease caused by eukaryotic protozon Plasmodium parasites and
transmitted through mosquito vectors, is one of humanity‟s greatest health concerns. It is
widespread throughout the tropical and subtropical regions of the Earth, spanning large ranges of
South America, Africa, Middle East, and Asia. In 2008, there were 247 million reported cases
of malaria and nearly one million deaths [1]. Most deaths are among children living in Africa,
where the disease is responsible for 20% of all childhood deaths [1]. Malaria is associated with
poverty as poor sanitary conditions contribute to its transmission, and furthermore, infected
populations experience reduced economic production. Malaria can decrease gross domestic
product by as much as 1.3% in countries with high disease rates [1].
1.1.2 Plasmodium lifecycle
All four species of Plasmodium that are found to infect humans Plasmodium (P.
falciparum, P. vivax, P. ovale and P. malariae) share a common lifecycle with slight variations.
This lifecycle includes several distinct stages in a mosquito vector and a human host (Figure 1).
Malaria infection spreads when sporozoites found in the saliva of an infected feeding mosquito
are injected into a human host. These are carried by the circulatory system and invade host liver
cells. In the liver stage, the intracellular parasite asexually produces merozoites, which are
capable of invading host erythrocytes. Upon erythrocytic invasion, merozoites undergo a trophic
period in which the parasite enlarges. After about a 48-hour period, new merozoites are released
2
into the bloodstream, which go on to infect further erythrocytes. In the erythrocytic stage, the
early trophic parasite is often referred to as the 'ring form' because of its ring-like appearance
under a microscope. During this stage, parasitic metabolism is extremely active and includes
active ingestion of host cytoplasm and the proteolysis of hemoglobin into amino acids to sustain
its rapid growth rate. The growing parasite subsequently undergoes multiple rounds of nuclear
division without cytokinesis resulting in a group of cells termed a „schizont‟. New merozoites
bud from the mature schizont, and are released into the blood via rupture of the infected
erythrocyte [2]. Merozoites can differentiate into gametocytes, which are taken up by other
feeding mosquitoes. Gametocytes form a zygote, which develops into another invasive form
capable of penetrating epithelial tissue in the mosquito gut. Here the parasite undergoes multiple
rounds of asexual replication, resulting in the production of sporozoites. These are released into
the mosquito body cavity, and subsequently migrate to and invade the salivary glands,
completing the parasitic lifecycle.
3
Figure 1. Overview of P. falciparum lifecycle. The malarial lifecycle involves distinct stages in the
mosquito, human liver, and human erythrocyte. Non-intracellular forms of the parasite include the sporozoites,
merozoites, and gametocytes. The erythrocytic stage is characterized by rapid parasitic growth and
reproduction, and results in observed malarial symptoms.
The symptoms and pathogenicity of malaria are mainly due to the repeated invasion and rupture
of host erythrocytes. Infected patients typically experience intermittent fevers, which correlate
with the synchronous lysis of the infected erythrocytes. P. falciparum is considered to be the
most pathogenic of all Plasmodium strains in humans. This is due to higher levels of associated
parasitemia (infected erythrocytes), and more complicated infections due to the sequestration of
infected erythrocytes deep in human tissue [2].
4
1.1.3 Erythrocyte membrane permeability and nutrient transport
The malarial parasite is reliant on nutrient transport from its host system. The entire P.
falciparum-erythrocyte system is a complex multi-membrane arrangement, consisting of the
erythrocytic membrane (EM), parasitophorous vacuole membrane (PVM), and parasitic plasma
membrane (PPM) (Figure 2). Nutrients cross these membranes using a wide spectrum of
transport mechanisms (endocytosis, ion channels, ion pumps, and symport/uniport transporters) ,
which have been reviewed elsewhere [3]. RBCs infected with malarial parasites display
significantly increased permeability to small molecule nutrients [4]. It is thought that after
invasion, P. falciparum produces and exports proteins that either increase activity of native RBC
transporters or open new permeation pathways (NPPs) once interacting with the RBC membrane.
Once gaining entry into the erythrocyte, nutrients may transverse the PVM and plasma
membrane via proposed channels or a „parasitophorous duct‟ [5]. Since RBCs mainly function to
transport oxygen, they have little endogenous metabolic activity and nutrient import capability
[6]. The induced NPPs provide the parasitic P. falciparum access to a greater amount and
variety of nutrients, and is a key adaptation that has enabled it to reside in an erythrocytic host
[7].
5
Figure 2. Transport of nutrients through P. falciparum-erythrocytic system. Nutrients must cross any or
all of the three membrane barriers; erythrocytic membrane (EM), parasitophorous vacuole membrane (PVM)
and the parasitic plasma membrane (PPM). Plasmodium species express proteins that localize to the EM and
increase its relatively low permeability. Figure adapted from Kirk and Saliba (2007) [3].
1.1.4 Antimalarial drugs and drug targets
Early medical practitioners treated malaria fevers with blood-letting, hallucinogens such
as opium, and even correlated them with astronomical phenomena because of their periodic
nature. However, eventually successful herbal remedies were stumbled upon and spread
throughout the globe. These herbal remedies were based on the cinchona bark and qinghao herbs.
Subsequently, the active compounds of these remedies, quinine and artimisinin, were isolated by
chemists in 1820 and 1971, respectively [8]. Artimisinin and its derivates remain the most rapid-
acting treatments for human malaria caused by P. falciparum [9].
Further antimalarial therapeutics, such as chloroquine, primaquine, and amodiaquine
were developed in the middle of the 20th
century through the screening of several thousands of
compounds. Chloroquine quickly became the most widely used antimalarial because of its low
cost of production. However, resistance to chloroquine arose after only approximately 10 years,
and has now spread across sub-Saharan Africa [8].
6
The antifolates, sulfadoxine and pyrimethamine, were also developed in the middle of the
20th
century as analogues of folic acid, which were found to interfere with folate metabolism of
pathogenic microbes. However, resistance to antifolates has spread throughout Southeast Asia
and recently appeared in Africa [8].
Chloroquine and other quinoline containing anti-malarials, such as mefloquine and
quinine, affect the parasitic food vacuole. The food vacuole is a lysosome-like organelle, where
toxic heme from the digestion of haemoglobin, is converted to hemozoin crystals. Chloroquine,
the best understood of these antimalarials, functions by selectively accumulating in the food
vacuole by a combination of ion trapping of the chloroquine in the acidic vacuole due to low pH,
active transport through an internal transporter, and stable binding of chloroquine to a receptor in
the food vacuole. There, the accumulated chloroquine disrupts the formation of hemozoin and
the parasite is killed by the toxicity of free heme. Chloroquine resistance arises due to a
decreased accumulation of chloroquine in the food vacuole. Two different transporters (CRT and
MDR1) have been implicated in resistance. The functions of these transporters and their exact
roles in chloroquine resistance are not known [10]
Another important class of antimalarial drugs are the antifolates, which inhibit parasitic
enzymes involved in folate metabolism. Folates serve as co-factors in many reactions involving
the transfer of carbon groups. The malaria parasite requires the metabolic synthesis of folates,
thus the enzymes involved in this process are good drug targets. Dihydropteroate synthase
(DHPS) and dihydrofolate reductase (DHFR) are two commonly targeted enzymes. DHPS is
inhibited by the anitfolates sulfadoxine and dapsone, and DHFR is inhibited by pyrimethamine
and proguanil [10].
7
Several other anti-protozoal drugs are believed to function by imparting oxidative stress
on the parasite. Oxidative stress is encountered by virtually all cells through production of
reactive oxygen intermediates (ROI) in metabolic side reactions. The extremely unstable ROI
can destroy cellular material by oxidizing various biomolecules. However, levels of oxidative
stress can be increased by drugs that act as direct oxidants, or by drugs that interfere with the
natural defenses against the harmful molecules [10].
1.1.5 Need for new antimalarials
Malaria is one of the world‟s most prevalent infectious diseases. The most recent
statistics published by the World Health Organization (WHO) indicate that in 2008, there were
247 million cases of malaria and nearly one million deaths [1]. Additionally, Plasmodium strains
that are resistant to the most widely used chloroquine-based drugs have emerged. Considering
the widespread pathogenicity of malaria and emerging resistance to available therapeutic agents,
there is broad consensus that there is an urgent need to develop new antimalarial drugs [11].
Economic factors are another area of concern for malarial treatment. Since the majority
of malaria sufferers reside in poverty-stricken areas of the globe, pharmaceutical companies are
hesitant to commit resources towards antimalarial research [11]. The drug development process
requires a significant investment of time and capital. Pharmaceutical companies must consider a
host of factors when deciding if such an investment is profitable, and among these factors is what
price the final product could be sold for to the end patients.
Several reviews of antimalarial drug research have suggested that improvements in drug
discovery, especially the notion of target-based drug design resulting from developing genomic
8
technologies, may go a long way towards increasing the economic feasibility of antimalarial drug
development [11-13].
1.2 Study of Metabolism
1.2.1 Traditional metabolic network research
Metabolism refers to the set of chemical reactions that enable living cells to grow,
reproduce, respond to their environments, and carry out other cellular functions. Metabolic
reactions are usually organized into pathways, in which one chemical metabolite is transformed
into another through a series of enzymes. The chemical transformations that comprise metabolic
pathways were initially hypothesized during the 19th
century but only elucidated in detail during
the 20th
century. Generally speaking, the step-by-step elucidation of metabolic processes was a
painstaking process that required great scientific effort. For example, glycolysis was one of the
earliest metabolic pathways to be elucidated. Its discovery can probably be traced back to
Pasteur‟s experiments near the end of the 19th
century, which showed that yeast cells fermented
sugar to alcohol. At the dawn of the 20th
century, it was found that yeast cell extracts (as
opposed to living cells) could produce the same reaction, which led to the identification of
biological enzymes. The exact chemical steps in the biochemical breakdown of sugar into
carbon dioxide, which is commonly termed glycolysis, was gradually discovered over a period of
approximately 40 years by the combined work of many scientists. It was only by the 1940‟s, that
the complete glycolytic pathway (including all enzymes, intermediates, and coenzymes) was
known [14]. Similarly, as the knowledge of biochemical enzymes increased, other ubiquitous
metabolic pathways such as the Krebs cycle and amino acid formation pathways were
concurrently elucidated.
9
By the end of the 20th
century a wealth of metabolic pathway information was established,
especially for model organisms such as Escherichia coli and other industrially/medically
important species such as human tissue cells. However, the metabolic capabilities for any given
organism of interest were an area of uncertainty. Pathways could be hypothesized and
investigated experimentally by testing for common start and end points (i.e. observing nutrient
uptake specificities and excretion of end products). In this case researchers could still not be
certain whether other pathways were not present in the organism or simply not used under the
environmental conditions that were tested. Furthermore, metabolic characteristics such as flux
through specific pathways and regulation in response to changing environmental stimuli could
only be investigated though experimental work involving molecular tracing techniques [14].
Both of these aspects of metabolic study have been revolutionized by the application of genome
sequencing and computational simulation of reconstructed metabolic networks.
1.2.2 Metabolic reconstructions in post-genomic era
In the post-genomic era of biology, metabolic network reconstructions can be generated
for an organism of interest without the availability direct biochemical information, due to the
availability of genome sequencing and annotation data.
A metabolic reconstruction essentially refers to determining the set of metabolic enzymes
present in an organism‟s metabolic network, and the associated metabolites, stoichiometry,
reversibility and localization for the reactions that they catalyze. The starting point for a
reconstruction is an annotated genome sequence of the organism of interest, which can generally
be obtained from databases such as EntrezGene or from organism-specific databases such as
EcoCyc [15] for E. coli. Genome annotation most importantly indicates the gene products
10
thought to interact or form metabolic enzymes in the target organism. From here, information
regarding the reactions that these supposed enzymes catalyze can be extracted from metabolic
databases, such as KEGG [16], using automated tools. Although the automated reconstruction
step is rapid, the reconstruction after this stage requires manual curation, which is more time-
consuming and tedious. The draft reconstruction must be thought of as a first hypothesis of the
metabolic reactions that are encoded by a genome. At this stage the reconstruction is likely to
contain gaps and/or additional reactions that do not actually occur in the target organism. Manual
curation aims to confirm that the reactions extracted from the metabolic databases are indeed
present in the organism, add any reactions that are thought to be missing, and modify the
reactions with any organism-specific features, such as substrate or cofactor specificity and sub-
cellular localization. These tasks have traditionally required expert knowledge of the organism
of interest, but can also be carried out by referencing textbooks, experimental literature, and
increasingly available organism-specific online databases. The curated metabolic reconstruction
is valuable because it can be computationally analyzed using modeling techniques, as outlined in
the next section [17].
Before the prevalence of widespread genome sequencing, organism-specific knowledge
of which metabolic reactions and pathways were present was limited to model species such as
E.coli, and industrially/medically important species such as human tissue. However, genomic-
based metabolic reconstruction techniques have made this information more widespread. A
recent count indicates that genome-scale metabolic reconstructions have been carried out for
approximately 32 species [18], and this total is growing rapidly. Since genome sequencing is
generally a much more rapid process than genome-scale metabolic reconstruction, there is an
increasing gap between number of available sequenced genomes and reconstructions [19].
11
1.2.3 Computational analysis of metabolic networks
With the increasing availability of genome-scale metabolic models, systems level
modeling, simulations and analysis of metabolic networks are ever more possible. These
simulations can offer great insight towards various aspects of cellular function, and can be used
to study aspects of metabolism that would be extremely time-consuming or expensive to
reproduce experimentally. Various computational approaches have been developed to study the
organization and operation of metabolic pathways and networks. Generally, the different
network analysis strategies can be grouped into three major approaches: graph-based, constraint-
based, and mechanism-based modelling [20]. In this section, the general characteristics of the
different approaches will be described, and the methodology that has been applied in this study
(constraint-based modelling) will be described in detail.
Graph-based analysis can be used to examine patterns of interaction between the
components in a metabolic network. Enzymes and metabolites can be represented as nodes in a
bipartite graph, with edges representing interactions through biochemical reactions (ignoring the
strengths of these interactions in terms of stoichiometries or kinetics). Subsequently, various
network statistics may be used to gain insights about global network organization. For example,
researchers have shown that the degree of node connectivity in metabolic networks for many
organisms follows a power-law distribution, meaning that generally nodes have few neighbours,
except for a few network “hubs” that have a high degree of connectivity [21].
The constraint-based approach uses the natural restrictions on metabolism that are
imposed by the principle of conservation of mass, which are represented by network
stoichiometries. Thus, the cellular phenotype is constrained to a set of feasible states. By
assuming that the system lies at steady-state and using algebra techniques stemming from convex
12
analysis, one can perform Flux Balance Analysis (FBA) on the system, which can lead to a wide
variety of analyses [20], as described further below.
Mechanism-based approaches use the detailed information for metabolism, signal
processing, and gene regulation to generate precise predictions of cellular dynamics. However,
these models require detailed knowledge of the overall process mechanisms, organism-specific
concentrations and kinetic parameters of the process components. Since the required data is not
available for many organisms, mechanism-based models are best reserved for widely studied
small-scale processes in model organisms, such as the dynamics of the lac operon genetic system
in E. coli [20].
Ideally, in silico models should cover large portions of cellular networks (as in graph-
based models) and contain detailed dynamic considerations (as in mechanism-based models).
However, due to the lack of widespread kinetic data, these models are not currently feasible. On
the other hand, constraint-based models are widely used, especially to analyse genome-scale
metabolic reconstructions, as they offer an attractive mix of characteristics from graph-based and
mechanism-based modelling. Constraint-based models require few biochemical parameters, and
can cover a large genome-scale network, yet they provide more insight into cell physiology than
graph networks, since they predict reaction fluxes based on stoichiometric considerations [20].
1.2.3.1 Flux Balance Analysis of biochemical systems
Flux balance analysis (FBA) has gained popularity for simulating cellular metabolism
from genome-scale metabolic reconstructions. FBA involves representation of a metabolic
network as a system of linear algebraic equations, and application of linear optimization in order
to determine steady-state reaction flux distribution for optimization of a defined cellular
objective. FBA requires the stoichiometry of the network to be known, which is captured in a
13
stoichiometric matrix. It is assumed that cellular metabolism has evolved to optimize its reaction
flux distribtion in order to maximize cellular growth (or another stated objective).
Typically FBA is carried out in distinct stages. The first step is similar to any modeling
procedure, and that is to define the system of interest. In the case of a metabolic network, one
needs to define all metabolic reactions, and associated metabolites, enzymes,
compartmentalization, and reversibility. Additionally, metabolites for which transport reactions
are required need to be identified. This usually includes carbon sources such as glucose and
lactate, and ubiquitous co-factors such as CO2 and H2O. The accumulation of reaction
information is precisely what is gathered during the metabolic reconstruction stage, which
naturally leads to FBA of the reconstructed network.
The next step in FBA involves performing a mass balance with respect to each metabolite
in the system. This is accomplished by mathematically representing the network using a
stoichiometric matrix, (Sm,n), where the number of rows (m) and columns (n) represent the
number of metabolites and reactions, respectively. Mass conservation dictates that for each
metabolite concentration, x, and reaction, v, in the metabolic network:
dx/dt = S * v
which simplifies to:
S * v = 0
at steady state.
Further constraints on the reactions take the form:
α ≤ v ≤ β
where α and β are lower and upper bounds to reaction v, respectively.
14
Since any given metabolic network typically consists of more reactions than metabolites
(n > m), the system is under-determined, containing n–m free variables or degrees of freedom.
However, the reaction fluxes can be solved for by optimizing with respect to a stated objective
function, forming the classic linear programming (LP) problem:
Max cTv s.t. S * v = 0
where c represents the objective function.
The mass balances on network metabolites form the initial system constraints.
Algebraically, these constraints form a bounded solution space wherein every possible flux
distribution must lie. Further constraints can be imposed by considering reaction
thermodynamics (limiting reaction reversibility) and enzyme capacities (limiting reaction or
transport flux). These constraints should represent high confidence biochemical rules that the
metabolic system must obey, which serve to further bound the solution space and eliminate
implausible reaction flux values. Experimental measurements can be used to form these enzyme
capacity constraints by experimentally determining reaction rates or uptake rates, though this is
not always possible [22].
The specification of a suitable objective function is also an important consideration in the
FBA process. The objective function must represent the “biochemical objective” of the
metabolic network, as flux distribution through network reactions will be predicted that
optimizes flux through this specified reaction. The standard objective function typically attempts
to maximize growth (i.e. production of biomass), which has generally been found yield results
consistent with experimental observations. However, in some cases other objective functions
have also been found to be accurate, such as: minimization of ATP production or the
minimization of the uptake of a certain [22]. A biomass reaction to be used as an objective
15
function is created by representing the formation of cellular components as a stoichiometrically
balanced reaction.
The last stage of FBA involves optimization of the linear program formed by the selected
objective function, and set of constraints formed by metabolic mass balancing and other reaction
knowledge, to obtain a simulated steady state flux distribution. This can be accomplished using
any one of a number of freely available or commercial LP solvers, which can be applied on their
own or as part of a larger flux analysis software suite. For example, the COBRA Toolbox for
MATLAB interfaces with a variety of LP solvers (LINDO, CPLEX and GLPK) [23]. This has
emerged as perhaps the most common tool for metabolic flux analysis as it is freely available,
and provides many helpful functions for the output and analysis of components [24].
1.2.3.2 Metabolic research using FBA
FBA yields a predicted metabolic flux distribution for a stated biological objective, and
has been applied to gain insights into the organization and behaviour of metabolic pathways that
would be very difficult to uncover by experimental means.
For example, in one study researchers computationally investigated alterations of internal
metabolic fluxes in response to environmental variations. The activity of reactions in three
microorganisms (E. coli, Helicobacter pylori, and Saccharomyces cerevisiae) was assessed by
simulating 30,000 different growth conditions. This was done by randomly constraining nutrient
uptake rates to specific values (each set of nutrient uptake rates consists a new „growth
condition‟) and observing the predicted fluxes of metabolic reactions upon optimization. It was
found that a set of metabolic reactions, termed the “metabolic core” remained active (carried
non-zero flux) under all growth conditions. These reactions were also found to be highly
16
correlated with each other, meaning that the fluxes of these reactions changed in unison.
Furthermore, it was found that amino acid sequences of the enzymes involved in catalyzing the
active reactions were relatively more evolutionarily conserved than others, suggesting a
significant selective advantage for keeping these enzymes free of random mutations [25].
In another study, a reconstruction of the metabolic network of Leishmania major, a
single-celled eukaryotic human pathogen, was developed and analysed. A minimal medium for
growth of L. major was hypothesized by systematically constraining nutrient transport reactions
to zero, and noting those that led to the elimination of biomass production. Furthermore, the
effect of therapeutic ATPase inhibitors was simulated by constraining the ATPase reaction in the
model to carry fractions of the flux that it was observed to carry in its optimal phase, and noting
the effects this constraint has on biomass production [26].
1.3 Drug Discovery
1.3.1 Traditional drug discovery
Pharmaceutical drugs are largely composed of medicines that have been developed from
prototype molecules, which have remained essentially unchanged from their natural source, and
medicines based on analogues of initial prototypes that have either replaced their predecessors or
been found to serve a new therapeutic niche [27]. The most cited study in this field indicates that
there are approximately 250 drug prototypes, from which 1200 medicinal compounds have been
derived. Furthermore, until the middle of the 20th century, most drug prototypes were derived
from plants, but as microbial knowledge increased, these became the major sources of drug
prototypes [28]. The mechanisms of action of successful drugs have not been of major concern
in traditional drug development methods, though this information has gradually become
17
available. A review of the biochemical targets that are employed by pharmaceutical drugs as of
1996 indicated that 45% of known drugs target cellular receptors, 28% target metabolic enzymes,
11% target hormones, 5% target ion channels, 4% target nuclear receptors and DNA, and 7%
have an unknown target [29].
1.3.2 Drug screening and development
The development of a pharmaceutical drug starting from a potential drug prototype is a
long and expensive procedure. Essentially, the process attempts to determine if a compound of
interest generates the desired therapeutic effects with minimal or acceptable associated side
effects. The studies of a potential drug are usually undertaken using a tiered screening approach,
where each progressive level generates more specific data about the compound‟s performance.
The detailed protocols at each successive tier for determining and optimizing lead molecules will
not be discussed here, but the general strategy and issues involved are described below.
The tiered levels can be thought of as successive decision points in the drug development
process, at which researchers must decide whether to continue studying the possibility of the
compound of interest being developed into a marketable therapeutic. Generally speaking, the
experimental assays at each level measure an associated activity criterion, and compounds that
meet activity criteria are passed onto the next level [30]. Compounds can be eliminated from the
process due to high toxicity in humans, low efficacy, or lack of bioavailability of the active
moiety in humans. Very few screened molecules have therapeutic value. For example, in high-
throughput screens, approximately only one out of 10,000 synthesized or isolated potential
therapeutic compounds will survive the screening process and be used in a pharmaceutical drug.
Thus, it is of utmost importance to identify compounds that will ultimately be eliminate (not
18
form drugs) as early as possible, since each successive stage in the drug development process is
more expensive and time consuming. This allows a concentration of resources on compounds
that have the greatest potential of serving as therapeutic agents [30].
1.3.3 Drug discovery in the post-genomic era
The gene sequencing and annotation revolution of the 21st century has made genomic
information potentially available for virtually any organism of interest. This progression has
been predicted to usher in an age of greater overall understanding of biology and as a result,
improved drug discovery.
As described above, historically drug discovery has relied heavily on the screening of
various chemical entities in a trial-and-error approach in the hopes of observing therapeutic
effects. However, it was thought that since gene sequencing provides a “parts” list for an
organism, understanding how these parts interact and contribute to disease would enable
researchers to develop drugs in a rational target-based approach. This paradigm shift would
benefit drug discovery in two ways. For one, drugs with novel mechanisms of action may be
designed. Currently the entire pharmaceutical industry is reliant on a limited number of drug
targets. As of 2005, it was estimated that about 100 drug targets are responsible for all
prescription drugs on the market [31]. It is reasonable to assume that there exist potentially
untapped drug targets that could lead to a drug revolution if uncovered. Secondly, rational drug
design conceivably puts forth fewer, higher-confidence drug molecules to be validated and
safety-tested via the clinical trial process than traditional methods. This would result in great
savings to the pharmaceutical industry as the tiered validation process of drug development is
extremely expensive, and it is beneficial to eliminate flawed drugs as early as possible. However
19
thus far, the general consensus is that the genomic era has not yielded the gains in drug discovery
initially expected [32]. This can be partially explained by the fact obtaining the parts list of a
cell does not automatically lead to understanding how these function together and contribute
towards cell physiology.
Nevertheless, drug candidates contributed by genomics technologies are currently in the
drug-discovery pipeline. As described above this validation pipeline can take upwards of 10-20
years from initial stages to final drug production. As of 2005, although only about 6% of New
Molecular Entities approved by the FDA in the previous decade were novel drug prototypes, an
increasing proportion of such drugs have been identified through target-based approaches [33].
However, proposed drugs with novel targets (modes of action) are less likely to progress to
market than drugs with established targets. For example, it has been found that only 9% of new
drug targets progress successfully from first patient dose to market versus 23% for a drug with an
established target (http://www.cmr.org). As a whole, very few novel targets are utilized by the
drug development community. Considering all of the new drugs launched annually, only about
1–3 new drug targets are introduced per year [34]. Thus, though progress has been made, overall
the scientific community is in the stage of figuring out how to utilize the plethora of newly
available biological data in the post-genome era towards the development of drugs. Thus, more
effort is required to understand how gene products predicted through sequencing interact and can
be affected by drugs.
20
1.3.4 Drug discovery using FBA of metabolic reconstructions
FBA of reconstructed genome-scale metabolic models represent one avenue of utilizing
the plethora of genome sequencing data towards identifying novel drug targets for pathogenic
microbes. Analysis of the metabolic network model provides an avenue for examining the
systematic effects of metabolic disruption. For a given pathogenic organism of interest, a model
of its metabolic network can be obtained and computationally represented for flux analysis as
described in Section 1.2.3. Following this, network perturbations in the form of enzyme and
transport reaction deletions (or “knockouts”) can be studied. This is done by effectively deleting
reactions from the system and noting the effects on predicted growth rates. By repeating this
process for each individual enzyme in the system, one can ascertain effects of inhibiting each
enzyme in the metabolic network. Enzymes are considered “essential” if they lead to zero
growth when eliminated from the system. This would mean that the enzyme is required to
produce a biomass component in the network, and an alternate pathway does not exist. A similar
process can be repeated for nutrient transport reactions in the metabolic network. Essential
enzymes and transport reactions form a putative list of drug targets against the organism of
interest. These targets require further screening to eliminate undruggable enzymes, such as those
that share a high similarity to human enzyme counterparts making them difficult to selectively
inhibit. Furthermore, small molecules that would inhibit essential enzymes (as determined by
molecular modeling techniques) would make good candidates to enter the tiered drug screening
process as described in Section 1.3.1.
Previously, FBA was employed by Raman and colleagues towards identifying novel drug
targets for Mycobacterium tuberculosis [35]. In this study, a metabolic reconstruction of
Mycobacterial mycolic acid metabolism (which is known to be important for their growth,
21
survival, and pathogenicity) was analysed in order to identify essential gene products for mycolic
acid biosynthesis. After identifying these essential genes and screening out those displaying high
similarity to human sequences, it was predicted that the genes AccD3, Fas, FabH, Pks13,
DesA1/2, and DesA3 were potential novel anti-tubercular drug targets [35]. Many of these have
since been experimentally investigated [36-39]. However, the potential use of these targets by
novel anti-tuberculosis drugs is likely too premature to be reported [40].
1.4 Project Objective
The objective of this study is to apply the aforementioned post-genomic approaches towards
the study of the important human parasite P. falciparum. This will involve obtaining a
reconstruction of P. falciparum that has been curated based on genome annotation and published
literature, and computationally analysing it using FBA and related constraints-based techniques.
This will enable us to highlight key aspects of malarial metabolism, potentially identify novel
metabolic drug targets, and highlight areas of malarial metabolism that may benefit from further
research.
22
CHAPTER 2 Flux Balance Analysis of Plasmodium falciparum Metabolism
Much of the material in this section has been compiled into a research article entitled
“Flux Balance Analysis of Plasmodium falciparum: Insights into a Parasite‟s Metabolism” for
submission to a peer-reviewed journal. Consent to include this material in this thesis has been
obtained from the co-authors, and contribution of authors has been described in the relevant
figure captions. The co-authors of this work are: Dr. John Parkinson, Dr. James Wasmuth,
Stacy Hung, and Tuan On, who are all current members of the Parkinson Lab (Program in
Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario) where this work
was carried out.
2.1 Overview
As described in the proceeding chapter, P. falciparum is the causative agent of malaria in
humans and thus requires investigation into its metabolic capabilities and identification of
potential drug targets. Here we have applied computational techniques that have been developed
in the post-genome era of biology to meet these goals. Specifically, in this chapter the Flux
Balance Analysis (FBA) of P. falciparum metabolism is presented. First, the methodology of
obtaining a reconstruction of malarial metabolism is described, along with methodology relating
to the formation of a biomass equation and transport constraints that are required to carry out
FBA of this network. Subsequently, results of the analysis are shown and discussed. Network
statistics are used to place the metabolism of P. falciparum in context with other studied
microorganisms. Nutrient transport, an important metabolic feature, is characterized by
inhibiting transport reactions and classifying those that are essential or required for optimal
23
parasitic growth. Furthermore, predicted flux through the critical energy-producing glycolysis
pathway is visualized. In an attempt to identify metabolic drug targets, enzymes that are
predicted to be essential for parasitic growth are compared to those that are annotated to be drug
targets, and those that have been predicted by other computational means. These comparisons
have enabled us to identify a short list of high-confidence computationally-derived metabolic
drug targets. Finally, discrepancies between model predictions and annotated drug targets, and
insights gained from the mapping of other genome-scale datasets onto the metabolic network
suggest possible avenues to refine the model.
24
2.2 Methods
2.2.1 Metabolic reconstruction
A metabolic reconstruction of P. falciparum was obtained from information presented at
the Malaria Parasite Metabolic Pathways (MPMP). A detailed description can be found on its
website and works by Ginsburg [41, 42]. Briefly, this reconstruction is compiled using various
literature sources and represents metabolic physiology of intraerythrocytic P. falciparum. All
enzymes were checked for associated gene annotations in PlasmoDB, and special care was taken
to avoid inferring the existence of entire pathways observed in other unicellular eukaryotic
organisms based on the evidence of a few enzymes.
The pathways shown in MPMP were represented with KEGG reaction and compound
identifiers. Generally, the reactions indicated by MPMP maps were used in the model.
However, by systematically representing each reaction displayed in the maps with reactions from
the KEGG database, some potentially erroneous reactions and ECs in MPMP were identified.
Network completeness was investigated using Flux Balance Analysis in an iterative process.
Gaps/inconsistencies in the network, such as the inability to produce biomass components, were
reconciled using additional KEGG reactions where possible or hypothetical reactions when
necessary. Intercompartmental and extracellular transport reactions were added in order to
provide the necessary metabolite transport.
Reaction reversibility was largely left unconstrained as this information is usually
speculative and can be greatly affected by actual thermodynamic factors in vivo. Reversibility
constraints were added heuristically to only those reactions with extremely unfavorable
backward reactions (e.g. reactions that release a phosphate group, reactions that pass electrons to
quinones) and to eliminate any large-scale futile cycles in the network. Enzyme cofactor usage
25
regarding NAD/NADPH was taken from MPMP when available as it was found to match
Plasmodium specific data presented in the BRENDA database. The reconstruction was named
iMPMP427 in accordance with naming conventions, which includes the source of reaction
curation and number of genes covered by the model. The metabolic reactions included in the
reconstruction are shown in Appendix I.
2.2.2 Flux Balance Analysis (FBA)
As described in Section 1.1.3, FBA is used to generate a set of steady-state fluxes for all
the reactions in the biochemical network upon the optimization of an objective reaction under a
set of constraints. A reaction that represents the formation of biomass is usually used as the
objective reaction and reaction constraints generally include reversibility rules and transport rates
(described below). All simulations were carried out using the COBRA Toolbox and its
associated functions [23]. Basic FBA solutions were obtained using the „optimizeCbModel‟
COBRA function.
Since flux distributions calculated by FBA are not necessarily unique (multiple solutions
can often be found to optimize growth rate [43, 44]), flux variablility analysis was used to
investigate flux variability in the cases of nitrogenous species transport and glycolytic reaction
flux. This was carried out using the „fluxVariability‟ COBRA function, which calculates the
range of fluxes allowable for each reaction (in a specified reaction set) that will result in an
optimal solution (or a specified fraction of the optimal solution).
26
2.2.3 Biomass equation
The biomass equation is an approximation of the chemical composition of Plasmodium.
Its purpose is to serve as a demand for metabolites essential for growth and serves as the
objective function that is maximized in FBA simulations [24]. It was reasoned that biomass
production would serve as an appropriate objective function for malarial metabolism since
malaria parasites undergo rapid growth in the erythrocytic stage (before segmenting and lysing
the erythrocyte to enter the blood serum) [2].
The chemical composition of P. falciparum was approximated through a variety of
sources and its derivation is presented in detail in Appendix II. Essentially, the aim of this
procedure is to represent the composition of P. falciparum as a stoichiometic combination of
metabolites in the metabolic network. Where data from Plasmodium could not be found (e.g.
overall cellular macromolecule compositions, and ATP maintenance requirements), values from
a related organism, Leishmania major, were used as approximations.
2.2.4 Transport and reaction constraints
Though experimentally derived transport rates for Plasmodium are scarce, placing
reasonable constraints on the many transport reactions present in the metabolic network was
required for physiologically sound simulation results. The existence of a transport reaction was
generally taken from information presented on the MPMP maps, and further transporters were
added for currency metabolites and other metabolites needed for model functionality. The
directionalities of transport reactions was generally left unconstrained (reversible) to allow the
model to predict potentially unintuitive metabolic states and because many transport proteins can
work in opposite directions if transport gradients dictate.
27
Transport of some metabolites was limited to their known directions based on
experimental observation. Inorganic phosphate was limited to import and lactate was limited to
export because it is a known end-product of glycolysis and experiments have shown that infected
erythrocytes increase lactate levels in their surrounding plasma [3, 45]. Malate was limited to
export because this carbon source is not available for uptake in either defined culture or serum
environments. The V-type pumps (ATPase and PPiase) were limited to transferring H+ out of
the cell [3]. These reactions were given a large maximum transport constraint of 10,000
mmol/gDW/hr yielding them essentially unconstrained in their appropriate directions.
Other transport constraints were reasoned as follows: glucose was assumed to be the
limiting nutrient because of its observed essentiality as a carbon source [3], and was limited to an
uptake rate of10 mmol/gDW/h. Amino acid transport was assumed to be reversible with a range
of +/- 1 mmol /gDW/h, based on experimentally measured rates in other organisms [46]. All
other lipids/small molecules, including the ingestion of hemoglobin, were also given the range of
+/- 1 mmol /gDW/hr as used in similar studies [47] .
Nutrient transport was coupled with proton (H+) transport in the cases of the V-type
pumps and the lactate (symport) transporter [3]. The energy cost of maintaining gradients for
other ions involved in transport (e.g. Na+, Ca+, Cu+) is assumed to be captured by the ATP
maintenance term included in the biomass demand reaction. Intracompartmental transport of
metabolites was assumed to take place by way of facilitated diffusion, because this information
is only partially known and unless a large portion of ion and metabolite symport/uniport is
accounted for, adding a few cases would not make model predictions more valid. General
reversible internal reactions were constrained to +/- 1000 mmol/gDW/h, which is a number large
enough to not restrict feasible reactions but prevents unbound FBA solutions.
28
Defined culture simulations allowed import of defined culture nutrients and export of all
other nutrients thought to be exchanged in serum. Additional constraints for nutrients only found
in serum were as follows: fatty acids and phospholipids were limited to uptake because these
have been shown to be scavenged from host [48]. Guanine and xanthine were limited to export
because it was found that this eliminated inconsistencies with experimental drug targets in the
purine synthesis pathway. Urea, a known waste product of nitrogen metabolism, was limited to
export.
2.2.5 Nutrient transport and metabolic enzyme deletion
Nutrient transport reactions were defined as positive flux for import and negative flux for
export. The effect of eliminating nutrient transport was investigated by individually constraining
the upper and lower bounds of each transport reaction to zero. As an example, to investigate the
effects eliminating metabolite „x‟ import, its transport reaction would be given an upper bound of
zero, and the growth rate calculated by FBA. To investigate the effects of its export, first the
upper bound would be reset, and then the lower bound of its transport reaction set to zero and
growth calculated by FBA. This process was repeated for each nutrient in the defined culture
and serum nutrient sets. Nutrient transport was considered “essential” if the resulting optimal
growth rate calculated by FBA was equal to zero, and was considered to be required for optimal
growth if the resulting growth rate was less than 99% the growth rate without the elimination of
transport. Similarly, the effects of enzyme deletions were simulated in silico by constraining all
reactions associated with a given enzyme to zero, and then predicting the resulting growth rate
by FBA. Enzymes were deemed to be required for parasite growth if in silico deletion resulted
in a growth rate of zero. In silico predicted essential nutrients are listed in Appendix IV.
29
2.3 Results and Discussion
2.3.1 Reconstruction of Plasmodium falciparum metabolic network
In this section, a general overview and statistics of the P. falciparum metabolic model
(iMPMP427) are presented in order to understand the scope of the network. Reaction
information and the network overview figure were created by adapting information presented on
the MPMP website as described in Section 2.1.1. The various network statistics pertaining to
metabolite and reaction totals were ascertained once the reconstruction was represented
mathematically in the COBRA Toolbox using different included output and printing functions.
Lastly, the network was compared to metabolic reconstructions of other organisms in published
literature and through visualizations of the present enzymes using the iMAP mapping software.
2.3.1.1 Reconstruction statistics and network overview
Based on the pathway maps provided by the MPMP database, we constructed a metabolic model
of the intraerythrocytic stage P. falciparum [41]. In an attempt to maintain consistency with
current naming conventions [49] and acknowledge the source of reaction data, we term our
model iMPMP427. The iMPMP427 reconstruction contains 427 genes (approximately 8% of the
P. falciparum genome), 513 reactions, and 457 metabolites (Figure 3).
30
Properties of iMPMP427
Genes 427
Enzymes (ECs) 322
Reactions 513
Gene-associated 365 (71%)
Non-gene associated intracellular 63 (12%)
Non-gene associated transport 84 (16%)
Metabolites 457
Compartments 5
Figure 3. Statistics of P. falciparum metabolic network reconstruction iMPMP427. (A) Table summarizing the
number of various network components, and (B) pie charts describing the breakdown of reactions in terms of
subcellular compartments (right) and class of enzyme (left).
Reactions were assigned to five compartments, cytosol, mitochondria, apicoplast, food
vacuole, and endoplasmic reticulum (Figure 4). The majority of network reactions were
localized to the cytosol, while the organelles house reactions for more specific roles (Figure 4).
Of the 513 reactions, the metabolites involved in 335 (65%) were confined to the cytosol, while
60 reactions (12%) exchanged metabolites with the extracellular environment. The abundance of
transport reactions reflects the reliance of the parasite on nutrient exchange with its surroundings.
The apicoplast (containing 5% of total network reactions) is a specialized organelle that hosts
fatty acid synthesis and isoprenoid metabolism [50]. The mitochondrion (4%) primarily houses
the tricarboxylic acid cycle (TCA) cycle and electron transport chain (ETC). The endoplasmic
reticulum (2%) and food vacuole (1%) are responsible for the production of
(B)
(A)
31
glycosylphosphatidylinositol (GPI) anchors and digestion of hemoglobin (Hb), respectively. To
reduce network complexity and due to their limited interconnectivity with other processes, many
of the reactions in these two compartments were grouped into a single process.
Of the 513 total reactions, 365 (71%) are associated with a gene sequence that encodes for
an enzyme catalysing the reaction. This statistic is considered an indication of network
confidence as reactions with a gene association are of higher confidence than reactions that are
solely included for modeling functionality [24]. Importantly, of the 147 non-gene associated
reactions in the model, 84 (57%) represent transport. Although recent studies have greatly
improved our understanding of transport between P. falciparum and its host [51, 52], many of
the genes and mechanisms responsible have not been discovered to date [53, 54]. Hence,
consistent with previous studies [24], in addition to including transport reactions associated with
known genes, we have also included transport reactions for which a gene has yet to be associated
but for which experimental evidence supports such an activity. Considering only intracellular
non-transport reactions, 85% (365 out of 428) are associated with a known gene. This is similar
to the 90% reaction-gene association observed for a recent reconstruction of the kinetoplastid
parasite L. major [26]. Through adopting the stringent annotations associated with the MPMP
resource, iMPMP427 represents a high-confidence model of P. falciparum metabolism amenable
to in silico investigation.
32
Figure 4. Schematic outline of Plasmodium falciparum metabolic reconstruction iMPMP427. In the schematic the model has been reduced to illustrate the compartmental organization of reaction pathways,
major branch-points, and production of biomass components (red). For simplicity, arrows represent multiple
reactions, cofactor pathways are collapsed and currency metabolites have been omitted. Metabolite abbreviations are:
1,2-DAG, 1,2-diacylglycerol; 5,10-MTHF, 5,10-methenyltetrahydrofolate; AcCoA, acetyl coenzyme A; AcylCoA,
acyl coenzyme A; ADN, adenosine; ADP, adenosine diphosphate; KG, alpha-ketoglutarate; AMP, adenosine
monophosphate; ASP, L-aspartate; ATP, adenosine triphosphate; CARBM, carbamoyl phosphate; CHOLP, choline
phosphate; CoA, coenzyme A; CTP, cytidine triphosphate; CYTS, cysteine; DHAP, dihydroxyacetone phosphate;
DOLP-Man, dolichyl phosphate D-mannose; DPP, dimethylallyl diphosphate; dTTP, deoxythymidine triphosphate;
FRC6P, beta-D-Fructose 6-phosphate; G3P, glyceraldehyde 3-phosphate; GDP, guanosine diphosphate; GLC,
alpha-D-glucose; GLC6P, alpha-D-glucose 6-phosphate; GLU, L-glutamate; GLY, L-glycine; GMP, guanosine
monophosphate; GPI, glycosylphosphatidylinositol anchor; GSH, glutathione; GSSG, glutathione disulfide; GTP,
guanosine triphosphate; Hb, human erythrocellular hemoglobin; HC, homocysteine; HMBD, 1-hydroxy-2-methyl-2-
butenyl-4-diphosphate; HYP, hypoxanthine; IMP, inosine monophosphate; INO, inosine; INS, 1-phosphatidyl-D-
myo-inositol; IPP, isopentenyl diphosphate; LAC, L-lactate; METH, methionine; N-Gly, precursors for N-linked
protein glycosylation; NIC, nicotinamide; NID, nicotinate; ORT, orotate; ORT5p, Orotidine 5'-phosphate; PANT,
pantothenate; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PEP, phosphoenolpyruvate; PS,
phosphatidylserine; PYR, pyruvate; R5P, ribose 5-phosphate; RIB, riboflavin ; SAHC, S-adenosylhomocysteine;
SAM, S-adenosylmethionine; SER, L-serine; snGLY3P, sn-glycerol 3-phosphate; SPM, sphingomyelin; SUC,
succinate; THF, tetrahydrofolate; THM, thiamine; TP, toxopyrimidine; UDGNAG, UDP-N-acetylglucosamine;
UDP, uridine diphosphate; UMP, uridine monophosphate; UTP, uridine triphosphate; XMP, xanthine
monophosphate. © James Wasmuth (use of this figure is by permission of the copyright holder).
33
2.3.1.2 Comparison to other metabolic reconstructions
Figure 5. Comparisons of selected metabolic reconstructions. (A) Comparison of sizes of reconstructions across
a select group of species (Data obtained from Raman et al., 2009 and Chavali et al., 2008). (B) iMAP
representations [55] of enzyme complements comparing the P. falciparum iMPMP427 reconstruction with a
selection of reconstructions for other species: L. major iAC560 [26], M. genitalium iPS189 [19], M. tuberculosis
GSMN-TB [56] and S. cerevisiae iND750 [57]. Notable features of the P. falciparum network are highlighted in red
and include the presence of reactions involved in N-glycan (1*) and glycosphingolipid synthesis (2*) and the
absence of reactions involved in steroid (3*), amino acid (4*) and purine (5*) metabolism.
34
The distribution of classifications defined by the Enzyme Commission (EC) present in the
network is similar to that found in other unicellular eukaryotes; L. major and S. cerevisiae [26,
58] (Figure 3). Thus despite large numbers of transport reactions, the internal network of P.
falciparum contains a conserved distribution of classes of metabolic enzymes. The size of the P.
falciparum metabolic network, in terms of reactions, genes or metabolites, is comparable to
many bacteria and is the smallest for any eukaryote so far reported (Figure 5A). A reconstruction
for L. major (iAC560) has 133 more genes and over twice as many reactions and metabolites,
although the authors suggest that this is may be a result of the highly compartmentalized nature
of their model [26].
Despite its smaller size, the iMPMP427 reconstruction includes reactions not found in
other larger models, such as several associated with N-glycan biosynthesis (Figure 5B). These
provide a class of reactions necessary for capturing important aspects of carbohydrate
metabolism in P. falciparum in the absence of carbohydrate stores. Glycosylation was not
included in the Leishmania (iAC560) or yeast (iND750) reconstructions presumably because it
plays a relatively minor role in carbohydrate metabolism when more significant forms, such as
mannan and trehalose; are present. Nonetheless, we accept that the stringency applied during
construction of iMPMP427 may exclude reactions that occur in vivo. This is an acceptable
limitation as the alternative is to add hypothetical reactions on the basis of completing pathways
for which there is little direct evidence from the P. falciparum genome. Regardless, it is clear
that the metabolic network of P. falciparum has become simplified (Figure 5B), probably as a
result of its reliance on nutrient exchange with its host [53, 59]. In the next section we investigate
the nutrient requirements associated with this relatively simplified network for the production of
metabolites required for growth.
35
2.3.2 Metabolic characteristics
The metabolic model of P. falciparum was used to investigate aspects of malarial
metabolism. An important aspect of malarial metabolism that has been observed experimentally
and is apparent through the reconstruction statistics (the presence of a relatively few internal
reactions and pathways) is its reliance on nutrient transport from external sources. Although
metabolic enzymes have traditionally been targeted for antimalarials, nutrient exchange
processes represent equally valuable targets, which have only recently been considered for
inhibition [3, 45, 60-64]. In a systematic series of simulations, we applied FBA to our model of
P. falciparum metabolism to examine the impact of eliminating the ability of the parasite to
import or export each nutrient in turn (as defined by the biomass function - see Section 2.1.3 and
Appendix II). Two sets of transport processes are defined, those that are predicted to be essential
for parasite growth, and those that are required for optimal growth. Comparison of nutrient
transport predictions to experimental observations are made where possible in order to assess the
performance of the model. Furthermore, since P. falciparum relies on glycolysis as its major
energy source and as a supply of various metabolic precursors [65-68], flux through this vital
pathway and its branch points were visualized.
2.3.2.1 Simulated growth environments
We investigated two different nutrient environments, a defined culture environment and a
serum environment (Appendix III). The defined culture environment is a subset of the complete
serum nutrients. It includes only those nutrients that would be found in a culture medium
consisting of RPMI-1640 and a hypoxanthine purine source [69, 70], which has been used
previously in P. falciparum culture. A lipid source was assumed not to be essential as the
36
machinery for fatty acid synthesis is present in P. falciparum. Moreover, the requirement for
scavenging specific fatty acids of specific chain length is not completely understood [48]. It is
appreciated that this nutrient set does not fully reflect in vitro conditions, as intraerythrocytic P.
falciparum always has access to hemoglobin (Hb). However, constraining the model to this
limited set of nutrients enables us to explore their unique contributions. The serum environment
was defined to reflect in vivo conditions. It includes additional nutrients proposed to be
exchanged with the host but absent from defined culture. These include lipids, additional purine
precursors and human erythrocytic Hb as a source of amino acids. For both environments, the
carbon source was restricted to glucose. Although recent work suggests fructose and mannose
[71, 72] as possible energy sources, their entry into carbon metabolism is similar to glucose and
would not be expected make a significant difference in the global metabolic flux states. During
the intraerythrocytic stage of the parasite, most nutrients in the extracellular environment will
pass to the parasite unchanged due to the fact that the host erythrocyte possesses a highly
reduced metabolic network [73], and the parasite-induced production of specialized transport
channels [5, 52].
37
2.3.2.2 Essential nutrients
Table 1. Nutrients required for Plasmodium growth. Eliminating import/export of the indicated nutrients
resulted in a growth rate equal to zero in model simulations, indicating that they are required for P. falciparum
biomass production.
Growth with defined culture nutrients
Growth with defined culture and serum nutrients
Import Import
Carbon and Purine
alpha-D-Glucose
Hypoxanthine
Amino Acids
L-Alanine
L-Arginine
L-Cysteine
L-Histidine
L-Isoleucine
L-Leucine
L-Lysine
L-Methionine
L-Phenylalanine
L-Threonine
L-Tryptophan
L-Tyrosine
L-Valine
Micronutrients
Pantothenate
Nicotinamide
Riboflavin
Thiamin
Other
O2
Carbon
alpha-D-Glucose
Amino Acids
L-Isoleucine
Micronutrients
Pantothenate
Riboflavin
Other
O2
Export Export
Formate
Formate
Nutrients predicted to be essential for parasite growth are shown in Table 1. P. falciparum
scavenges amino acids from the host both in their native state and from the degradation of host
hemoglobin [74, 75]. Isoleucine is the only amino acid absent from adult hemoglobin and is
therefore directly imported by the parasite [70, 76]. Model simulations providing uptake of only
defined culture nutrients revealed 13 amino acids could not be synthesized de novo and must be
38
imported (Table 1). The remaining seven amino acids may be considered non-essential, albeit
with caveats. Neither glycine nor serine are essential, however they form an "essential pair"; they
can be synthesized from each other by a reversible reaction catalysed by serine
hydroxymethyltransferase (EC:2.1.2.1, PFL1720w). Hence, in the absence of both, biomass
cannot be produced. Asparagine and aspartate do not form an essential pair as a transaminase
reaction (EC:2.6.1.1, PFB0200c) can produce aspartate which may be converted to asparagine.
Similarly, glutamate can be generated from NH3 and -ketoglutarate by EC:1.4.1.4 (PF14_033)
or EC:1.4.1.2 (PF08_0132) and subsequently converted to glutamine. Finally proline can be
synthesized by pyrroline-5-carboxylate reductase (EC:1.5.1.2, MAL13P1.284), a reaction that
involves precursors generated by catabolism of arginine. The opposite conversion does not occur
because the reaction catalysed by arginase (EC:3.5.3.1, PFI0320w), is thought to be irreversible
making the import of arginine necessary. The non-essentiality of six of the above amino acids is
consistent with a previous bioinformatic analysis [77]. On the other hand, the Payne and Loomis
study also predicted that serine was essential, which is not consistent with our model and
therefore worthy of future experimental investigations.
The reliance of Plasmodium on purine salvage pathways is well known [78]. Preference
for host-derived purine precursors differ between species of Plasmodium; hypoxanthine is the
preferred purine source for P. falciparum [79]. However, in its absence, adenosine is taken up by
the parasite and rapidly converted to hypoxanthine. Therefore hypoxathine import cannot be
considered essential. In addition to a purine source, P. falciparum requires the co-factor
precursors: pantothenate, riboflavin, nicotinamide and thiamin. Of these only pantothenate and
riboflavin are predicted by our model to be essential for growth in the serum enviroment.
Pantothenate is required for CoA biosynthesis and is the only co-factor precursor which has been
39
confirmed to be essential in vivo [80]. While deficiency in riboflavin has been observed to be
protective against malaria [81]. In the defined culture environment, nicotinamide is required for
the synthesis of NAD and NADP through conversion to nicotinate via nicotinamidase
(EC:3.5.1.19, PFC0910w). However, in serum, uptake of nicotinate provides an alternative entry
point. Similarly, thiamine uptake is also required from the defined culture environment, while in
the serum-based simulations, thiamine can be synthesised through the uptake and conversion of
toxopyrimidine [82]. Formate is produced in the biosynthesis of folate in the reaction catalysed
by GTP cyclohydrolase (EC:3.5.4.16, PFL1155w). Though KEGG lists 42 metabolic enzymes
that involve folate as a reactant or product, none are predicted in the P. falciparum genome.
Interestingly, the MPMP database annotates three putative formate transporters, PFC0725c,
PFB0465c and PFI1295c. Given that the model predicts formate excretion is essential, these
genes represent potentially interesting, yet unexplored drug targets. Likewise, as noted above,
riboflavin transport offers another potential target, however the gene(s) responsible has yet to be
identified.
40
2.3.2.3 Optimal growth nutrients
Table 2. Serum nutrients required for optimum P. falciparum growth. Eliminating import/export of the
indicated nutrients resulted in growth rate less than 99% of the optimal value. Percentage abundance of amino acids
present in human erythrocellular hemoglobin and the P. falciparum proteome is listed for all amino acids that are
required to be transported for optimal growth.
Nutrient exchange for optimal growth in serum environment
Import Export
Amino Acids (% abundance Hb, P. fal)
L-Glutamate (7, 7)
L-Glutamine (1, 3)
L-Methionine (2, 2)
Other
Hb
Amino Acids (% abundance Hb, P. fal)
L-Alanine (8, 2)
L-Asparagine (4, 14)
L-Cysteine (1, 2)
Glycine (10, 3)
L-Histidine (5, 2)
L-Leucine (12, 8)
L-Lysine (8, 12)
L-Phenylalanine (6, 4)
L-Threonine (6, 4)
L-Tryptophan (2, < 1)
L-Valine (10, 4)
Other
Homocysteine
Putrescine
HCO3-
Figure 6. Impact of nutrient transport constraints on parasite growth. The effects of transport inhibition for
nutrients required for optimal growth. Vertical bars represent normalized growth rate when the indicated transport is
eliminated.
41
In addition to identifying nutrients deemed to be essential, FBA also identified the set of
nutrients that when removed, impact parasite growth potential (Table 2 and Figure 6). From this
set, HCO3- transport appears to have the most significant effect and arises from the release of
CO2 during glucose catabolism. The MPMP database describes the transport of HCO3- through a
putative inorganic ion exchange transporter (PF14_0679) on the plasma membrane. Our
simulations show that the majority of production of CO2 comes from two reactions: 6-
phosphogluconate dehydrogenase (EC:1.1.1.44, PF14_0520) in the pentose phosphate pathway,
and ornithine decarboxylase (EC:4.1.1.17, PF10_0322) in methionine metabolism. Thus, while
carbonic anhydrase has been recently proposed as a promising antimalarial drug target [83, 84],
these simulations suggest that the related HCO3- transporter would be a similarly attractive drug
target.
P. falciparum is missing the majority of the amino acid synthesis pathways, relying instead
on ingestion of host hemoglobin. Since the relative abundance of hemoglobin-derived amino
acids differs from the requirement of P. falciparum protein translation, certain amino acids need
to be exported from the parasite [85]. To maximize growth potential, the model predicts the
export of ten amino acids (Figure 7). Of these, eight are more abundant in hemoglobin than the
P. falciparum proteome (Table 2). Two others, cysteine and lysine, are more abundant in P.
falciparum proteins. However, production of these metabolites from other sources suggests that
degradation of hemoglobin results in an excess of these amino acids than that is required for
optimal growth. Conversely, for optimal growth, the parasite needs to import glutamate,
glutamine and methionine. Of these only glutamine has a lower relative abundance in
hemoglobin than the P. falciparum proteome. On the other hand, all three amino acids can act as
precursors in other energetically demanding pathways. For example, methionine is involved in
42
the production of the important methyl donor, S-adenosylmethioine (SAM). The predicted export
of alanine, histidine, phenylalanine, tryptophan and valine, along with the import of glutamine
and methionine is consistent with experimental observations of parasitized erythrocytes during a
48 hour developmental cycle [59]. Interestingly this study also found that glutamate is exported
in small amounts, suggesting that the rates of transport of amino acids in vivo are not uniform as
assumed in our model. Further investigations of these processes would therefore benefit from the
availability of experimentally determined transport constraints.
43
2.3.2.4 Amino acid transport variability
Figure 7. Flux variability analysis for transport fluxes associated with amino acids and nitrogen species. Two
growth conditions were investigated: Defined culture environment, serum environment at optimal growth. The bars
indicate the minimum and maximum transport fluxes possible under the two conditions. Negative fluxes represent
export; positive fluxes indicate import. Metabolites whose transport minimum and maximum are the same sign, can
only be exported (negative) or imported (positive).
We performed a flux variability analysis, which identifies ranges of flux values that
reactions can take for a given objective value (see Section 2.1.2), in order to identify the
potential range of fluxes that amino acid transport can adopt to optimize growth. Flux
distributions calculated by FBA are rarely unique as multiple solutions can often optimize
44
growth rate [43, 44]. With the defined culture environment (i.e. absence of hemoglobin), the
iMPMP427 model requires import of most amino acids, typically in small amounts (Figure 7).
Glutamate and glutamine are imported at the maximum rate again highlighting their
importance to energy production in addition to their requirements for protein synthesis. Aspartate
flux is highly variable as it can be exported or imported at its maximum rate without affecting
the cellular objective function. Glycine is the only amino acid that is constitutively exported,
regardless of the biomass requirement. This suggests that the import of serine and subsequent
conversion to glycine through serine hydroxymethyltransferase, is more favourable than the
uptake of serum glycine. In the presence of hemoglobin, there is a marked shift to amino acid
export (Figure 7) that generally reflects their relative abundance in hemoglobin. Interestingly,
proline, aspartate and arginine are the only three amino acids which can be either imported or
exported subject to the fluxes adopted by other transport reactions. The potential for these three
metabolites to be imported, as noted earlier for glutamate, glutamine and methionine, reflects
their use in other pathways beyond protein synthesis. A notable exception is isoleucine, the only
amino acid not found in hemoglobin, which demonstrates a constant pattern of import across all
growth environments and conditions.
These results have clinical implications. For example, tracking certain amino acid levels
in serum samples may indicate whether the parasite is growing at optimal levels. For example,
we would predict that if glutamate, glutamine, or methionine are exported, the parasite is
growing at suboptimal levels. Several studies have investigated the relationship between serum
amino acid levels and disease by comparing serum of healthy patients and those infected with
malaria [86]. Serum concentrations of several amino acids were found to vary, but most notably
decreases in arginine and increases in phenylalanine and histidine were observed [87-89]. This is
45
consistent with model predictions where phenylalanine and histidine are exported from P.
falciparum at optimal growth and only imported at very low levels at reduced growth.
The extensive export of amino acids, is a unique facet of P. falciparum metabolism,
arising as a consequence of hemoglobin degradation. As nitrate and/or nitrate are typically
imported as a source of nitrogen for amino acid synthesis, we examined the role of these
pathways in the iMPMP427 reconstruction. The MPMP database annotates transport of nitrate
and nitrite to be facilitated by two putative transporters (PFC0725c and PF14_0321) along with
the presence of nitrate reductase (EC:1.7.1.3, PF13_0353) and nitrite reductase (EC:1.7.1.1,
PF13_0353). Flux variability simulations (Figure 7) suggest these metabolites can be imported
or exported without consequence to growth. Thus, the putative nitrate and nitrite transporters in
P. falciparum may be operating to export excess nitrogen from the parasite. Together these flux
variability analyses illustrate the impact of pathway redundancy on amino acid and nitrogen
transport, particularly under suboptimal growth.
46
2.3.3 Glycolytic flux
Figure 8. Glycolytic flux in P. falciparum. (A) Reaction flux through glycolysis and branching pathways. Circular
nodes represent metabolites and rectangular nodes represent reactions. Node color is representative of flux carried
by the reaction as indicated by the color scale. Asterisks indicate reactions carrying nine of the ten greatest fluxes
within the network. Red outlines indicate reactions with flux variability at optimal growth. (B) Sensitivity of parasite
growth to flux through the glycolytic ATP-generating reactions catalysed by EC:2.7.2.3 and EC:2.7.1.40
respectively. (C) Maximum glycerol export possible as a function of percentage optimal growth. Metabolite
abbreviations are: 1,3-bisG, 1,3-bisphospho-D-glycerate; 2PG, 2-phosphoglycerate; 3PG, 3-phosphoglycerate;
DHAP, dihydroxyacetone phosphate; FRC-1,6P, beta-D-fructose 1,6-bisphosphate; FRC6P, beta-D-fructose 6-
phosphate; G3P, glyceraldehyde 3-phosphate; GLA, glyceraldehyde; GLC, alpha-D-glucose; GLC6P, alpha-D-
glucose 6-phosphate; GLY, glycerol; LAC, L-lactate; PEP, phosphoenolpyruvate; PYR, pyruvate; snGLY3P, sn-
glycerol 3-phosphate; SOR, sorbitol.
47
The importance of glucose to P. falciparum is demonstrated by the 100-fold increase in
glucose utilization in infected erythrocytes relative to their uninfected counterparts [65], resulting
in the release of large amounts of lactate [66]. In addition to providing energy for growth and
maintaining cellular homeostasis - e.g. removal of extracellular glucose results in an immediate
drop in pH [67] - glycolysis plays an important role in providing precursors for many other
pathways such as nucleotide synthesis via the pentose phosphate pathway [68]. Applying FBA to
iMPMP427 reveals that of the top 10 flux carrying reactions, 9 are in the glycolysis pathway
(Figure 8A). 88% of the carbon in glucose is converted to lsactate under optimal growth
conditions: for 10 mmol uptake of glucose, 17.69 mmol of lactate is exported, consistent with
previous experimental studies [90]. Flux variability analysis shows that five reactions in this
pathway can adopt alternate fluxes at optimal growth (Figure 8A). Reactions catalysed by
EC:1.1.5.3 and EC:1.1.1.8 can comprise an electron shuttle for the electron transport chain
(ETC), however other donors are present. For the remaining three reactions involving
EC:2.7.1.40, EC:1.1.1.27 and transport of lactate out of the cytosol, oxaloacetate provides an
alternate means to generate phosphoenolpyruvate (PEP). Hence, these reactions may be subject
to increased flux without consequence for growth potential as demonstrated by the plateau in
growth rate observed in Figure 8B. These simulations indicate that the network displays a
general pattern of importing large amounts of glucose, metabolizing it to lactate through
glycolysis, and siphoning off metabolites to branching pathways as required.
The lactate branch of glycolysis contains the ATP-generating or so called “payback”
reactions, via phosphoglycerate kinase (EC:2.7.2.3, PFI1105w) and pyruvate kinase (2.7.1.40,
PFF1300w), respectively. P. falciparum is thought to forego oxidative phosphorylation in the
mitochondria and rely almost exclusively on glycolysis for ATP production [91, 92]. Here we
48
examined the sensitivity of iMPMP427 to changes in flux through the two ATP-generating
reactions catalyzed by EC:2.7.2.3 and EC:2.7.1.40, respectively. As expected, decreasing the
flux through these reactions led to a predicted decrease in optimal growth (Figure 8B). Though
lactate production has long believed to be the only major product of glycolysis, recently glycerol
production has been observed in parasites grown in sub optimal conditions [93]. It is not entirely
understood how glycerol is produced by P. falciparum as the reaction catalyzed by glycerol
kinase (EC:2.7.1.30, PF13_0269) is considered irreversible. Glycerol 3-phosphatase
(EC:3.1.3.21), required for the production of glycerol from glycerol 3-phosphate under most
physiological thermodynamic conditions, is not annotated in the P. falciparum genome.
However, assuming that glycerol can be produced through reversible action of glycerol kinase,
our model predicts that glycerol can only be produced at suboptimal growth rates (Figure 8C).
2.3.4 Metabolic enzyme inhibitions
A major objective of this study is to exploit the P. falciparum model for the purposes of
identifying new candidates for therapeutic intervention. Previous studies based on both
experimental and theoretical investigations have already identified a number of useful targets.
This includes annotations of enzymes that have shown evidence of being potential drug targets
by the MPMP database and by Fatumo et al [94]. Furthermore, a previous computational study
by Fatumo et al. has proposed potential hypothetical metabolic drug targets. Thus, it is desired
to compare these predictions with essential enzymes predicted by iMPMP427. In silico
metabolic enzyme deletions were simulated in order to investigate the effects on the global
network in response to inhibition by therapeutic agents. Predicted essential enzymes were
compared with enzymes documented as drug targets in vivo from two sources (all datasets are
49
provided in Appendix IV). This comparison was used to identify areas in P. falciparum that
require further research, and identify potential novel metabolic enzyme drug targets.
Figure 9. Overlap of computationally predicted metabolic drug targets and those that have been annotated
as drug targets based on experimental evidence. Computational dataset I consists of enzymes predicted by
iMPMP427 to catalyze reactions that are required for biomass production. MPMP drug target annotations were
derived from information presented on pathway maps at the MPMP website. Computational dataset II and Fatumo
annotations were derived from previously published lists of predicted drug targets and literature curated drug targets,
respectively [94]. Tables indicate putative drug targets identified through dataset overlap. Enzyme data and
annotations obtained from Stacy Hung.
To predict putative metabolic drug targets we performed single and double enzyme
deletions in silico to identify those required for parasitic growth. Of the single enzyme deletions,
FBA of the iMPMP472 model predicts 151 out of 322 (47%) to be required for production of
biomass components essential for growth (Appendix IV). This relatively high proportion of
enzymes further illustrates the simplified nature of P. falciparum metabolism, which contains
few alternate routes for the production of growth metabolites. Additionally, we identified 44
non-trivial essential enzyme pairs involving 31 unique enzymes. Of these 16 were associated
50
with three pathways: pyruvate metabolism (5 enzymes); thiamine metabolism (6 enzymes) and
glutamate metabolism (5 enzymes), highlighting redundant mechanisms in these processes
(Appendix IV). We compared these predictions of single enzyme deletions with two sets of
annotations of metabolic enzyme drug targets (Figure 9).
There are instances of metabolic enzymes that have been annotated to be drug targets by
various sources, but are not predicted to be essential enzymes by model simulations. These have
been presented and classified in Appendix V. These discrepancies are attributable to several
reasons. Fundamentally, enzymes deemed to be essential by iMPMP427 are those that catalyze
reactions that lead to the production of a biomass component without an alternate route. Thus, if
eliminated, these reactions lead to zero predicted growth. However, enzymes observed to be
potentially essential in vivo could function by producing metabolites for other physiological roles
or be involved in a complex regulatory cycle not captured by the flux-balance model.
Furthermore, the annotated metabolic enzyme drug target may lie on a blocked reaction or have
an alternate pathway for biomass component production in the model. These represent the gap
of knowledge between metabolic enzyme genes and in vivo behavior and possibly indicate
further areas of model refinement and study. Additionally, some chemical therapeutics may
target numerous similar metabolic enzymes simultaneously in vivo. For example, 12 of the 15
enzymes annotated by MPMP to be potential metabolic drug targets that are not essential
enzymes in silico, are involved in the breakdown of „dipeptides‟ into single amino acids (in the
hemoglobin digestion pathway). These enzymes are targeted in combination by the Bestatin
drugs and display therapeutic effects in vivo. However, due to the generic nature of a
„dipeptides‟ metabolite, this process was represented by a single grouped reaction with multiple
and EC in silico, which individually are not essential in the enzyme deletion simulations. In
51
short, though discrepancies between annotated metabolic drug targets and predicted essential
enzymes may provide some information regarding model accuracy and refinement, many are the
result of caveats in the FBA methodology which must be accepted.
Lastly, it is important to note that enzymes annotated as potential drug targets arise from
experimental reports from different sources, each culturing P. falciparum in different
environmental conditions. P. falciparum culturing techniques are not standardized and
researchers commonly use non-defined serum-based supplements to promote parasite replication.
However, different nutrient availability almost certainly has an effect on enzyme and reaction
essentiality. The fact that Fatumo and MPMP drug target annotations only correspond at 16%
(11/72) is an indication that overall there is little agreement on which enzymes constitute as a
metabolic drug target in P. falciparum. It would be useful to perform a single genome-scale
knockdown of P. falciparum genes (data that is currently not available), preferably using
standardized media without serum supplements, which could then be compared to model
predictions given access to the same nutrients. This would be useful in quantification of the
predictive capacity of the model, as it would enable the comparison of false negatives and false
positives with the genome scale model. Other genome scale models have been found to
correspond anywhere from 65% - 90% of gene knockout studies [26, 47].
In this situation, the most informative sections of the Venn diagram presented in Figure 9
are the single enzyme deletions that are annotated by both sources to be metabolic drug targets
yet not predicted to be essential enzymes in silico, and the essential enzymes that correspond
with metabolic drug targets predicted previously by Fatumo et al.
Of the enzymes annotated to inhibit P. falciparum growth experimentally, there are four
that are annotated by both sources but are not predicted to be essential in silico. These
52
discrepancies may point to the existence of an unidentified biomass component or the existence
of a neglected alternative pathway [24] and serve to direct future biochemical investigations and
model refinement. For example, ornithine decarboxylase (EC:4.1.1.17), purine nucleoside
phosphorylase (EC:2.4.2.1) and adenosime deaminase (EC:3.5.4.4) are involved in the
production of S-methyl-5-thio-D-ribose 1-phosphate, a metabolite predicted by the iMPMP427
reconstruction to be neither exported or consumed by another reaction and hence not essential.
On the other hand, these enzymes have been shown experimentally to be important for parasite
growth [41, 95], thus it is reasonable to hypothesize that this pathway serves a thus far
uncharacterized important physiological function. The fourth discrepancy from this region
involves sphingomyelin phosphodiesterase (EC:3.1.4.12). Further investigation reveals that in
isolation, it does not represent an experimental drug target. It is targeted by Scyphostatin, which
simultaneously inhibits phosopholipase C (EC:3.4.1.3) and interferes with choline production via
the breakdown of phosphatidylcholine [96]. In silico, uptake of choline can circumvent this
inhibition, however in vivo, choline is relatively scarce, potentially contributing to the
therapeutic effects of Scyphostatin. This therefore represents an additional hypothesis for further
experimental investigation.
Furthermore, it is valuable to compare essential enzymes predicted by iMPMP427 with
drug targets generated by two previous studies employing mathematical (non constraint-based)
representations of metabolism [94, 95]. These previous approaches have attempted to predict
essential reactions for P. falciparum topologically, based on different assumptions of metabolic
network connectivity. However, it is noted that both previous studies were undertaken using
PlasmoCyc metabolic network annotations, which are thought to lack the accuracy of the MPMP
resource as discussed above [42, 97], further emphasizing the need to compare predictions of
53
reaction essentiality. We obtain a list of 22 potential novel drug targets for P. falciparum from
Fatumo et al. [94] that fulfill the definition of reaction essentiality from the two previously
connectivity-based methods, lack significant homology to human proteins (making them
theoretically more amenable to selective inhibition using small molecule therapeutics), and have
not been previously annotated as a potential metabolic drug target. Of these 7 are identified as
essential enzymes by iMPMP427 (Figure 9). Generally speaking, by taking the intersection of
drug targets identified by different computational approaches, weaknesses in any one
methodology can be compensated for. For example, since the previous topology-based methods
were carried out on a metabolic network with potential faulty annotations and identify enzymes
without consideration of physiological role in P. falciparum, they are further supported by
essential enzymes in iMPMP427 as they stop the production of known biomass components.
Thus, these 7 enzymes represent the most high-confidence computationally derived metabolic
drug targets for P. falciparum to date.
2.3.5 Incorporation of other genome-scale data sets
The metabolic reconstruction can be used as a platform onto which other genome-scale
and/or high throughput datasets may be incorporated, in order to gain unique insights associated
with these datasets. For example, here we have mapped available genome-scale biological data
relating to connectivity, enzyme conservation, and stage-specific expression for P. falciparum
onto its metabolic network in order to potentially gain any further insights into malarial
metabolism and/or model refinement.
54
55
Figure 10. Mappings of genome-scale data onto bipartite visualization of iMPMP427 Small nodes represent
metabolites, large nodes represent reactions and clusters represent pathways. Currency metabolites with a
connectivity greater than 10 have been omitted. (A) Reaction activity: reactions are coloured according to their
status as 'active' or 'blocked' (i.e. those which produce metabolites which are not utilized elsewhere). (B) Reaction
conservation: reactions are coloured according to the number of eukaryotic genomes in which the associated genes
possess orthologs. Orthologs were determined through an Inparanoid-based pipeline [98]. (C) Reaction expression:
reactions are coloured according to peak stage expression in the erythrocytic malarial lifecyle [99]. Visualisation
was performed using Cytoscape [100]. Enzyme conservation calculated by Tuan On based on procedures described
in [98].
The incorporation of other genome-scale datasets gives some insights onto P. falciparum
metabolic model refinement. For example, there are 56 predicted 'blocked' reactions in the
metabolic model (Figure 10). These are reactions that lie in a pathway that is not connected to
the rest of the network, and thus cannot carry flux in FBA simulations. Of these, some may
represent annotation artifacts while others may represent operative pathways with additional
reactions that have yet to be identified. Integration of conservation and expression data allows
the identification of enzyme genes that are either highly divergent or expressed at different time
points to other members of the pathway. Such enzymes may represent pathway artifacts and are
either non-functional or catalyze reactions in alternative pathways. Conversely genes encoding
enzymes that are conserved and/or display similar expression profiles to other pathway members
are presumed to be associated with operative reactions producing metabolites important for
56
physiological function and/or production of biomass. As an example, PF11_0427 encodes
ceramide glucosyltransferase (EC:2.4.1.80) responsible for producing glucosylceramide.
Currently, the model suggests that this metabolite is not required by the parasite and may
therefore represent an annotation artifact. However, given its conservation and similar expression
to other members of the pathway, it is likely that glucosylceramide is an important constituent of
the parasites lipid complement. Conversely, MAL13P1.319 is a hypothetical protein predicted to
encode anthranilate synthase (EC:4.1.3.27). The product of this enzyme, anthranilate, again
appears not to be utilized by the parasite. However, its low level of conservation and different
expression profile suggests that this gene may not encode this enzyme or the enzyme may
operate in a different pathway.
These datasets may be further exploited to provide insights into the evolution and operation
of these pathways. For example, from Figure 10B, we note that the shikimate, isoprenoid and
folate pathways are relatively less well conserved than others and may therefore represent
specialized processes adapted by the parasite that may be exploited for therapeutic intervention.
From Figure 10C, we observe an interesting temporal pattern of expression. First, at the
beginning of the parasites life cycle, we observe peak expression of amino acid and co-factor
metabolism, setting the stage for protein synthesis. This is then followed by expression of
enzymes involved in purine and pyrimidine metabolism during the trophozoite stage, prior to
DNA replication. Finally during the late trophozoite/schizont stage we observe peak expression
of enzymes involved in lipid metabolism associated with cell division. These datasets may be
further exploited by placing additional constraints on model reactions as a function of gene
expression [26, 101].
57
CHAPTER 3 Conclusions and Future Work
3.1 Conclusions
This study represents a post-genomic approach towards the investigation of
microorgamism metabolism, as applied to the important human pathogen P. falciparum. The
metabolic reconstruction (named iMPMP427) is made possible through genomic annotation
collected by MPMP curators, and the capabilities of this metabolic network were analyzed
computationally using FBA. The iMPMP427 model, the first for an apicomplexan species, will
join the growing list of metabolic reconstructions available to the scientific community. The
metabolic reconstruction was shown to be a simplified network, which is representative of P.
falciparum’s evolution as an intra-erythrocytic pathogen, and correlated well with experimental
observations of nutrient transport. Furthermore, the essential enzymes predicted by this model
that overlap with target enzymes previously identified through other computational means, add
further evidence to these as potential drug targets. Lastly, the occurrence of enzymes that are not
predicted to be essential in silico but have been observed to display essentiality in vivo, highlight
regions of the metabolic network that would likely benefit from further experimental research.
3.2 Future work
Metabolic reconstructions are never truly complete as they are constantly subject to
updates based on experimental information of metabolic enzymes and processes. For example,
the currently available E. coli metabolic reconstructions have been developed over a period of
approximately thirteen years and many iterations [102] [49]. Though this is an extreme case
58
since many of the current established reconstruction techniques were pioneered during this
period and using this model species, other species (such as M. tuberculosis, and H. pylori) have
also experienced multiple renovations in metabolic model content [17].
Many of the possible refinements for the current iMPMP427 model have been mentioned
in the text. For example, much of the organism-specific data that is commonly used in metabolic
reconstructions is not available for P. falciparum and was approximated in this study. This
includes data such as ATP required for cell maintenance, and overall cellular composition (i.e.
cellular percentage of carbohydrates, protein, nucleic acid, and lipid). As these data become
available, they can be incorporated into the model. Metabolic tracing techniques may be applied
to the proposed dead ends in the model, especially those proposed to be performing important
functions in P. falciparum, in order to gain insight into further steps of these pathways or perhaps
physiological roles that these metabolites take. Furthermore, genome-scale gene knockdown
data for Plasmodium would go a long way towards characterizing the overall accuracy of the
model. It would be especially useful if the genome knockdowns could be carried out on
organisms growing in standardized culture (without serum supplements), as it would enable an
identical nutrient environment to be simulated in silico (without the uncertainty of potential
nutrients available in serum that are not represented in the model). The relative paucity of
Plasmodium data likely stems from complications in its culturing. Since it is an intra-
erythrocytic eukaryotic parasite, many of the approaches used to manipulate and culture free
living single-celled prokaryotes (such as E. coli) may not apply to Plasmodium. Thus,
generation of additional data that could be applied to the metabolic reconstruction may actually
hinge on further developments in the biochemical assays that are required.
59
Furthermore, experimental measurements relating to specific transport rates of nutrients
would increase the accuracy of internal network flux predictions. For example, the current
import of glucose is limited to 10 mmol/gDW/h as a reasonable approximation of expected
uptake based on observed values in other species. FBA simulations predict that glucose is
imported at this imposed maximum, since P. falciparum is almost completely dependent on the
energy producing reactions of glycolysis for ATP production. It is important to note then, that
the predicted flux through glycolytic reactions in this situation is purely theoretical (hence all
related discussion was limited to observation of ratios between glucose uptake and lactate
production, and relative fluxes through glycolytic and branching reactions). However, if a
physiologically observed glucose uptake rate was experimentally determined, the corresponding
transport reaction in the model could be constrained to that value, and thus the predicted
glycolytic flux would be more representative of P. falciparum physiology. Similarly,
experimental determination of hemoglobin digestion and fatty acid uptake, would add more
physiological significance to predicted amino acid efflux rates and reactions in the various lipid
pathways, respectively. As a preliminary investigation into these issues, sensitivity analysis of
various uptake rates could be investigated. The current imposed transport limits could be altered
and various changes in predicted network flux and growth rates could be investigated. For
example, flux variability analysis of the amino acid transport gave indication of their sensitivities
to transport constraints and directionality.
The metabolic network reconstruction may also be potentially improved by applying
thermodynamic algorithms to determine reversibility of the network reactions. Currently,
following metabolic reconstruction conventions, reactions involving extremely unfavorable
Gibbs free energy changes in the backwards direction were constrained as irreversible [24]. This
60
includes reactions involving the release of a diphosphate group, transfer of phosphate from ATP
to an acceptor molecule, and redox processes involving quinines. Additional reactions with
known directionality in living systems or Plasmodium as documented in biochemical textbooks,
were constrained accordingly, while reactions with no other information were generally left
reversible. However the „Group Contribution Method‟ thermodynamic algorithm [103], and its
online application tool (webGCM) may give insight into the reversibility of these reactions. This
tool can estimate the Gibbs energy for most compounds based on their molecular structure and
arrangement of functional groups, thus the Gibbs energy change for reactions involving these
compounds can be calculated. However, this tool is not applicable for all reactions since the
Gibbs energy estimates are not available for all compounds. Furthermore, the approximated
values for Gibbs energy generated by this tool have high uncertainties in their estimates [103],
which must also be taken into account before applying reversibility constraints on model
reactions. Lastly, since metabolic experimental observation (such as nutrient or enzyme
essentiality) must always take precedence, adding reversibility constraints that would reduce the
accuracy of the model must be avoided. However, in these cases, identifying model reactions
that are required to take directionality contrary to webGCM predictions could still be significant.
(Incorporation of computationally predicted reaction reversibility is currently ongoing work).
Lastly, the intraerythocytic model of P. falciparum metabolism developed here may be
integrated with erythrocytic models of metabolism. One approach would be to integrate
previously developed kinetic models of erythrocytic metabolism [104] with the malarial flux
model [105]. In this approach, FBA of the system would be performed repeatedly at time points
over a simulation period, and system constraints (such as the rate of glucose uptake) could be
updated based on consumption at the previous time step. Furthermore, malarial metabolomic
61
data [59] may be used to create additional constraints in the form of metabolomic pooling fluxes
to represent the accumulation and consumption of metabolites not at steady-state. By imposing
these additional constraints at different points in the simulated time period, FBA predictions may
give insight into the function of metabolic pathways (such as the TCA cycle) at different stages
of the Plasmodium lifecycle.
62
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APPENDICIES
69
Appendix I: Metabolic reconstruction network reactions
RID Reactants Products EC Gene
a_EX_1-3DPGA
3-Phospho-D-glyceroyl-
P[a] <=> 3-Phospho-D-glyceroyl-P[c] NA
a_EX_1-Acyl-sn-
glycerol_3-P 1-Acyl-sn-glycerol-3-P[a] <=> 1-Acyl-sn-glycerol-3-P[c] NA
a_EX_ADP ADP[a] <=> ADP[c] NA
a_EX_AMP AMP[a] <=> AMP[c] NA
a_EX_ATP ATP[a] <=> ATP[c] NA
a_EX_CMP CMP[a] <=> CMP[c] NA
a_EX_CoA CoA[a] <=> CoA[c] NA
a_EX_CTP CTP[a] <=> CTP[c] NA
a_EX_DHAP Glycerone-P[a] <=> Glycerone-P[c] NA
a_EX_DMAP Dimethylallyl-diP[a] <=> Dimethylallyl-diP[c] NA
a_EX_H H[a] <=> H[c] NA
a_EX_H2O H2O[a] <=> H2O[c] NA
a_EX_HCO3- HCO3-[a] <=> HCO3-[c] NA
a_EX_IPP Isopentenyl-diP[a] <=> Isopentenyl-diP[c] NA
a_EX_Palmitate Hexadecanoic-acid[a] <=> Hexadecanoic-acid[c] NA
a_EX_Palmitoyl-CoA Palmitoyl-CoA[a] <=> Palmitoyl-CoA[c] NA
a_EX_PEP Phosphoenolpyruvate[a] <=> Phosphoenolpyruvate[c] NA
a_EX_Phosphatidate Phosphatidate[a] <=> Phosphatidate[c] NA
a_EX_Pi Pi[a] <=> Pi[c] NA
a_EX_X5P 1-dD-Xyulose5P[a] <=> 1-dD-Xyulose5P[c] NA
a_R00004 H2O[a] + PPi[a] <=> 2.000000 Pi[a] 3.6.1.1
a_R00132 HCO3-[a] + H[a] <=> H2O[a] + CO2[a] 4.2.1.1
a_R00200
ADP[a] +
Phosphoenolpyruvate[a] <=> ATP[a] + pyruvate[a] 2.7.1.40 PF10_0363,PFF1300w
a_R00209
NAD[a] + CoA[a] +
pyruvate[a] <=>
NADH[a] + CO2[a] +
Acetyl-CoA[a] + H[a]
1.2.4.1 AND 1.8.1.4
AND 2.3.1.12
PF11_0256,PF14_0441
AND
PF08_0066,PFL1550w
AND
PFC0170c,PF10_0407
a_R00842
NAD[a] + sn-Glycerol-3-
P[a] <=>
NADH[a] + Glycerone-P[a]
+ H[a] 1.1.1.8
PFC0275w,PFL0780w,P
F11_0157
a_R00851
NADH[a] + sn-Glycerol-3-
P[a] + Stearate[a] <=>
NAD[a] + 1-Acyl-sn-
glycerol-3-P[a] 2.3.1.15 PF13_0100,PFL0620c
70
a_R01015
2R--2-Hydroxy-3--
phosphonooxy--propanal[a] <=> Glycerone-P[a] 5.3.1.1 PF14_0378,PFC0831w
a_R01061
NAD[a] + Pi[a] + 2R--2-
Hydroxy-3--phosphonooxy-
-propanal[a] <=>
NADH[a] + 3-Phospho-D-
glyceroyl-P[a] + H[a] 1.2.1.59
a_R01195
2.000000 NAD[a] +
2.000000 red-ferredoxin[a] <=>
2.000000 NADH[a] +
2.000000 ox-ferredoxin[a] +
2.000000 H[a] 1.18.1.2
PF07_0085,PFF1115w,P
F11_0407
a_R01280
ATP[a] + CoA[a] +
Hexadecanoic-acid[a] <=>
PPi[a] + AMP[a] +
Palmitoyl-CoA[a] 6.2.1.3
PF14_0761,PFA0455c,P
FI0980w,PF14_0751,PF
B0685c,PFL2570w,PFF0
945c,PF07_0129,PFB069
5c,PFE1250w,PFL0035c,
PFL1880w,MAL13P1.48
5,PFC0050c,PFD0085c,P
FF0290w
a_R02241
1-Acyl-sn-glycerol-3-P[a] +
Stearate[a] <=> Phosphatidate[a] 2.3.1.51 PF14_0421
a_R04385
ATP[a] + HCO3-[a] +
Holo--carboxylase-[a] <=>
ADP[a] + Pi[a] +
Carboxybiotin-carboxyl-
carrier-protein[a] 6.3.4.14 PF14_0664,PF14_0573
a_R04386
Acetyl-CoA[a] +
Carboxybiotin-carboxyl-
carrier-protein[a] <=>
Malonyl-CoA[a] + Holo--
carboxylase-[a] 6.4.1.2 PF14_0664,PF10_0409
a_R05633
CTP[a] + 2C-methyl-D-
Erythritol4P[a] <=>
PPi[a] + 4Cytid5diP-2C-
methyl-D-Erythritol4P[a] 2.7.7.60 PFA0340w
a_R05634
ATP[a] + 4Cytid5diP-2C-
methyl-D-Erythritol4P[a] <=>
ADP[a] + 2P-4Cytid5diP-
2C-methyl-D-Erythritol4P[a] 2.7.1.148 PFE0150c
a_R05636
pyruvate[a] + 2R--2-
Hydroxy-3--phosphonooxy-
-propanal[a] <=>
CO2[a] + 1-dD-
Xyulose5P[a] 2.2.1.7 MAL13P1.186
a_R05637
2P-4Cytid5diP-2C-methyl-
D-Erythritol4P[a] <=>
CMP[a] + 2C-methyl-D-
Erythritol-2,4cycloP[a] 4.6.1.12 PFB0420w
a_R05688
NAD[a] + 2C-methyl-D-
Erythritol4P[a] <=>
NADH[a] + 1-dD-
Xyulose5P[a] + H[a] 1.1.1.267 PF14_0641
a_R05884
2.000000 red-ferredoxin[a]
+ 1hydroxy-2methyl-
2butenyl-4diP[a] <=>
Isopentenyl-diP[a] +
2.000000 ox-ferredoxin[a] 1.17.1.2 PFA0225w
71
a_R07219
2.000000 red-ferredoxin[a]
+ 1hydroxy-2methyl-
2butenyl-4diP[a] <=>
Dimethylallyl-diP[a] +
2.000000 ox-ferredoxin[a] 1.17.1.2 PFA0225w
a_R08689
2C-methyl-D-Erythritol-
2,4cycloP[a] + 2.000000
red-ferredoxin[a] <=>
H2O[a] + 1hydroxy-
2methyl-2butenyl-4diP[a] +
2.000000 ox-ferredoxin[a] 1.17.4.3 PF10_0221
a_Rfas16
14.000000 NADH[a] +
Acetyl-CoA[a] + 7.000000
Malonyl-CoA[a] +
14.000000 H[a] <=>
14.000000 NAD[a] +
8.000000 CoA[a] +
7.000000 CO2[a] +
Hexadecanoic-acid[a] 2.3.1.85
PFB0505c,PFF1275c,PFI
1125c
a_Rfas18
16.000000 NADH[a] +
Acetyl-CoA[a] + 8.000000
Malonyl-CoA[a] +
16.000000 H[a] <=>
16.000000 NAD[a] +
9.000000 CoA[a] +
8.000000 CO2[a] +
Stearate[a] 2.3.1.85
PFB0505c,PFF1275c,PFI
1125c
a_Rfas8
6.000000 NADH[a] +
Acetyl-CoA[a] + 3.000000
Malonyl-CoA[a] +
6.000000 H[a] <=>
6.000000 NAD[a] +
4.000000 CoA[a] +
3.000000 CO2[a] +
Octanoic-acid[a] 2.3.1.85
PFB0505c,PFF1275c,PFI
1125c
a_Rlipo Octanoic-acid[a] <=> lipoyl-E2[a]
2.3.1.12 AND
2.3.1.181 AND
2.8.1.8
PFC0170c,PF10_0407
AND (no gene) AND (no
gene)
er_EX_1PDMI
1-Phosphatidyl-D-myo-
inositol[r] <=>
1-Phosphatidyl-D-myo-
inositol[c] NA
er_EX_Acetate Acetate[r] <=> Acetate[c] NA
er_EX_CoA CoA[r] <=> CoA[c] NA
er_EX_Dolichyl_P Dolichyl-P[r] <=> Dolichyl-P[c] NA
er_EX_Dolichyl_P_D-
mannose Dolichyl-P-D-mannose[r] <=> Dolichyl-P-D-mannose[c] NA
er_EX_H2O H2O[r] <=> H2O[c] NA
er_EX_Palmitoyl-CoA Palmitoyl-CoA[r] <=> Palmitoyl-CoA[c] NA
er_EX_Phosphatidylet
hanolamine
Phosphatidylethanolamine[r
] <=> Phosphatidylethanolamine[c] NA
er_EX_UDP UDP[r] <=> UDP[c] NA
er_EX_UDP-NaG
UDP-N-acetyl-D-
glucosamine[r] <=>
UDP-N-acetyl-D-
glucosamine[c] NA
er_R02654
1-Phosphatidyl-D-myo-
inositol[r] + UDP-N-acetyl-
D-glucosamine[r] <=>
UDP[r] + N-Acetyl-D-
glucosaminylphosphatidylin
ositol[r] 2.4.1.198
PF10_0316,PFF0915w,P
FI1705w
er_R03482
N-Acetyl-D-
glucosaminylphosphatidyli
nositol[r] + H2O[r] <=>
6--alpha-D-Glucosaminyl--
1-phosphatidyl-1D-my[r] +
Acetate[r] 3.5.1.89 PFF1190c,PFI0535w
72
er_Rgpi01
6--alpha-D-Glucosaminyl--
1-phosphatidyl-1D-my[r] +
Palmitoyl-CoA[r] <=> CoA[r] + Cgpi01[r] PFF0740c
er_Rgpi02
3.000000 Dolichyl-P-D-
mannose[r] + Cgpi01[r] <=>
3.000000 Dolichyl-P[r] +
Cgpi02[r] 2.4.1.- PFL0540w,PFL2270w
er_Rgpi03
3.000000 Dolichyl-P-D-
mannose[r] + Cgpi02[r] <=>
3.000000 Dolichyl-P[r] +
Cgpi03[r] 2.4.1.30
er_Rgpi04
Phosphatidylethanolamine[r
] + Cgpi02[r] <=> Cgpi04[r] PFL0685w
er_Rgpi05
Phosphatidylethanolamine[r
] + Cgpi03[r] <=> Cgpi05[r] PFL0685w
er_Rgpi06
3.000000 Dolichyl-P-D-
mannose[r] + Cgpi04[r] <=>
3.000000 Dolichyl-P[r] +
Cgpi05[r] 2.4.1.30
EX_1,2-Diacyl-sn-
glycerol <=> 1,2-Diacyl-sn-glycerol[c] NA
EX_1-Phosphatidyl-D-
myo-inositol <=>
1-Phosphatidyl-D-myo-
inositol[c] NA
EX_4-Amino-5-
hydroxymethyl-2-
methylpyrimidine <=>
4-Amino-5-hydroxymethyl-
2-methylpyrimidine[c] NA
EX_4-Aminobenzoate <=> 4-Aminobenzoate[c] NA
EX_Adenosine <=> Adenosine[c] NA
PFA0160c, MAL8P1.32,
PF13_0252, PF14_0662
EX_ADP-ATP ATP[c] <=> ADP[c] NA PF10_0051, PF10_0366
EX_alpha-D-Glucose <=> alpha-D-Glucose[c] NA PFB0210c
EX_Choline <=> Choline[c] NA PFL0620c
EX_Ethanolamine <=> Ethanolamine[c] NA
EX_Fatty_acid <=> Fatty-acid[c] NA
EX_Formate <=> Formate[c] NA
EX_Glycerol Glycerol[c] <=> NA PF11_0338
EX_Glycine <=> Glycine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_Guanine Guanine[c] <=> NA
PFA0160c, MAL8P1.32,
PF13_0252, PF14_0662
EX_H2O <=> H2O[c] NA
EX_HCO3- <=> HCO3-[c] NA PF14_0679
EX_Homocysteine <=> Homocysteine[c] NA
73
EX_Hypoxanthine <=> Hypoxanthine[c] NA
PFA0160c, MAL8P1.32,
PF13_0252, PF14_0662
EX_Lactate H[c] + Lactate[c] <=> NA PFB0465c, PFI1295c
EX_L-Alanine <=> L-Alanine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Arginine <=> L-Arginine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Asparagine <=> L-Asparagine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Aspartate <=> L-Aspartate[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Cysteine <=> L-Cysteine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Glutamate <=> L-Glutamate[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Glutamine <=> L-Glutamine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Histidine <=> L-Histidine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
74
EX_L-Isoleucine <=> L-Isoleucine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Leucine <=> L-Leucine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Lysine <=> L-Lysine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Methionine <=> L-Methionine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Phenylalanine <=> L-Phenylalanine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Proline <=> L-Proline[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Serine <=> L-Serine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Threonine <=> L-Threonine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Tryptophan <=> L-Tryptophan[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
75
EX_L-Tyrosine <=> L-Tyrosine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_L-Valine <=> L-Valine[c] NA
PFF1430c, PFL0420w,
PFL1515c, PFB0435c,
PFE0775c, PF11_0334
EX_Malate S--Malate[c] <=> NA
EX_NH3 NH3[c] <=> NA
EX_Nicotinamide <=> Nicotinamide[c] NA
EX_Nicotinate <=> Nicotinate[c] NA
EX_Nitrate <=> Nitrate[c] NA
EX_Nitrite <=> Nitrite[c] NA
EX_O2 <=> O2[c] NA
EX_Pantothenate <=> Pantothenate[c] NA
EX_Phosphatidylcholi
ne <=> Phosphatidylcholine[c] NA
EX_Phosphatidylethan
olamine <=> Phosphatidylethanolamine[c] NA
EX_Phosphatidylserin
e <=> Phosphatidylserine[c] NA
EX_Pi <=> Pi[c] NA MAL13P1.206
EX_Putrescine <=> Putrescine[c] NA
EX_Riboflavin <=> Riboflavin[c] NA
EX_Selenite <=> Selenite[c] NA
EX_Spermidine <=> Spermidine[c] NA
EX_Sterol <=> Sterol[c] NA
EX_Thiamin <=> Thiamin[c] NA
EX_Urea Urea[c] <=> NA
EX_V-ATPase ATP[c] + H[c] <=> ADP[c] + Pi[c] NA PFI1670c, PF13_0065
EX_V-PPase PPi[c] + H[c] <=> 2.000000 Pi[c] NA PF14_0541
EX_Xanthine Xanthine[c] <=> NA
PFA0160c, MAL8P1.32,
PF13_0252, PF14_0662
m_EX_5,10mTHF
5,10-
Methylenetetrahydrofolate[
m] <=>
5,10-
Methylenetetrahydrofolate[c] NA
m_EX_AcCoA Acetyl-CoA[m] <=> Acetyl-CoA[c] NA
m_EX_ADP ADP[m] <=> ADP[c] NA
m_EX_a-Keto-Acid alpha-Ketoglutaric-acid[m] <=> alpha-Ketoglutaric-acid[c] NA
m_EX_AMP AMP[m] <=> AMP[c] NA
m_EX_ATP ATP[m] <=> ATP[c] NA
76
m_EX_CoA CoA[m] <=> CoA[c] NA
m_EX_GDP GDP[m] <=> GDP[c] NA
m_EX_Glycine Glycine[m] <=> Glycine[c] NA
m_EX_GTP GTP[m] <=> GTP[c] NA
m_EX_H H[m] <=> H[c] NA
m_EX_H2O H2O[m] <=> H2O[c] NA
m_EX_H2O2 H2O2[m] <=> H2O2[c] NA
m_EX_HCO3- HCO3-[m] <=> HCO3-[c] NA
m_EX_lipoic lipoic-acid[m] <=> lipoic-acid[c] NA
m_EX_Malate S--Malate[m] <=> S--Malate[c] NA
m_EX_NADPshuttle NADP[m] + NADPH[c] <=> NADPH[m] + NADP[c] NA
m_EX_NH3 NH3[m] <=> NH3[c] NA
m_EX_O2 O2[m] <=> O2[c] NA
m_EX_Pi Pi[m] <=> Pi[c] NA
m_EX_Serine L-Serine[m] <=> L-Serine[c] NA
m_EX_Succinyl-CoA Succinyl-CoA[m] <=> Succinyl-CoA[c] NA
m_EX_THF Tetrahydrofolate[m] <=> Tetrahydrofolate[c] NA
m_R00004 H2O[m] + PPi[m] <=> 2.000000 Pi[m] 3.6.1.1
m_R00081
O2[m] + 4.000000
Ferrocytochrome-c[m] <=>
2.000000 H2O[m] +
4.000000 Ferricytochrome-
c[m] 1.9.3.1
PF13_0327,PF14_0288,P
FI1365w,PFI1375w,PF14
_0331,PF14_0721
m_R00115
NADP[m] + 2.000000
Glutathione[m] <=>
NADPH[m] + Glutathione-
disulfide[m] + H[m] 1.8.1.7 PF14_0192
m_R00132 H[m] + HCO3-[m] <=> H2O[m] + CO2[m] 4.2.1.1
m_R00267 NADP[m] + Isocitrate[m] <=>
NADPH[m] + CO2[m] +
alpha-Ketoglutaric-acid[m] +
H[m] 1.1.1.42 PF13_0242
m_R00274
H2O2[m] + 2.000000
Glutathione[m] <=>
2.000000 H2O[m] +
Glutathione-disulfide[m] 1.11.1.9 PFL0595c
m_R00342 NAD[m] + S--Malate[m] <=>
NADH[m] +
Oxaloacetate[m] + H[m] 1.1.99.16 PFF0815w
m_R00351 CoA[m] + Citrate[m] <=>
H2O[m] + Acetyl-CoA[m] +
Oxaloacetate[m] 2.3.3.1 PF10_0218,PFF0455w
m_R00405
ATP[m] + CoA[m] +
Succinate[m] <=>
ADP[m] + Pi[m] + Succinyl-
CoA[m] 6.2.1.5 PF14_0295
m_R00408 FAD[m] + Succinate[m] <=> Fumarate[m] + FADH2[m] 1.3.99.1 PF10_0334,PFL0630w
m_R00432
CoA[m] + Succinate[m] +
GTP[m] <=>
Pi[m] + GDP[m] + Succinyl-
CoA[m] 6.2.1.4 PF10_0334,PF11_0097
77
m_R00945
H2O[m] + Glycine[m] +
5,10-
Methylenetetrahydrofolate[
m] <=>
L-Serine[m] +
Tetrahydrofolate[m] 2.1.2.1 PFL1720w,PF14_0534
m_R01082 S--Malate[m] <=> H2O[m] + Fumarate[m] 4.2.1.2 PFI1340w
m_R01221
NAD[m] + Glycine[m] +
Tetrahydrofolate[m] <=>
NADH[m] + CO2[m] +
NH3[m] + 5,10-
Methylenetetrahydrofolate[m
] + H[m]
1.4.4.2 AND 1.8.1.4
AND 2.1.2.10
(no gene) AND
PF08_0066,PFL1550w
AND
PF13_0345,PF14_0497,
MAL13P1.390
m_R01324 Citrate[m] <=> Isocitrate[m] 4.2.1.3 PF13_0229
m_R02161
2.000000 Ferricytochrome-
c[m] + Ubiquinol[m] <=>
2.000000 Ferrocytochrome-
c[m] + Ubiquinone[m] 1.10.2.2
PF14_0373,PF10_0120,P
F14_0248
m_R02163
Ubiquinone[m] + NADH[c]
+ H[c] <=> Ubiquinol[m] + NAD[c] 1.6.5.3 PFI0735c
m_R08549
NAD[m] + CoA[m] +
alpha-Ketoglutaric-acid[m] <=>
NADH[m] + CO2[m] +
Succinyl-CoA[m] + H[m]
1.2.4.2 AND 1.8.1.4
AND 2.3.1.61
PF11_0256,PF14_0441
AND
PF08_0066,PFL1550w
AND PF13_0121
m_Rlipo ATP[m] + lipoic-acid[m] <=>
lipoyl-E2[m] + AMP[m] +
PPi[m] 2.7.7.63 AND 1.2.4.2
MAL8P1.37,PFI1160w,P
F13_0083 AND
PFC0170c,PF08_0045
m_RxxxM1
Ubiquinone[m] + sn-
Glycerol-3-P[c] <=>
Ubiquinol[m] + Glycerone-
P[c] 1.1.5.3
m_RxxxM2
Ubiquinone[m] + S--
Dihydroorotate[c] <=> Ubiquinol[m] + Orotate[c] 1.3.3.1 PFF0160c
m_RxxxM3
Ubiquinone[m] +
FADH2[m] <=> FAD[m] + Ubiquinol[m]
78
v_R4 Dipeptides[c] <=>
7.000000 L-Glutamate[c] +
10.000000 Glycine[c] +
8.000000 L-Alanine[c] +
8.000000 L-Lysine[c] +
7.000000 L-Aspartate[c] +
3.000000 L-Arginine[c] + L-
Glutamine[c] + 5.000000 L-
Serine[c] + 2.000000 L-
Methionine[c] + 2.000000 L-
Tryptophan[c] + 6.000000 L-
Phenylalanine[c] + L-
Tyrosine[c] + L-Cysteine[c]
+ 13.000000 L-Leucine[c] +
5.000000 L-Histidine[c] +
5.000000 L-Proline[c] +
4.000000 L-Asparagine[c] +
11.000000 L-Valine[c] +
6.000000 L-Threonine[c]
3.4.11.1 OR
3.4.11.21 OR
3.4.11.18 OR
3.4.11.2 OR 3.4.11.9
OR 3.4.22.1 OR
3.4.21.62
PF14_0439 OR PFI1570c
OR
PF10_0150,PF14_0327,P
FE1360c,MAL8P1.140
OR MAL13P1.56 OR
PF14_0517 OR
PF11_0174 OR
PF11_0381,PFE0370c
n_Racet1 Acetyl-CoA[c] <=> acetyl-histone[n] + CoA[c] 2.3.1.48
PF10_0036,PF14_0350,P
F08_0034,PFL1345c,PF1
3_0131,PF10_0200
R00004 H2O[c] + PPi[c] <=> 2.000000 Pi[c] 3.6.1.1
R00036
2.000000 5-
Aminolevulinate[c] <=>
2.000000 H2O[c] +
Porphobilinogen[c] 4.2.1.24 PF14_0381
R00084
H2O[c] + 4.000000
Porphobilinogen[c] <=>
4.000000 NH3[c] +
Hydroxymethylbilane[c] 2.5.1.61 PFL0480w
R00089 ATP[c] <=> PPi[c] + cAMP 4.6.1.1 PFB0420w
R00093
NAD[c] + 2.000000 L-
Glutamate[c] <=>
NADH[c] + alpha-
Ketoglutaric-acid[c] + L-
Glutamine[c] + H[c] 1.4.1.14 PF14_0334
R00100
NADH[c] + 2.000000
Ferricytochrome-b5[c] <=>
NAD[c] + H[c] + 2.000000
Ferrocytochrome-b5[c] 1.6.2.2 PF13_0353,PFI1140w
79
R00104 ATP[c] + NAD[c] <=> NADP[c] + ADP[c] 2.7.1.23
R00112 NAD[c] + NADPH[c] <=> NADH[c] + NADP[c] 1.6.1.1 PF14_0508
R00115
NADP[c] + 2.000000
Glutathione[c] <=>
NADPH[c] + H[c] +
Glutathione-disulfide[c] 1.8.1.7 PF14_0192
R00124 ADP[c] + GTP[c] <=> ATP[c] + GDP[c] 2.7.4.6 PF13_0349,PFF0275c
R00127 ATP[c] + AMP[c] <=> 2.000000 ADP[c] 2.7.4.3
PFD0755c,PF08_0062,P
F10_0086,PFA0530c
R00130
ATP[c] + Dephospho-
CoA[c] <=> ADP[c] + CoA[c] 2.7.1.24 PF14_0415
R00132 H[c] + HCO3-[c] <=> H2O[c] + CO2[c] 4.2.1.1
R00138 H2O[c] + TriP[c] <=> Pi[c] + PPi[c] 3.6.1.25
R00149
H2O[c] + 2.000000 ATP[c]
+ CO2[c] + NH3[c] <=>
2.000000 ADP[c] + Pi[c] +
Carbamoyl-P[c] 6.3.4.16 PF13_0044
R00156 ATP[c] + UDP[c] <=> ADP[c] + UTP[c] 2.7.4.6 PF13_0349,PFF0275c
R00158 ATP[c] + UMP[c] <=> ADP[c] + UDP[c] 2.7.4.14 PFA0555c
R00161 ATP[c] + FMN[c] <=> PPi[c] + FAD[c] 2.7.7.2 PF10_0147
R00174 ATP[c] + Pyridoxal[c] <=> ADP[c] + Pyridoxal-P[c] 2.7.1.35 PFF0775w
R00177
H2O[c] + ATP[c] + L-
Methionine[c] <=>
Pi[c] + PPi[c] + S-Adenosyl-
L-methionine[c] 2.5.1.6 PFI1090w
R00178
S-Adenosyl-L-
methionine[c] + H[c] <=>
CO2[c] + S-
Adenosylmethioninamine[c] 4.1.1.50 PF10_0322
R00181 H2O[c] + AMP[c] <=> NH3[c] + IMP[c] 3.5.4.6 MAL13P1.146
R00184 ATP[c] + AMP[c] <=> H2O[c] + AppppA[c] 3.6.1.17 PFE1035c
R00191 H2O[c] + cAMP <=> AMP[c] 3.1.4.17
PFL0475w,PF14_0672,
MAL13P1.118,MAL13P
1.119
R00192
H2O[c] + S-Adenosyl-L-
homocysteine[c] <=>
Homocysteine[c] +
Adenosine[c] 3.3.1.1 PFE1050w
R00200
ADP[c] +
Phosphoenolpyruvate[c] <=> ATP[c] + Pyruvate[c] 2.7.1.40 PF10_0363,PFF1300w
R00229
ATP[c] + CoA[c] +
Acetate[c] <=>
ADP[c] + Pi[c] + Acetyl-
CoA[c] 6.2.1.13 PFF1350c,PF14_0357
R00234 Acetyl-CoA[c] <=> CoA[c] 2.3.1.88
PF10_0036,PFL2120w,
MAL8P1.200,PFA0465c
R00235
ATP[c] + CoA[c] +
Acetate[c] <=>
PPi[c] + AMP[c] + Acetyl-
CoA[c] 6.2.1.1 PFF1350c
R00238 2.000000 Acetyl-CoA[c] <=>
CoA[c] + Acetoacetyl-
CoA[c] 2.3.1.9 PF14_0484
80
R00243
H2O[c] + NAD[c] + L-
Glutamate[c] <=>
NADH[c] + NH3[c] + alpha-
Ketoglutaric-acid[c] + H[c] 1.4.1.2 PF08_0132
R00248
H2O[c] + NADP[c] + L-
Glutamate[c] <=>
NADPH[c] + NH3[c] +
alpha-Ketoglutaric-acid[c] +
H[c] 1.4.1.4 PF14_0164,PF14_0286
R00253
ATP[c] + NH3[c] + L-
Glutamate[c] <=>
ADP[c] + Pi[c] + L-
Glutamine[c] 6.3.1.2 PFI1110w
R00257
H2O[c] + ATP[c] + L-
Glutamine[c] + Deamino-
NAD+[c] <=>
NAD[c] + PPi[c] + AMP[c]
+ L-Glutamate[c] 6.3.5.1 PFI1310w
R00274
H2O2[c] + 2.000000
Glutathione[c] <=>
2.000000 H2O[c] +
Glutathione-disulfide[c] 1.11.1.9 OR 2.5.1.18
R00277
H2O[c] + O2[c] +
Pyridoxamine-P[c] <=>
NH3[c] + Pyridoxal-P[c] +
H2O2[c] 1.4.3.5 PF14_0570
R00278 O2[c] + Pyridoxine-P[c] <=> Pyridoxal-P[c] + H2O2[c] 1.4.3.5 PF14_0570
R00289 UTP[c] + D-Glucose-1-P[c] <=> PPi[c] + UDP-glucose[c] 2.7.7.9 MAL13P1.218
R00310 Protoporphyrin[c] <=> Heme[c] + 2.000000 H[c] 4.99.1.1 MAL13P1.326
R00329 H2O[c] + XDP[c] <=> Pi[c] + Xanthosine-5'-P[c] 3.6.1.6
MAL13P1.121,MAL13P
1.248
R00330 ATP[c] + GDP[c] <=> ADP[c] + GTP[c] 2.7.4.6 PF13_0349,PFF0275c
R00332 ATP[c] + GMP[c] <=> ADP[c] + GDP[c] 2.7.4.8 PFI1420w
R00341 ATP[c] + Oxaloacetate[c] <=>
ADP[c] + CO2[c] +
Phosphoenolpyruvate[c] 4.1.1.49 PF13_0234
R00342 NAD[c] + S--Malate[c] <=>
NADH[c] + Oxaloacetate[c]
+ H[c] 1.1.1.37 PFF0895w
R00345
H2O[c] + CO2[c] +
Phosphoenolpyruvate[c] <=> Pi[c] + Oxaloacetate[c] 4.1.1.31 PF14_0246
R00355
alpha-Ketoglutaric-acid[c]
+ L-Aspartate[c] <=>
L-Glutamate[c] +
Oxaloacetate[c] 2.6.1.1 PFB0200c
R00405
ATP[c] + CoA[c] +
Succinate[c] <=>
ADP[c] + Pi[c] + Succinyl-
CoA[c] 6.2.1.5 PF14_0295
R00408 FAD[m] + Succinate[c] <=> FADH2[m] + Fumarate[c] 1.3.99.1 PF10_0334,PFL0630w
R00416
UTP[c] + N-Acetyl-alpha-
D-glucosamine-1-P[c] <=>
PPi[c] + UDP-N-acetyl-D-
glucosamine[c] 2.7.7.23 MAL13P1.218
R00424 H2O[c] + GTP[c] <=>
Formate[c] + 2-Amino-4-
hydroxy-6--erythro-1,2,3-
trihydroxy[c] 3.5.4.16 PFL1155w
R00434 GTP[c] <=> PPi[c] + 3',5'-Cyclic-GMP[c] 4.6.1.2
MAL13P1.301,PF11_039
5
81
R00462 L-Lysine[c] <=> CO2[c] + Cadaverine[c] 4.1.1.18 PFD0285c,PFD0670c
R00497
ATP[c] + Glycine[c] +
gamma-L-Glutamyl-L-
cysteine[c] <=>
ADP[c] + Pi[c] +
Glutathione[c] 6.3.2.3 PFE0605c
R00512 ATP[c] + CMP[c] <=> ADP[c] + CDP[c] 2.7.4.14 PFA0555c
R00548 H2O[c] + FMN[c] <=> Pi[c] + Riboflavin[c] 3.1.3.2 PF14_0036,PFI0880c
R00549 ATP[c] + Riboflavin[c] <=> ADP[c] + FMN[c] 2.7.1.26 MAL13P1.292
R00551 H2O[c] + L-Arginine[c] <=> L-Ornithine[c] + Urea[c] 3.5.3.1 PFI0320w
R00570 ATP[c] + CDP[c] <=> ADP[c] + CTP[c] 2.7.4.6 PF13_0349,PFF0275c
R00573
H2O[c] + ATP[c] + L-
Glutamine[c] + UTP[c] <=>
ADP[c] + Pi[c] + L-
Glutamate[c] + CTP[c] 6.3.4.2 PF14_0100
R00575
H2O[c] + 2.000000 ATP[c]
+ L-Glutamine[c] + HCO3-
[c] <=>
2.000000 ADP[c] + Pi[c] +
L-Glutamate[c] +
Carbamoyl-P[c] 6.3.5.5 PF13_0044
R00578
H2O[c] + ATP[c] + L-
Aspartate[c] + L-
Glutamine[c] <=>
PPi[c] + AMP[c] + L-
Glutamate[c] + L-
Asparagine[c] 6.3.5.4 PFC0395w
R00617
ATP[c] + Thiamin-
monoP[c] <=> ADP[c] + Thiamin-diP[c] 2.7.4.16
R00619 ATP[c] + Thiamin[c] <=> AMP[c] + Thiamin-diP[c] 2.7.6.2 PFI1195c
R00658 2-Phospho-D-glycerate[c] <=>
H2O[c] +
Phosphoenolpyruvate[c] 4.2.1.11 PF10_0155
R00667
alpha-Ketoglutaric-acid[c]
+ L-Ornithine[c] <=>
L-Glutamate[c] + L-
Glutamate-5-
semialdehyde[c] 2.6.1.13 PFF0435w
R00670 L-Ornithine[c] <=> CO2[c] + Putrescine[c] 4.1.1.17 PF10_0322
R00694
alpha-Ketoglutaric-acid[c]
+ L-Phenylalanine[c] <=>
L-Glutamate[c] +
Phenylpyruvate[c] 2.6.1.57 OR 2.6.1.1 PFB0200c OR PFB0200c
R00703 NAD[c] + Lactate[c] <=>
NADH[c] + Pyruvate[c] +
H[c] 1.1.1.27 PF13_0141,PF13_0144
R00715
H2O[c] + NAD[c] + N6--L-
1,3-Dicarboxypropyl--L-
lysine[c] <=>
NADH[c] + alpha-
Ketoglutaric-acid[c] + L-
Lysine[c] + H[c] 1.5.1.7 PFB0880w
R00734
alpha-Ketoglutaric-acid[c]
+ L-Tyrosine[c] <=>
L-Glutamate[c] + 3--4-
Hydroxyphenyl-pyruvate[c] 2.6.1.57 OR 2.6.1.1 PFB0200c OR PFB0200c
R00768
L-Glutamine[c] + beta-D-
Fructose-6-P[c] <=>
L-Glutamate[c] + D-
Glucosamine-6-P[c] 2.6.1.16 PF10_0245
R00794
H2O[c] + NAD[c] +
Nitrite[c] <=>
NADH[c] + H[c] +
Nitrate[c] 1.7.1.1 PF13_0353
R00796
H2O[c] + NADP[c] +
Nitrite[c] <=>
NADPH[c] + H[c] +
Nitrate[c] 1.7.1.3 PF13_0353
82
R00830
Glycine[c] + Succinyl-
CoA[c] <=>
CoA[c] + CO2[c] + 5-
Aminolevulinate[c] 2.3.1.37 PFL2210w
R00840 D-Glucose-6-P[c] <=> Inositol-1-P[c] 5.5.1.4 PFE0585c
R00842
NAD[c] + sn-Glycerol-3-
P[c] <=>
NADH[c] + H[c] +
Glycerone-P[c] 1.1.1.8
PFC0275w,PFL0780w,P
F11_0157
R00847 ATP[c] + Glycerol[c] <=> ADP[c] + sn-Glycerol-3-P[c] 2.7.1.30 PF13_0269
R00851
Acyl-CoA[c] + sn-
Glycerol-3-P[c] <=>
CoA[c] + 1-Acyl-sn-
glycerol-3-P[c] 2.3.1.15 PF13_0100,PFL0620c
R00867 ATP[c] + D-Fructose[c] <=>
ADP[c] + beta-D-Fructose-
6-P[c] 2.7.1.1 PFF1155w
R00885
GTP[c] + D-Mannose-1-
P[c] <=> PPi[c] + GDP-mannose[c] 2.7.7.13 PFL0675c,PF14_0774
R00888 GDP-mannose[c] <=>
H2O[c] + GDP-4-dehydro-6-
deoxy-D-mannose[c] 4.2.1.47 PF08_0077
R00894
ATP[c] + L-Glutamate[c] +
L-Cysteine[c] <=>
ADP[c] + Pi[c] + gamma-L-
Glutamyl-L-cysteine[c] 6.3.2.2 PFI0925w
R00939
NADP[c] +
Tetrahydrofolate[c] <=>
NADPH[c] + H[c] +
Dihydrofolate[c] 1.5.1.3 PFD0830w
R00942
ATP[c] + L-Glutamate[c] +
Tetrahydrofolate[c] <=>
ADP[c] + Pi[c] +
Tetrahydrofolyl--Glu--2-[c] 6.3.2.17 PF13_0140
R00945
H2O[c] + Glycine[c] +
5,10-
Methylenetetrahydrofolate[
c] <=>
L-Serine[c] +
Tetrahydrofolate[c] 2.1.2.1 PFL1720w,PF14_0534
R00946
Homocysteine[c] + 5-
Methyltetrahydrofolate[c] <=>
L-Methionine[c] +
Tetrahydrofolate[c] 2.1.1.13
R00959 D-Glucose-1-P[c] <=> alpha-D-Glucose-6-P[c] 5.4.2.2 PF10_0122
R00965 Orotidine-5'-P[c] <=> CO2[c] + UMP[c] 4.1.1.23 PF10_0225
R00969
H2O[c] + P1,P4-Bis-5'-
uridyl--tetraP[c] <=> UTP[c] + UMP[c] 3.6.1.17 PFE1035c
R00986
L-Glutamine[c] +
Chorismate[c] <=>
Pyruvate[c] + L-
Glutamate[c] +
Anthranilate[c] 4.1.3.27 MAL13P1.319
R01004 H2O[c] + Dolichyl-diP[c] <=> Pi[c] + Dolichyl-P[c] 3.6.1.43 MAL8P1.202
R01007
UDP-N-acetyl-D-
glucosamine[c] + Dolichyl-
P[c] <=>
UMP[c] + N-Acetyl-D-
glucosaminyldiphosphodolic
hol[c] 2.7.8.15 PFC0935c,MAL8P1.133
R01009
GDP-mannose[c] +
Dolichyl-P[c] <=>
GDP[c] + Dolichyl-P-D-
mannose[c] 2.4.1.83 PF11_0427
R01015
2R--2-Hydroxy-3--
phosphonooxy--propanal[c] <=> Glycerone-P[c] 5.3.1.1 PF14_0378,PFC0831w
83
R01018 CTP[c] + Dolichol[c] <=> Dolichyl-P[c] + CDP[c] 2.7.1.108 PFA0485w
R01021 ATP[c] + Choline[c] <=> ADP[c] + Choline-P[c] 2.7.1.32 PF14_0020
R01030
H2O[c] + sn-glycero-3-
Phosphocholine[c] <=>
sn-Glycerol-3-P[c] +
Choline[c] 3.1.4.46 PF14_0060
R01036 NAD[c] + Glycerol[c] <=>
NADH[c] + H[c] + D-
Glyceraldehyde[c] 1.1.1.21 MAL13P1.324
R01049 ATP[c] + D-Ribose-5-P[c] <=>
AMP[c] + 5-Phospho-alpha-
D-ribose-1-diP[c] 2.7.6.1 PF13_0143,PF13_0157
R01056 D-Ribulose-5-P[c] <=> D-Ribose-5-P[c] 5.3.1.6 PFE0730c
R01057 alpha-D-Ribose-1-P[c] <=> D-Ribose-5-P[c] 5.4.2.2 PF10_0122
R01061
NAD[c] + Pi[c] + 2R--2-
Hydroxy-3--phosphonooxy-
-propanal[c] <=>
NADH[c] + H[c] + 3-
Phospho-D-glyceroyl-P[c] 1.2.1.12 PF14_0598
R01066 2-Deoxy-D-ribose-5-P[c] <=>
Acetaldehyde[c] + 2R--2-
Hydroxy-3--phosphonooxy--
propanal[c] 4.1.2.4 PF10_0210
R01067
D-Xylulose-5-P[c] + D-
Erythrose-4-P[c] <=>
D-Fructose-6-P[c] + 2R--2-
Hydroxy-3--phosphonooxy--
propanal[c] 2.2.1.1 PFF0530w
R01070 beta-D-Fructose-1,6-bisP[c] <=>
Glycerone-P[c] + 2R--2-
Hydroxy-3--phosphonooxy--
propanal[c] 4.1.2.13 PF14_0425
R01082 S--Malate[c] <=> H2O[c] + Fumarate[c] 4.2.1.2 PFI1340w
R01083
N6--1,2-Dicarboxyethyl--
AMP[c] <=> AMP[c] + Fumarate[c] 4.3.2.2 PFB0295w
R01090
alpha-Ketoglutaric-acid[c]
+ L-Leucine[c] <=>
L-Glutamate[c] + 4-Methyl-
2-oxopentanoate[c] 2.6.1.42 PF14_0557
R01126 H2O[c] + IMP[c] <=> Pi[c] + Inosine[c] 3.1.3.5 PFL0305c
R01130
H2O[c] + NAD[c] +
IMP[c] <=>
NADH[c] + H[c] +
Xanthosine-5'-P[c] 1.1.1.205 PFI1020c
R01132
5-Phospho-alpha-D-ribose-
1-diP[c] + Hypoxanthine[c] <=> PPi[c] + IMP[c] 2.4.2.8 PF10_0121
R01135
GTP[c] + L-Aspartate[c] +
IMP[c] <=>
Pi[c] + GDP[c] + N6--1,2-
Dicarboxyethyl--AMP[c] 6.3.4.4 PF13_0287
R01137 ATP[c] + dADP[c] <=> ADP[c] + dATP[c] 2.7.4.6 PF13_0349,PFF0275c
R01185 H2O[c] + Inositol-1-P[c] <=> Pi[c] + myo-Inositol[c] 3.1.3.25
84
R01214
alpha-Ketoglutaric-acid[c]
+ L-Valine[c] <=>
L-Glutamate[c] + 3-Methyl-
2-oxobutanoic-acid[c] 2.6.1.42 PF14_0557
R01220
NADP[c] + 5,10-
Methylenetetrahydrofolate[
c] <=>
NADPH[c] + 5,10-
Methenyltetrahydrofolate[c] 1.5.1.5 PFF1490w
R01229
5-Phospho-alpha-D-ribose-
1-diP[c] + Guanine[c] <=> PPi[c] + GMP[c] 2.4.2.8 PF10_0121
R01231
H2O[c] + ATP[c] + L-
Glutamine[c] + Xanthosine-
5'-P[c] <=>
PPi[c] + AMP[c] + L-
Glutamate[c] + GMP[c] 6.3.5.2 PF10_0123
R01232
H2O[c] + P1,P4-Bis-5'-
guanosyl--tetraP[c] <=> GTP[c] + GMP[c] 3.6.1.17 PFE1035c
R01234
H2O[c] + 3',5'-Cyclic-
GMP[c] <=> GMP[c] 3.1.4.17
PFL0475w,PF14_0672,
MAL13P1.118,MAL13P
1.119
R01248 NAD[c] + L-Proline[c] <=>
NADH[c] + H[c] + S--1-
Pyrroline-5-carboxylate[c] 1.5.1.2 MAL13P1.284
R01252
O2[c] + alpha-Ketoglutaric-
acid[c] + L-Proline[c] <=>
CO2[c] + Succinate[c] +
trans-4-Hydroxy-L-
proline[c] 1.14.11.2 MAL8P1.8
R01268 H2O[c] + Nicotinamide[c] <=> NH3[c] + Nicotinate[c] 3.5.1.19 PFC0910w
R01280
ATP[c] + CoA[c] +
Hexadecanoic-acid[c] <=>
PPi[c] + AMP[c] +
Palmitoyl-CoA[c] 6.2.1.3
PF14_0761,PFA0455c,P
FI0980w,PF14_0751,PF
B0685c,PFL2570w,PFF0
945c,PF07_0129,PFB069
5c,PFE1250w,PFL0035c,
PFL1880w,MAL13P1.48
5,PFC0050c,PFD0085c,P
FF0290w
R01281
L-Serine[c] + Palmitoyl-
CoA[c] <=>
CoA[c] + CO2[c] + 3-
Dehydrosphinganine[c] 2.3.1.50 PF14_0155
R01312
H2O[c] +
Phosphatidylcholine[c] <=>
Choline-P[c] + 1,2-Diacyl-
sn-glycerol[c] 3.1.4.3 PF10_0132
R01315
H2O[c] +
Phosphatidylcholine[c] <=>
Fatty-acid[c] + 1-Acyl-sn-
glycero-3-phosphocholine[c] 3.1.1.4
PFI1180w,PFB0410c,PF
B0870w,MAL13P1.285
R01321
CDP-choline[c] + 1,2-
Diacyl-sn-glycerol[c] <=>
CMP[c] +
Phosphatidylcholine[c] 2.7.8.2 PFF1375c
R01326 ATP[c] + D-Mannose[c] <=> ADP[c] + D-Mannose-6-P[c] 2.7.1.1 PFF1155w
85
R01369
H2O[c] +
Triacylglycerol[c] <=>
Carboxylate[c] +
Diacylglycerol[c] 3.1.1.3
R01397
L-Aspartate[c] +
Carbamoyl-P[c] <=>
Pi[c] + N-Carbamoyl-L-
aspartate[c] 2.1.3.2 MAL13P1.221
R01402 Pi[c] + MTA[c] <=>
Adenine[c] + S-Methyl-5-
thio-D-ribose-1-phosphate[c] 2.4.2.28
R01468 ATP[c] + Ethanolamine[c] <=> ADP[c] + Ethanolamine-P[c] 2.7.1.82 PF11_0257
R01470
H2O[c] + sn-glycero-3-
Phosphoethanolamine[c] <=>
sn-Glycerol-3-P[c] +
Ethanolamine[c] 3.1.4.46 PF14_0060
R01497
UDP-glucose[c] + N-
Acylsphingosine[c] <=>
UDP[c] +
Glucosylceramide[c] 2.4.1.80 PF11_0427
R01512
ATP[c] + 3-Phospho-D-
glycerate[c] <=>
ADP[c] + 3-Phospho-D-
glyceroyl-P[c] 2.7.2.3 PFI1105w,MAL13P1.40
R01518 2-Phospho-D-glycerate[c] <=> 3-Phospho-D-glycerate[c] 5.4.2.1 PF11_0208,PFD0660w
R01528
NADP[c] + 6-Phospho-D-
gluconate[c] <=>
NADPH[c] + CO2[c] + H[c]
+ D-Ribulose-5-P[c] 1.1.1.44 PF14_0520
R01529 D-Ribulose-5-P[c] <=> D-Xylulose-5-P[c] 5.1.3.1 PFL0960w
R01560 H2O[c] + Adenosine[c] <=> NH3[c] + Inosine[c] 3.5.4.4 PF10_0289
R01561 Pi[c] + Adenosine[c] <=>
Adenine[c] + alpha-D-
Ribose-1-P[c] 2.4.2.1 PFE0660c
R01625 CoA[c] + Apo-ACP[c] <=> Adenosine-3',5'-bisP[c] 2.7.8.7 PFD0980w
R01641
D-Ribose-5-P[c] + D-
Xylulose-5-P[c] <=>
2R--2-Hydroxy-3--
phosphonooxy--propanal[c]
+ D-Sedoheptulose-7-P[c] 2.2.1.1 PFF0530w
R01655
H2O[c] + 5,10-
Methenyltetrahydrofolate[c
] <=>
H[c] + 10-
Formyltetrahydrofolate[c] 3.5.4.9 PFF1490w
R01658
Isopentenyl-diP[c] +
Dimethylallyl-diP[c] <=> PPi[c] + Geranyl-diP[c] 2.5.1.1 PF11_0295,PFB0130w
R01714
5-O--1-Carboxyvinyl--3-
phosphoshikimate[c] <=> Pi[c] + Chorismate[c] 4.2.3.5 PFF1105c
R01716
L-Glutamine[c] +
Chorismate[c] <=>
L-Glutamate[c] + 4-Amino-
4-deoxychorismate[c] 2.6.1.85 PFI1100w
R01724
5-Phospho-alpha-D-ribose-
1-diP[c] + Nicotinate[c] <=>
PPi[c] + Nicotinate-D-
ribonucleotide[c] 2.4.2.11 PFF1410c
R01736
H2O[c] + R--S-
Lactoylglutathione[c] <=>
Glutathione[c] + R--
Lactate[c] 3.1.2.6 PFD0311w,PFL0285w
86
R01786
ATP[c] + alpha-D-
Glucose[c] <=>
ADP[c] + alpha-D-Glucose-
6-P[c] 2.7.1.1 PFF1155w
R01787 NADP[c] + D-Sorbitol[c] <=>
NADPH[c] + H[c] + alpha-
D-Glucose[c] 1.1.1.21 MAL13P1.324
R01799 CTP[c] + Phosphatidate[c] <=>
PPi[c] + CDP-
diacylglycerol[c] 2.7.7.41 PF14_0097
R01800
L-Serine[c] + CDP-
diacylglycerol[c] <=>
CMP[c] +
Phosphatidylserine[c] 2.7.8.8 MAL13P1.335
R01801
sn-Glycerol-3-P[c] + CDP-
diacylglycerol[c] <=>
CMP[c] +
PhosphatidylglyceroP[c] 2.7.8.5 MAL8P1.58
R01802
myo-Inositol[c] + CDP-
diacylglycerol[c] <=>
CMP[c] + 1-Phosphatidyl-D-
myo-inositol[c] 2.7.8.11 MAL13P1.82
R01818 D-Mannose-6-P[c] <=> D-Mannose-1-P[c] 5.4.2.8 PF10_0169
R01819 D-Mannose-6-P[c] <=> beta-D-Fructose-6-P[c] 5.3.1.8 MAL8P1.156
R01826
H2O[c] +
Phosphoenolpyruvate[c] +
D-Erythrose-4-P[c] <=>
Pi[c] + 2-Dehydro-3-deoxy-
D-arabino-heptonate-7-P[c] 2.5.1.54
R01857 ATP[c] + dGDP[c] <=> ADP[c] + dGTP[c] 2.7.4.6 PF13_0349,PFF0275c
R01863 Pi[c] + Inosine[c] <=>
Hypoxanthine[c] + alpha-D-
Ribose-1-P[c] 2.4.2.1 PFE0660c
R01870
5-Phospho-alpha-D-ribose-
1-diP[c] + Orotate[c] <=> PPi[c] + Orotidine-5'-P[c] 2.4.2.10 PFE0630c
R01890 CTP[c] + Choline-P[c] <=> PPi[c] + CDP-choline[c] 2.7.7.15 MAL13P1.86
R01891
N-Acylsphingosine[c] +
CDP-choline[c] <=> CMP[c] + Sphingomyelin[c] 2.7.8.3 PFF1210w
R01909 ATP[c] + Pyridoxine[c] <=> ADP[c] + Pyridoxine-P[c] 2.7.1.35 PFF0775w
R01920
Putrescine[c] + S-
Adenosylmethioninamine[c
] <=> MTA[c] + Spermidine[c] 2.5.1.16 PF11_0301
R01977
NADP[c] + R--3-
Hydroxybutanoyl-CoA[c] <=>
NADPH[c] + H[c] +
Acetoacetyl-CoA[c] 1.1.1.36 PFF1265w
R01993
H2O[c] + S--
Dihydroorotate[c] <=> N-Carbamoyl-L-aspartate[c] 3.5.2.3 PF14_0697
R02003
Isopentenyl-diP[c] +
Geranyl-diP[c] <=>
PPi[c] + trans,trans-
Farnesyl-diP[c] 2.5.1.10 PF11_0295
R02016 NAD[c] + Thioredoxin[c] <=>
NADH[c] + H[c] +
Thioredoxin-disulfide[c] 1.8.1.9 PFI1170c
R02017
H2O[c] + dADP[c] +
Thioredoxin-disulfide[c] <=> ADP[c] + Thioredoxin[c] 1.17.4.1
PF10_0154,PF14_0053,P
F14_0352
87
R02018
H2O[c] + Thioredoxin-
disulfide[c] + dUDP[c] <=> UDP[c] + Thioredoxin[c] 1.17.4.1
PF10_0154,PF14_0053,P
F14_0352
R02019
H2O[c] + Thioredoxin-
disulfide[c] + dGDP[c] <=> GDP[c] + Thioredoxin[c] 1.17.4.1
PF10_0154,PF14_0053,P
F14_0352
R02024
H2O[c] + Thioredoxin-
disulfide[c] + dCDP[c] <=> CDP[c] + Thioredoxin[c] 1.17.4.1
PF10_0154,PF14_0053,P
F14_0352
R02029
H2O[c] +
PhosphatidylglyceroP[c] <=>
Pi[c] +
Phosphatidylglycerol[c] 3.1.3.27
R02030
CDP-diacylglycerol[c] +
Phosphatidylglycerol[c] <=> CMP[c] + Cardiolipin[c] 2.7.8.- PFF0465c
R02035
H2O[c] + D-Glucono-1,5-
lactone-6-P[c] <=> 6-Phospho-D-gluconate[c] 3.1.1.31 PF14_0511
R02037
S-Adenosyl-L-
methionine[c] +
Ethanolamine-P[c] <=>
S-Adenosyl-L-
homocysteine[c] + N-
Methylethanolamine-P[c] 2.1.1.103 MAL13P1.214
R02038
CTP[c] + Ethanolamine-
P[c] <=>
PPi[c] + CDP-
ethanolamine[c] 2.7.7.14 PF13_0253
R02052
H2O[c] +
Phosphatidylethanolamine[
c] <=>
Ethanolamine-P[c] + 1,2-
Diacyl-sn-glycerol[c] 3.1.4.3 PF10_0132
R02053
H2O[c] +
Phosphatidylethanolamine[
c] <=>
Fatty-acid[c] + 1-Acyl-sn-
glycero-3-
phosphoethanolamine[c] 3.1.1.4
PFI1180w,PFB0410c,PF
B0870w,MAL13P1.285
R02055 Phosphatidylserine[c] <=>
CO2[c] +
Phosphatidylethanolamine[c] 4.1.1.65 PFI1370c
R02057
CDP-ethanolamine[c] +
1,2-Diacyl-sn-glycerol[c] <=>
CMP[c] +
Phosphatidylethanolamine[c] 2.7.8.1 PFF1375c
R02058
Acetyl-CoA[c] + D-
Glucosamine-6-P[c] <=>
CoA[c] + N-Acetyl-D-
glucosamine-6-P[c] 2.3.1.4
R02061
Isopentenyl-diP[c] +
trans,trans-Farnesyl-diP[c] <=>
PPi[c] + Geranylgeranyl-
diP[c] 2.5.1.29 PF11_0483
R02093 ATP[c] + dTDP[c] <=> ADP[c] + dTTP[c] 2.7.4.6 PF13_0349,PFF0275c
R02094 ATP[c] + dTMP[c] <=> ADP[c] + dTDP[c] 2.7.4.9 PFL2465c
R02098 ATP[c] + dUMP[c] <=> ADP[c] + dUDP[c] 2.7.4.9 PFL2465c
R02100 H2O[c] + dUTP[c] <=> PPi[c] + dUMP[c] 3.6.1.23 PF11_0282
R02101
5,10-
Methylenetetrahydrofolate[
c] + dUMP[c] <=> dTMP[c] + Dihydrofolate[c] 2.1.1.45 PFD0830w
88
R02114
Phosphatidylcholine[c] +
Sterol[c] <=>
Steryl-ester[c] + 1-Acyl-sn-
glycero-3-phosphocholine[c] 2.3.1.43 PFF1420w
R02135
H2O[c] + Thiamin-
monoP[c] <=> Pi[c] + Thiamin[c] 3.1.3.- PF07_0059
R02142
5-Phospho-alpha-D-ribose-
1-diP[c] + Xanthine[c] <=> PPi[c] + Xanthosine-5'-P[c] 2.4.2.8 PF10_0121
R02199
alpha-Ketoglutaric-acid[c]
+ L-Isoleucine[c] <=>
L-Glutamate[c] + S--3-
Methyl-2-oxopentanoic-
acid[c] 2.6.1.42 PF14_0557
R02237
ATP[c] + L-Glutamate[c] +
Dihydropteroate[c] <=>
ADP[c] + Pi[c] +
Dihydrofolate[c] 6.3.2.12 PF13_0140
R02239 H2O[c] + Phosphatidate[c] <=>
Pi[c] + 1,2-Diacyl-sn-
glycerol[c] 3.1.3.4 PFC0150w
R02240
ATP[c] + 1,2-Diacyl-sn-
glycerol[c] <=> ADP[c] + Phosphatidate[c] 2.7.1.107 PF14_0681,PFI1485c
R02241
Acyl-CoA[c] + 1-Acyl-sn-
glycerol-3-P[c] <=> CoA[c] + Phosphatidate[c] 2.3.1.51 PF14_0421
R02251
Acyl-CoA[c] + 1,2-Diacyl-
sn-glycerol[c] <=> CoA[c] + Triacylglycerol[c] 2.3.1.20 PFC0995c
R02276
H2O[c] + 5-
Acetamidopentanoate[c] <=>
Acetate[c] + 5-
Aminopentanoate[c] 3.5.1.63
R02319 H2O[c] + XTP[c] <=> Pi[c] + XDP[c] 3.6.1.15 PF14_0297
R02325 H2O[c] + dCTP[c] <=> NH3[c] + dUTP[c] 3.5.4.13 PF13_0259
R02326 ATP[c] + dCDP[c] <=> ADP[c] + dCTP[c] 2.7.4.6 PF13_0349,PFF0275c
R02331 ATP[c] + dUDP[c] <=> ADP[c] + dUTP[c] 2.7.4.6 PF13_0349,PFF0275c
R02412 ATP[c] + Shikimate[c] <=> ADP[c] + Shikimate-3-P[c] 2.7.1.71 PFB0280w
R02413 NADP[c] + Shikimate[c] <=>
NADPH[c] + H[c] + 3-
Dehydroshikimate[c] 1.1.1.25
R02493 ATP[c] + Pyridoxamine[c] <=> ADP[c] + Pyridoxamine-P[c] 2.7.1.35 PFF0775w
R02530 R--S-Lactoylglutathione[c] <=>
Glutathione[c] +
Methylglyoxal[c] 4.4.1.5 PF11_0145,PFF0230c
R02531 NAD[c] + Lactaldehyde[c] <=>
NADH[c] + H[c] +
Methylglyoxal[c] 1.1.1.21 MAL13P1.324
R02541
H2O[c] +
Sphingomyelin[c] <=>
N-Acylsphingosine[c] +
Choline-P[c] 3.1.4.12 PFL1870c
R02577
NADP[c] + Propane-1,2-
diol[c] <=>
NADPH[c] + H[c] +
Lactaldehyde[c] 1.1.1.21 MAL13P1.324
89
R02736
NADP[c] + beta-D-
Glucose-6-P[c] <=>
NADPH[c] + H[c] + D-
Glucono-1,5-lactone-6-P[c] 1.1.1.49 PF14_0511
R02739 alpha-D-Glucose-6-P[c] <=> beta-D-Glucose-6-P[c] 5.3.1.9 PF14_0341
R02740 alpha-D-Glucose-6-P[c] <=> beta-D-Fructose-6-P[c] 5.3.1.9 PF14_0341
R02918 ATP[c] + L-Tyrosine[c] <=>
PPi[c] + AMP[c] + L-
Tyrosyl-tRNA-Tyr-[c] 6.1.1.1 PF11_0181,MAL8P1.125
R02971 ATP[c] + Pantetheine[c] <=>
ADP[c] + Pantetheine-4'-
P[c] 2.7.1.33 PF14_0200,PF14_0354
R02973 H2O[c] + Pantetheine[c] <=>
Pantothenate[c] +
Thioethanolamine[c] 3.5.1.92
R02978 NADP[c] + Sphinganine[c] <=>
NADPH[c] + H[c] + 3-
Dehydrosphinganine[c] 1.1.1.102 PFD0465c
R03005
ATP[c] + Nicotinate-D-
ribonucleotide[c] <=> PPi[c] + Deamino-NAD+[c] 2.7.7.18 PF13_0159
R03018 ATP[c] + Pantothenate[c] <=>
ADP[c] + D-4'-
Phosphopantothenate[c] 2.7.1.33 PF14_0200,PF14_0354
R03035
ATP[c] + Pantetheine-4'-
P[c] <=> PPi[c] + Dephospho-CoA[c] 2.7.7.3 PF07_0018
R03038 ATP[c] + L-Alanine[c] <=>
PPi[c] + AMP[c] + L-
Alanyl-tRNA[c] 6.1.1.7 PF13_0354
R03067
4-Aminobenzoate[c] + 2-
Amino-7,8-dihydro-4-
hydroxy-6--
diphosphooxy[c] <=> PPi[c] + Dihydropteroate[c] 2.5.1.15 PF08_0095
R03083
2-Dehydro-3-deoxy-D-
arabino-heptonate-7-P[c] <=> Pi[c] + 3-Dehydroquinate[c] 4.2.3.4
R03084 3-Dehydroquinate[c] <=>
H2O[c] + 3-
Dehydroshikimate[c] 4.2.1.10
R03165 Hydroxymethylbilane[c] <=>
H2O[c] +
Uroporphyrinogen-III[c] 4.2.1.75
R03197 Uroporphyrinogen-III[c] <=>
4.000000 CO2[c] +
Coproporphyrinogen-III[c] 4.1.1.37 PFF0360w
R03220
O2[c] +
Coproporphyrinogen-III[c] <=>
2.000000 H2O[c] +
2.000000 CO2[c] +
Protoporphyrinogen-IX[c] 1.3.3.3 PF11_0436
R03222
3.000000 O2[c] + 2.000000
Protoporphyrinogen-IX[c] <=>
6.000000 H2O[c] +
2.000000 Protoporphyrin[c] 1.3.3.4 PF10_0275
90
R03223
4-Methyl-5--2-
phosphoethyl--thiazole[c] +
2-Methyl-4-amino-5-
hydroxymethylpyrimidine-
di[c] <=> PPi[c] + Thiamin-monoP[c] 2.5.1.3 PFF0680c
R03269
R--4'-
Phosphopantothenoyl-L-
cysteine[c] <=> CO2[c] + Pantetheine-4'-P[c] 4.1.1.36 MAL8P1.81
R03361
ATP[c] + 1-Phosphatidyl-
D-myo-inositol[c] <=>
ADP[c] + 1-Phosphatidyl-
1D-myo-inositol-4-P[c] 2.7.1.67
PFC0475c,PFD0965W,P
FE0485w
R03362
ATP[c] + 1-Phosphatidyl-
D-myo-inositol[c] <=>
ADP[c] + 1-Phosphatidyl-
1D-myo-inositol-3-P[c] 2.7.1.137 PFC0475c,PFE0765w
R03393
H2O[c] + 1D-myo-Inositol-
1,4-bisP[c] <=> Pi[c] + myo-Inositol-4-P[c] 3.1.3.57
R03394
H2O[c] + D-myo-Inositol-
1,4,5-trisP[c] <=>
Pi[c] + 1D-myo-Inositol-1,4-
bisP[c] 3.1.3.56
PF11_0122,PF07_0024,P
F13_0285,MAL8P1.151
R03416
H2O[c] + 1-Acyl-sn-
glycero-3-
phosphoethanolamine[c] <=>
Fatty-acid[c] + sn-glycero-3-
Phosphoethanolamine[c] 3.1.1.5
PF07_0005,PF10_0018,P
FL2530w,PF14_0737,PF
I1800w,MAL7P1.178,PF
07_0040,PF10_0379,PF1
4_0738,PFI1775w
R03435
H2O[c] + 1-Phosphatidyl-
D-myo-inositol-4,5-bisP[c] <=>
1,2-Diacyl-sn-glycerol[c] +
D-myo-Inositol-1,4,5-
trisP[c] 3.1.4.11 PF10_0132
R03460
Phosphoenolpyruvate[c] +
Shikimate-3-P[c] <=>
Pi[c] + 5-O--1-
Carboxyvinyl--3-
phosphoshikimate[c] 2.5.1.19 PFB0280w
R03469
ATP[c] + 1-Phosphatidyl-
1D-myo-inositol-4-P[c] <=>
ADP[c] + 1-Phosphatidyl-D-
myo-inositol-4,5-bisP[c] 2.7.1.68
PF10_0306,PFA0515w,P
F11_0307
R03471
ATP[c] + 4-Amino-5-
hydroxymethyl-2-
methylpyrimidine[c] <=>
ADP[c] + 4-Amino-2-
methyl-5-
phosphomethylpyrimidine[c] 2.7.1.49 PFE1030c
R03503
ATP[c] + 2-Amino-4-
hydroxy-6-hydroxymethyl-
7,8-dihydro[c] <=>
AMP[c] + 2-Amino-7,8-
dihydro-4-hydroxy-6--
diphosphooxy[c] 2.7.6.3 PF08_0095
R03595
H2O[c] + ATP[c] +
Selenide[c] <=> Pi[c] + AMP[c] + SelenoP[c] 2.7.9.3 PFI0505c
R03646 ATP[c] + L-Arginine[c] <=>
PPi[c] + AMP[c] + L-
Arginyl-tRNA-Arg-[c] 6.1.1.19 PFI0680c,PFL0900c
91
R03648 ATP[c] + L-Asparagine[c] <=>
PPi[c] + AMP[c] + L-
Asparaginyl-tRNA-Asn-[c] 6.1.1.22
PFE0715w,PFB0525w,P
FE0475w
R03650 ATP[c] + L-Cysteine[c] <=>
PPi[c] + AMP[c] + L-
Cysteinyl-tRNA-Cys-[c] 6.1.1.16 PF10_0149
R03651 ATP[c] + L-Glutamate[c] <=>
PPi[c] + AMP[c] + L-
Glutamyl-tRNA-Gln-[c] 6.1.1.24
R03652 ATP[c] + L-Glutamine[c] <=>
PPi[c] + AMP[c] +
Glutaminyl-tRNA[c] 6.1.1.18 PF13_0170
R03654 ATP[c] + Glycine[c] <=>
PPi[c] + AMP[c] + Glycyl-
tRNA-Gly-[c] 6.1.1.14 PF14_0198
R03655 ATP[c] + L-Histidine[c] <=>
PPi[c] + AMP[c] + L-
Histidyl-tRNA-His-[c] 6.1.1.21 PF14_0428,PFI1645c
R03656 ATP[c] + L-Isoleucine[c] <=>
PPi[c] + AMP[c] + L-
Isoleucyl-tRNA-Ile-[c] 6.1.1.5 PF13_0179,PFL1210w
R03657 ATP[c] + L-Leucine[c] <=>
PPi[c] + AMP[c] + L-
Leucyl-tRNA[c] 6.1.1.4 PF08_0011,PFF1095w
R03658 ATP[c] + L-Lysine[c] <=>
PPi[c] + AMP[c] + L-Lysyl-
tRNA[c] 6.1.1.6 PF13_0262,PF14_0166
R03659 ATP[c] + L-Methionine[c] <=>
PPi[c] + AMP[c] + L-
Methionyl-tRNA[c] 6.1.1.10
PF14_0401,PF10_0053,P
F10_0340
R03660
ATP[c] + L-
Phenylalanine[c] <=>
PPi[c] + AMP[c] + L-
Phenylalanyl-tRNA-Phe-[c] 6.1.1.20
PF11_0051,PFA0480w,P
FF0180w,PFL1540c
R03661 ATP[c] + L-Proline[c] <=>
PPi[c] + AMP[c] + L-Prolyl-
tRNA-Pro-[c] 6.1.1.15 PFI1240c,PFL0670c
R03662 ATP[c] + L-Serine[c] <=>
PPi[c] + AMP[c] + L-Seryl-
tRNA-Ser-[c] 6.1.1.11 PF07_0073,PFL0770w
R03663 ATP[c] + L-Threonine[c] <=>
PPi[c] + AMP[c] + L-
Threonyl-tRNA-Thr-[c] 6.1.1.3 PF11_0270
R03664 ATP[c] + L-Tryptophan[c] <=>
PPi[c] + AMP[c] + L-
Tryptophanyl-tRNA-Trp-[c] 6.1.1.2 PF13_0205,PFL2485c
R03665 ATP[c] + L-Valine[c] <=>
PPi[c] + AMP[c] + L-Valyl-
tRNA-Val-[c] 6.1.1.9 PFC0470w,PF14_0589
R03813 L-Leucyl-tRNA[c] <=>
tRNA[c] + L-Leucyl-
protein[c] 2.3.2.6 PFB0585w
R03905
H2O[c] + ATP[c] + L-
Glutamine[c] + L-
Glutamyl-tRNA-Gln-[c] <=>
ADP[c] + Pi[c] + L-
Glutamate[c] + Glutaminyl-
tRNA[c] 6.3.5.7 PFF1395c,PFD0780w
R03940
10-
Formyltetrahydrofolate[c] +
L-Methionyl-tRNA[c] <=>
Tetrahydrofolate[c] + N-
Formylmethionyl-tRNA[c] 2.1.2.9 MAL13P1.67
92
R04058 L-Glutaminyl-peptide[c] <=>
NH3[c] + 5-Oxoprolyl-
peptide[c] 2.3.2.5 PF14_0447
R04142
NADH[c] + CO2[c] + H[c]
+ 5-
Acetamidopentanoate[c] <=>
H2O[c] + NAD[c] + 2-Oxo-
6-acetamidocaproate[c] 1.2.4.-
R04216
Dolichyl-
diphosphooligosaccharide[c
] <=>
Dolichyl-diP[c] + N-
glycan[c] 2.4.1.119 PFI0960w,PF11_0173
R04231
CTP[c] + L-Cysteine[c] +
D-4'-
Phosphopantothenate[c] <=>
PPi[c] + CMP[c] + R--4'-
Phosphopantothenoyl-L-
cysteine[c] 6.3.2.5 PF11_0036,PFD0610w
R04286
2-Amino-4-hydroxy-6--
erythro-1,2,3-trihydroxy[c] <=>
TriP[c] + 6-
Pyruvoyltetrahydropterin[c] 4.2.3.12 PFF1360w
R04448
ATP[c] + 5--2-
Hydroxyethyl--4-
methylthiazole[c] <=>
ADP[c] + 4-Methyl-5--2-
phosphoethyl--thiazole[c] 2.7.1.50 PFL1920c,PFF1335c
R04496
S-Adenosyl-L-
methionine[c] + Protein-C-
terminal-S-farnesyl-L-
cysteine[c] <=>
S-Adenosyl-L-
homocysteine[c] + Protein-
C-terminal-S-farnesyl-L-
cysteine-meth[c] 2.1.1.100 PFL1780w
R04779
ATP[c] + beta-D-Fructose-
6-P[c] <=>
ADP[c] + beta-D-Fructose-
1,6-bisP[c] 2.7.1.11 PFI0755c,PF11_0294
R04986
3-Octaprenyl-4-
hydroxybenzoate[c] <=>
CO2[c] + 2-
Octaprenylphenol[c] 4.1.1.-
R05553
4-Amino-4-
deoxychorismate[c] <=>
Pyruvate[c] + 4-
Aminobenzoate[c] 4.1.3.38 PFI1100w
R05556
Isopentenyl-diP[c] +
trans,trans,cis-
Geranylgeranyl-diP[c] <=>
PPi[c] + Dehydrodolichyl-
diphosphate[c] 2.5.1.- PFF0370w
R05577 ATP[c] + L-Aspartate[c] <=>
PPi[c] + AMP[c] + L-
Aspartyl-tRNA-Asp-[c] 6.1.1.12 PFA0145c
R05578 ATP[c] + L-Glutamate[c] <=>
PPi[c] + AMP[c] + L-
Glutamyl-tRNA-Glu-[c] 6.1.1.17
PF13_0170,PF13_0257,
MAL13P1.281
R05612
Isopentenyl-diP[c] + all-
trans-Hexaprenyl-diP[c] <=>
PPi[c] + all-trans-
Heptaprenyl-diP[c] 2.5.1.30 PFB0130w
R05613
Isopentenyl-diP[c] + all-
trans-Pentaprenyl-diP[c] <=>
PPi[c] + all-trans-
Hexaprenyl-diP[c] 2.5.1.33 PFB0130w
R05692
NADP[c] + GDP-L-
fucose[c] <=>
NADPH[c] + H[c] + GDP-4-
dehydro-6-deoxy-D-
mannose[c] 1.1.1.271 PF10_0137
93
R05800
ATP[c] + D-myo-Inositol-
1,4,5-trisP[c] <=>
ADP[c] + 1D-myo-Inositol-
1,4,5,6-tetrakisP[c] 2.7.1.151 PF13_0089
R05801
ATP[c] + 1D-myo-Inositol-
1,4,5,6-tetrakisP[c] <=>
ADP[c] + 1D-myo-Inositol-
1,3,4,5,6-pentakisP[c] 2.7.1.151 PF13_0089
R06517
Acyl-CoA[c] +
Sphinganine[c] <=>
CoA[c] +
Dihydroceramide[c] 2.3.1.24 PF14_0034,PFE0405c
R06519
Reduced-Acceptor[c] +
O2[c] +
Dihydroceramide[c] <=>
Acceptor[c] + 2.000000
H2O[c] + N-
Acylsphingosine[c] 1.14.-.-
R06868
S-Adenosyl-L-
methionine[c] + N-
Methylethanolamine-P[c] <=>
S-Adenosyl-L-
homocysteine[c] +
Phosphodimethylethanolami
ne[c] 2.1.1.103 MAL13P1.214
R06869
S-Adenosyl-L-
methionine[c] +
Phosphodimethylethanolam
ine[c] <=>
S-Adenosyl-L-
homocysteine[c] + Choline-
P[c] 2.1.1.103 MAL13P1.214
R07168
NAD[c] + 5-
Methyltetrahydrofolate[c] <=>
NADH[c] + H[c] + 5,10-
Methylenetetrahydrofolate[c] 1.5.1.20
R07267
Isopentenyl-diP[c] + all-
trans-Octaprenyl-diP[c] <=>
PPi[c] + all-trans-
Nonaprenyl-diP[c] 2.5.1.11 PFB0130w
R07269
Isopentenyl-diP[c] +
trans,trans-Farnesyl-diP[c] <=>
PPi[c] + di-trans,poly-cis-
Undecaprenyl-diP[c] 2.5.1.31 MAL8P1.22
R08193
N-Acetyl-D-glucosamine-
6-P[c] <=>
N-Acetyl-alpha-D-
glucosamine-1-P[c] 5.4.2.3 PF11_0311
R08218 ATP[c] + L-Serine[c] <=>
PPi[c] + AMP[c] + L-Seryl-
tRNA-Sec-[c] 6.1.1.11 PF07_0073,PFL0770w
R08219
SelenoP[c] + L-Seryl-
tRNA-Sec-[c] <=>
Pi[c] + L-Selenocysteinyl-
tRNA-Sec-[c] 2.9.1.1
R08575
2R--2-Hydroxy-3--
phosphonooxy--propanal[c]
+ D-Sedoheptulose-7-P[c] <=>
D-Fructose-6-P[c] + D-
Erythrose-4-P[c] 2.2.1.2
Rargn1
S--1-Pyrroline-5-
carboxylate[c] <=>
L-Glutamate-5-
semialdehyde[c] 1.5.1.12
Rasug1
Dolichyl-P-D-mannose[c] +
N-Acetyl-D-
glucosaminyldiphosphodoli
chol[c] <=>
Dolichyl-
diphosphooligosaccharide[c]
94
Rfolt1
6-
Pyruvoyltetrahydropterin[c] <=>
2-Amino-4-hydroxy-6-
hydroxymethyl-7,8-
dihydro[c]
Rglyc1 GDP-L-fucose[c] <=> glyc-fru[c] + GDP[c]
Rglyc2 GDP-mannose[c] <=> GDP[c] + glyc-man[c]
Rglyc3 UDP-glucose[c] <=> glyc-glu[c] + UDP[c]
Rlysn1 Cadaverine[c] <=> Piperideine[c]
Rlysn2 Piperideine[c] <=> 5-Aminopentanoate[c]
Rmeth1
S-Adenosyl-L-
methionine[c] <=>
acceptor-Ch3[c] + S-
Adenosyl-L-homocysteine[c] 2.1.1.-
PF11_0305,PFE1275c,PF
L2395c,PFD0350w,PFB0
220w,PFB0855c,PF14_0
273,PF13_0286,PFE1115
c,PFF1070c,PFF1085c
Rnitr1
H2O[c] + 3.000000
NAD[c] + NH3[c] <=>
3.000000 NADH[c] +
3.000000 H[c] + Nitrite[c] 1.7.1.4 PF07_0085
Rpyrm1 UTP[c] + Thioredoxin[c] <=>
Thioredoxin-disulfide[c] +
dUTP[c] 1.17.4.2
Rseln1 Selenite[c] <=> Selenide[c] 1.8.1.-
Rthmn1
L-Tyrosine[c] + L-
Cysteine[c] + 1-dD-
Xyulose5P[c] <=>
5--2-Hydroxyethyl--4-
methylthiazole[c] 2.8.1.7
PF07_0068,MAL7P1.150
AND PF13_0344,
PF11_0271, PF13_0182
Rthmn2
1-5P-ribosyl-5-
aminoimidazole[c] <=>
4-Amino-5-hydroxymethyl-
2-methylpyrimidine[c]
Rthmn3
ATP[c] + 4-Amino-2-
methyl-5-
phosphomethylpyrimidine[
c] <=>
ADP[c] + 2-Methyl-4-
amino-5-
hydroxymethylpyrimidine-
di[c] 2.7.1.49 PFE1030c
Rubqu1
L-Glutamine[c] +
Chorismate[c] <=>
Pyruvate[c] + L-
Glutamate[c] + 4-
Hydroxybenzoate[c]
Rubqu2
4-Hydroxybenzoate[c] +
Geranylgeranyl-diP[c] <=>
PPi[c] + 3-Octaprenyl-4-
hydroxybenzoate[c] 2.5.1.39 PFF0370w
95
Rubqu3
3.000000 NADH[c] +
3.000000 O2[c] + 3.000000
S-Adenosyl-L-
methionine[c] + 2-
Octaprenylphenol[c] <=>
3.000000 H2O[c] +
3.000000 NAD[c] + CO2[c]
+ 3.000000 S-Adenosyl-L-
homocysteine[c] +
Ubiquinone[c]
Rxacc1
Acceptor[c] + NADH[c] +
H[c] <=>
Reduced-Acceptor[c] +
NAD[c]
Rxdol1
Dehydrodolichyl-
diphosphate[c] <=> Dolichyl-diP[c]
Rxdol2
Isopentenyl-diP[c] +
trans,trans-Farnesyl-diP[c] <=>
PPi[c] + trans,trans,cis-
Geranylgeranyl-diP[c] 2.5.1.29 PF11_0483
Rxgly1 alpha-D-Glucose-6-P[c] <=> D-Glucose-6-P[c]
Rxins1 myo-Inositol-4-P[c] <=> Inositol-1-P[c]
Rxliv1
NADH[c] + 3-Methyl-2-
oxobutanoic-acid[c] <=>
NAD[c] + 3-Hydroxy-2-
methylpropanoate[c] 1.2.4.4 PF13_0070,PFE0225w
Rxliv2
NADH[c] + 4-Methyl-2-
oxopentanoate[c] <=>
NAD[c] + 3-Hydroxy-2-
methylpropanoate[c]
1.8.1.4 AND
2.3.1.168
PF08_0066,PFL1550w
AND PFC0170c
Rxliv3
NADH[c] + S--3-Methyl-2-
oxopentanoic-acid[c] <=>
NAD[c] + 3-Hydroxy-2-
methylpropanoate[c] 4.2.1.17 AND 3.1.2.4
PF14_0232 AND
PFL1940w
Rxprp1
L-Glutamine[c] +
Glycerone-P[c] + D-
Ribulose-5-P[c] <=>
Pyridoxal-P[c] + L-
Glutamate[c] + D-Ribose-5-
P[c] + 2R--2-Hydroxy-3--
phosphonooxy--propanal[c]
Rxprp2 H2O[c] + Pyridoxal-P[c] <=> Pi[c] + Pyridoxal[c] 3.1.3.- PF07_0059
Rxprp3 H2O[c] + Pyridoxine-P[c] <=> Pi[c] + Pyridoxine[c] 3.1.3.- PF07_0059
Rxprp4
H2O[c] + Pyridoxamine-
P[c] <=> Pi[c] + Pyridoxamine[c] 3.1.3.- PF07_0059
Rxprp5 Pyridoxamine[c] <=> NH3[c] + Pyridoxal[c] 1.4.3.5 PF14_0570
Rxprp6 Pyridoxine[c] <=> Pyridoxal[c] 1.1.1.65
Rxpyr3 Glycerone-P[c] <=> Methylglyoxal[c]
Rxspm1
Phosphatidylcholine[c] +
N-Acylsphingosine[c] <=>
Sphingomyelin[c] + 1,2-
Diacyl-sn-glycerol[c] 2.7.8.27 PFF1215w
Rxter1 trans,trans-Farnesyl-diP[c] <=>
PPi[c] + Protein-C-terminal-
S-farnesyl-L-cysteine[c] 2.5.1.58 PF11_0483,PFF0120w
96
Rxter2 Geranylgeranyl-diP[c] <=> all-trans-Pentaprenyl-diP[c]
Rxter3
all-trans-Heptaprenyl-
diP[c] <=> all-trans-Octaprenyl-diP[c]
Rxter4 Geranylgeranyl-diP[c] <=> PPi[c] 2.5.1.59 PFF0120w
Rxter5 Geranylgeranyl-diP[c] <=> PPi[c] 2.5.1.60
PF14_0403,PFL0695c,PF
L2050w
Rxxfru beta-D-Fructose-6-P[c] <=> D-Fructose-6-P[c]
Rxxpc1
H2O[c] + 1-Acyl-sn-
glycero-3-
phosphocholine[c] <=>
Fatty-acid[c] + sn-glycero-3-
Phosphocholine[c] 3.1.1.5
PF07_0005,PF10_0018,P
FL2530w,PF14_0737,PF
I1800w,MAL7P1.178,PF
07_0040,PF10_0379,PF1
4_0738,PFI1775w
Rxxps1
ATP[c] + CoA[c] + Fatty-
acid[c] <=>
PPi[c] + AMP[c] + Acyl-
CoA[c] 6.2.1.3
PF14_0761,PFA0455c,P
FI0980w,PF14_0751,PF
B0685c,PFL2570w,PFF0
945c,PF07_0129,PFB069
5c,PFE1250w,PFL0035c,
PFL1880w,MAL13P1.48
5,PFC0050c,PFD0085c,P
FF0290w
RxxxFA Fatty-acid[c] <=> lipoic-acid[c]
Rxxxp1
O2[c] + Acyl-CoA[c] +
2.000000 H[c] + 2.000000
Ferrocytochrome-b5[c] <=>
2.000000 H2O[c] + Acyl-1-
CoA[c] + 2.000000
Ferricytochrome-b5[c] 1.14.19.1 PFE0555w
v_EX_Dipeptides Dipeptides[v] <=> Dipeptides[c] NA
v_EX_H2O H2O[v] <=> H2O[c] NA
v_EX_O2 O2[v] <=> O2[c] NA
v_EX_oxyHb <=> oxyHb[v] NA
v_R00009 2.000000 H2O2[v] <=> O2[v] + 2.000000 H2O[v] 1.11.1.6
v_R1 oxyHb[v] <=> LargePeptides[v]
3.4.23.38 AND
3.4.23.39
PF14_0075,PF14_0076
AND PF14_0077
v_R2 LargePeptides[v] <=> SmallPeptides[v]
3.4.22.- AND
3.4.23.- AND
3.4.24.-
PFB0335c,PF11_0161,P
F11_0162,PF11_0165,PF
B0340c AND
PF13_0133,PF14_0078
AND PF13_0322
v_R3 SmallPeptides[v] <=> Dipeptides[v] 3.4.14.1 PFL2290w
97
Appendix II: Biomass equation The biomass equation is an approximation of the chemical composition of Plasmodium falciparum. Its purpose
is to serve as a sink for metabolites essential for growth and is the objective function that is maximized in FBA
simulations [24].
Where relevant data for Plasmodium could not be found (eg. overall cellular macromolecule compositions, and
ATP maintenance requirements), values from Leishmania major were used as approximations (Chavali et al.,
2008). This was deemed acceptable because FBA growth predictions have been shown to be generally
insensitive to slight variations to coefficients in the biomass equation (Varma and Palsson, 1993, 1994, 1995).
However, this data would need to be generated for P. falciparum before further studies that examine
quantitative predictions of parasitic growth rates can be undertaken.
The detailed derivation of the biomass equation used in this analysis is shown below.
Macromolecular composition
The overall macromolecular composition of P. falciparum was assumed to resemble that of L. major (Chavali et
al., 2008).
Component % Dry Weight
Protein 45
DNA 2
RNA 10
Lipids 23
Carbohydrates and GPI 20
Protein
The contribution of amino acids to biomass was determined by counting the number of each amino acid present
in the protein sequence associated with every ORF from the P. falciparum genome (PlasmoDB version 5.5).
The percentage prevalence for each amino acid was determined by dividing the count of each amino acid by the
number of total amino acids and multiplying by 100. Percentage prevalence was converted to mmol/gDW
using the following template equation:
Amino acid contribution to biomass (mmol/gDW) =
[Prevalence * MW / Sum of (Prevalence*MW)] / 100 * 0.45/MW * 1000
Amino Acid MW
(g/mol) # of AA % Prev %Prev * MW % Weight mmol/gDWcell
Alanine (A) 89.05 80544 1.971 1.755 1.292 0.0653
Arginine (R) 175.11 108239 2.649 4.638 3.414 0.0877
Asparagine (N) 132.05 587256 14.372 18.978 13.966 0.4759
Aspartic acid (D) 132.04 264513 6.473 8.547 6.290 0.2144
Cysteine (C) 121.02 72386 1.771 2.144 1.578 0.0587
Glutamate (E) 146.05 291432 7.132 10.416 7.666 0.2362
Glutamine (Q) 146.07 112851 2.762 4.034 2.969 0.0915
Glycine (G) 75.03 115824 2.835 2.127 1.565 0.0939
98
Histidine (H) 155.07 99272 2.429 3.767 2.772 0.0805
Isoleucine (I) 131.09 377891 9.248 12.123 8.922 0.3063
Leucine (L) 131.09 309131 7.565 9.917 7.298 0.2505
Lysine (K) 147.11 479527 11.735 17.264 12.705 0.3886
Methionine (M) 149.05 89801 2.198 3.276 2.411 0.0728
Phenylalanine (F) 165.08 178120 4.359 7.196 5.296 0.1444
Proline (P) 115.06 81285 1.989 2.289 1.684 0.0659
Serine (S) 105.04 260869 6.384 6.706 4.935 0.2114
Threonine (T) 119.06 167383 4.096 4.877 3.589 0.1357
Tryptophan (W) 204.09 20242 0.495 1.011 0.744 0.0164
Tyrosine (Y) 181.07 233413 5.712 10.343 7.612 0.1892
Valine (V) 117.08 156228 3.823 4.476 3.294 0.1266
Total: 4086207 100 1.359
DNA
The percent prevalence of DNA nucleotides was determined assuming a G+C content of 19.4% (Carlton J et al.,
2004), and then applying the same equation (using the factor 0.02 instead of 0.45 to reflect DNA composition).
DNA MW (g/mol) % Prevalence % Prev * MW % Weight mmol/gDWcell
dAMP 329.07 40 131.63 40.54 0.025
dCMP 305.06 10 30.51 9.4 0.006
dGMP 345.06 10 34.51 10.63 0.006
dTMP 320.06 40 128.02 39.43 0.025
Total: 100 324.66
RNA
The contribution of RNA monomers was determined in following the same procedure as with amino acids, but
using the DNA sequence associated with every ORF from the P. falciparum genome.
RNA MW (g/mol) Abundance % Prevalence %Prev * MW % Weight mmol/gDWcell
AMP 345.06 5543029 45.15 155.79 46.35 0.1343
CMP 321.05 1717680 13.99 44.92 13.36 0.0416
GMP 361.06 1201554 9.79 35.34 10.51 0.0291
UMP 322.04 3815373 31.08 100.08 29.77 0.0925
Total: 12277636 100 3.361
Lipid An average molecular weight of phospholipids was calculated based on % PC, PE, SM, PS, and PI obtained
from malaria literature (Vial and Ancelin, 2000), by taking the sum of individual MW multiplied by percent
prevalence.
Lipid Component MW (g/mol) % Prevalence % Prev*MW
PC 776.19 47.00 364.809
PE 734.11 41.00 300.985
SM 492.97 2.00 9.859
99
PS 385.3 2.00 7.706
PI 852.18 8.00 68.174
Lipid Component
MW (g/mol)
Cholesterol 386.35
Phospholipid (PL) 751.53
Based on a given cholesterol/PL mole ratio of 0.1 (Vial and Ancelin, 2000), an equivalent mass ratio was
determined:
Mass ratio = (mol Chol/mol PL) *( MW PL/MW Chol) = 0.195, which equals a percent weight of 16.28%
cholesterol and 83.72% PL.
Lipid Component % Weight
Cholesterol 16.28
Phospholipid (PL) 83.72
Phospholipid and cholesterol contribution to biomass was calculated using the following equation:
(Prev/MW)* (%weight /100)* 0.23*1000
mmol/gDWcell
Cholesterol 0.0969
Phospholipid (PL)
PC 0.1166
PE 0.1075
SM 0.0078
PS 0.0100
PI 0.0181
Carbohydrates All carbohydrate content in P. falciparum was assumed to be equally divided in the form of protein
glycosylation and protein methylation/acetylation. It has been approximated that 95% of glycosylation is in the
form of GPI anchors and 5% other N-glycan chains (Sherman, 2000). Additional demand of N-glycan
precursors was represented by inclusion of GDP-mannose and GDP-fucose with a minimal coefficient of 0.001
mmol/gDW. Methylation and acetylation demands are represented through donation to hypothetical receptors;
methylated-acceptor and acetylated-acceptor.
Carbohydrate MW (g/mol) % Weight mmol/gDWcell
GPI anchor 2147 47.5 0.0442
N-glycan 2062 2.5 0.0024
Methylation 15.01 25 3.3311
Acetylation 43.04 25 1.1617
Maintenance Energy ATP requirement for maintenance for P. falciparum was taken to be equal to L. major (Chavali et al., 2008).
Metabolite mmol/gDWcell
ATP 32.26
ADP -32.26
Pi -32.26
100
Cofactors Cofactors were added to the biomass equation with an assumed coefficient of 0.001 mmol/gDW, which was
high enough to observe flux through the associated reaction pathways but low enough so that it did not
significantly affect biomass production (Jamshidi and Palsson, 2007) This demand for cofactors was required
in order to compare computational enzyme deletion studies to experimental drug targets, since many of these lie
in cofactor synthesis pathways.
Final Biomass Equation
Reactants:
0.0653 L-Alanyl-tRNA
0.0877 L-Arginyl-tRNA
0.4759 L-Asparaginyl-tRNA
0.2144 L-Aspartyl-tRNA
0.0587 L-Cysteinyl-tRNA
0.2362 L-Glutamyl-tRNA
0.0915 L_Glutaminyl-tRNA
0.0939 Glycyl-tRNA
0.0805 L-Histidyl-tRNA
0.3063 L-Isoleucyl-tRNA
0.2505 L-Leucyl-tRNA
0.3886 L-Lysyl-tRNA
0.0728 L-Methionyl-tRNA
0.1444 L-Phenylalanyl-tRNA
0.0659 L-Prolyl-tRNA
0.2114 L-Seryl-tRNA
0.1357 L-Threonyl-tRNA
0.0164 L-Tryptophanyl-tRNA
0.1892 L-Tyrosyl-tRNA
0.1266 L-Valyl-tRNA
0.1166 Phosphatidylcholine
0.1075 Phosphatidylethanolamine
0.0181 1-Phosphatidyl-D-myo-inositol
0.0078 Sphingomyelin
0.01 Phosphatidylserine
0.01 Phosphatidate
0.0422 GPI
0.001 GDP-fucose
0.001 GDP-mannose
0.024 N-glycan
3.3311 methylated-acceptor
1.1617 acetylated-acceptor
0.025 dATP
0.006 dCTP
0.006 dGTP
101
0.025 dTTP
0.1343 ATP
0.0416 CTP
0.0291 GTP
0.0925 UTP
0.001 CoA
0.001 Heme
0.001 Dihydrofolate
0.001 Tetrahydrofolate
0.001 5,10-Methylenetetrahydrofolate
0.001 5-Methyltetrahydrofolate
0.001 NAD
0.001 NADP
0.001 Pyridoxal phosphate
0.001 FAD
0.001 4-Amino-4-deoxychorismate
0.001 Thiamin diphophate
0.001 Ubiquinone
0.001 lipoyl-E2_apicoplast
0.001 lipoyl-E2_mitochondria
0.001 Glutathione
32.26 ATP
Products:
32.26 ADP
32.26 Pi
References
Carlton J, Silva J, Hall N (2004) The Genome of Model Malaria Parasites, and Comparative Genomics. In
Malaria Parasites: Genomes and Molecular Biology, Waters AP and Janse CJ (ed) pp 76. Netherlands: Caister
Academic Press
Chavali AK, Whittemore JD, Eddy JA, Williams KT, Papin JA (2008) Systems analysis of metabolism in the
pathogenic trypanosomatid Leishmania major. Mol Syst Biol 4: 177
Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv
using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 1: 26
Sherman IW (2000) Carbohydrate Metabolism of Asexual Stages. In Malaria: Parasite Biology, Pathogenesis, &
Protection, Sherman W (ed) pp 141. Riverside: American Society Microbiolgy
Thiele I, Palsson BO (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction.
Nat Protoc 5: 93-121
102
Varma A, Palsson BO (1993) Metabolic capabilities of Escherichia coli
0.2. Optimal-growth patterns. J Theor Biol 165: 503–522
Varma A, Palsson BO (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic
by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 60: 3724–3731
Varma A, Palsson BO (1995) Parametric sensitivity of stoichiometric flux balance models applied to wild-type
Escherichia coli metabolism. Biotechnol Bioeng 45: 69–79
Vial HJ, Ancelin ML (2000) Malarial Lipids. In Malaria: Parasite Biology, Pathogenesis, & Protection,
Sherman W (ed) pp 159-165. Riverside: American Society Microbiolgy
103
Appendix III: Nutrient simulation environments
The simulation environments are shown below in the figure below. As described in Section 2.4, the defined
culture environment included nutrients that are found in a culture medium based on RPMI-1640 and a
hypoxanthine purine source as used in previous experimental studies (Lingau et al., 1994). The serum
environment additionally includes those nutrients annotated by MPMP to be accessible to P. falciparum in vivo.
Serum Nutrients
Defined culture nutrients
Carbon source
alpha-D-Glucose
Purines
Hypoxanthine
Amino acids
All 20 amino acids
Micronutrients
1-Phosphatidyl-D-myo-inositol
Choline
Pantothenate
4-Aminobenzoate
Nicotinamide
Riboflavin
Thiamin
Other small molecules
HCO3-
Pi
O2
Nitrate
Purines Adenosine
Amino acids Hb
(no isoleucine)
Micronutrients Ethanolamine
Homocysteine
Nicotinate
Putrescine
Selenite
Spermidine
Urea Toxopyrimidine
Other small molecules NH3
Nitrite
Lipids Fatty_acid
1,2-Diacyl-sn-glycerol
Phosphatidylcholine
Phosphatidylethanolamine
Phosphatidylserine
Glycerol
Sterol
104
Appendix IV: Predicted essential enzymes and drug target annotation datasets
iMPMP427 lethal single enzymes
1.1.1.102 2.4.1.119 3.3.1.1 6.1.1.19
1.1.1.205 2.4.1.198 3.5.1.89 6.1.1.2
1.1.1.25 2.4.1.83 3.5.2.3 6.1.1.20
1.1.1.267 2.4.2.10 3.5.4.16 6.1.1.21
1.1.1.27 2.4.2.11 3.6.1.1 6.1.1.22
1.1.1.271 2.4.2.8 3.6.1.23 6.1.1.3
1.1.1.42 2.5.1.- 3.6.1.25 6.1.1.4
1.1.1.44 2.5.1.1 3.6.1.43 6.1.1.5
1.1.1.49 2.5.1.10 4.1.1.- 6.1.1.6
1.1.99.16 2.5.1.15 4.1.1.23 6.1.1.7
1.10.2.2 2.5.1.19 4.1.1.36 6.1.1.9
1.14.-.- 2.5.1.29 4.1.1.37 6.2.1.3
1.17.1.2 2.5.1.39 4.2.1.1 6.3.2.12
1.17.4.1 2.5.1.54 4.2.1.10 6.3.2.2
1.17.4.3 2.5.1.6 4.2.1.2 6.3.2.3
1.18.1.2 2.5.1.61 4.2.1.24 6.3.2.5
1.2.1.59 2.6.1.16 4.2.1.3 6.3.4.14
1.2.4.1 2.7.1.1 4.2.1.47 6.3.4.2
1.2.4.2 2.7.1.148 4.2.1.75 6.3.4.4
1.3.3.1 2.7.1.23 4.2.3.12 6.3.5.1
1.3.3.3 2.7.1.24 4.2.3.4 6.3.5.2
1.3.3.4 2.7.1.26 4.2.3.5 6.4.1.2
1.5.1.3 2.7.1.33 4.3.2.2 2.4.1.-
1.8.1.4 2.7.1.40 4.6.1.12 2.4.1.30
1.8.1.9 2.7.1.71 4.99.1.1 2.3.1.48
1.9.3.1 2.7.4.14 5.1.3.1 2.6.1.85
2.1.1.- 2.7.4.3 5.3.1.1 2.3.1.181
2.1.1.45 2.7.4.6 5.3.1.6 2.8.1.8
2.1.3.2 2.7.4.8 5.3.1.8 2.7.7.63
2.2.1.1 2.7.4.9 5.3.1.9
2.2.1.2 2.7.6.1 5.4.2.2
2.2.1.7 2.7.6.3 5.4.2.3
2.3.1.12 2.7.7.13 5.4.2.8
2.3.1.24 2.7.7.18 6.1.1.1
2.3.1.37 2.7.7.2 6.1.1.10
2.3.1.4 2.7.7.23 6.1.1.11
2.3.1.50 2.7.7.3 6.1.1.12
2.3.1.61 2.7.7.60 6.1.1.14
2.3.1.85 2.7.7.9 6.1.1.15
2.3.3.1 2.7.8.15 6.1.1.16
3.1.1.31 6.1.1.17
105
iMPMP427 lethal double enzymes (EC1 and EC2)
Only non-trivial cases are shown (pairs that don‟t involve any independently essential genes)
1.2.1.12 and 2.7.2.3 2.7.2.3 and 3.4.14.1
1.2.1.12 and 4.2.1.11 2.7.4.16 and 2.7.6.2
1.2.1.12 and 5.4.2.1 4.1.1.49 and 4.2.1.11
1.3.99.1 and 2.7.2.3 4.1.1.49 and 5.4.2.1
1.3.99.1 and 4.2.1.11 4.2.1.11 and 6.2.1.4
1.3.99.1 and 5.4.2.1 4.2.1.11 and 3.4.23.38
2.1.1.13 and 1.5.1.20 4.2.1.11 and 3.4.23.39
2.3.1.15 and 2.7.1.107 4.2.1.11 and 3.4.22.-
2.3.1.51 and 2.7.1.107 4.2.1.11 and 3.4.23.-
2.3.1.88 and 2.7.2.3 4.2.1.11 and 3.4.24.-
2.3.1.88 and 4.2.1.11 4.2.1.11 and 3.4.14.1
2.3.1.88 and 5.4.2.1 5.4.2.1 and 6.2.1.4
2.5.1.3 and 2.7.6.2 5.4.2.1 and 3.4.23.38
2.7.1.49 and 2.7.6.2 5.4.2.1 and 3.4.23.39
2.7.1.50 and 2.7.6.2 5.4.2.1 and 3.4.22.-
2.7.2.3 and 4.1.1.49 5.4.2.1 and 3.4.23.-
2.7.2.3 and 6.2.1.4 5.4.2.1 and 3.4.24.-
2.7.2.3 and 3.4.23.38 5.4.2.1 and 3.4.14.1
2.7.2.3 and 3.4.23.39 6.1.1.18 and 6.1.1.24
2.7.2.3 and 3.4.22.- 6.1.1.18 and 6.3.5.7
2.7.2.3 and 3.4.23.- 6.2.1.1 and 6.2.1.13
2.7.2.3 and 3.4.24.- 6.3.4.16 and 6.3.5.5
106
MPMP annotations
Obtained from http://sites.huji.ac.il/malaria/
1.1.1.205 3.4.11.1
1.1.1.267 3.4.11.18
1.1.1.8 3.4.11.2
1.10.2.2 3.4.11.21
1.3.3.1 3.4.11.9
1.5.1.3 3.4.21.62
1.9.3.1 3.4.22.-
2.3.1.24 3.4.22.1
2.3.1.50 3.4.23.-
2.4.1.80 3.4.23.38
2.4.2.1 3.4.23.39
2.4.2.8 3.4.24.-
2.5.1.15 3.5.4.4
2.5.1.19 4.1.1.17
2.6.1.85 6.3.4.4
2.7.8.3 6.4.1.2
3.1.4.12
Fatumo annotations
Obtained from Fatumo et al (2006), Table 1
1.1.1.205 2.7.1.32
1.17.4.1 3.1.3.56
1.2.4.4 3.1.4.12
1.3.3.1 3.1.4.17
1.3.99.1 3.3.1.1
1.5.1.3 3.5.4.4
1.6.5.3 3.6.1.17
2.1.1.100 4.1.1.17
2.1.1.45 4.1.1.23
2.1.1.64 4.1.1.50
2.3.1.15 4.1.2.13
2.3.1.41 4.2.1.24
2.4.2.1 4.2.3.5
2.4.2.8 4.4.1.5
2.5.1.15 6.1.1.3
2.5.1.16 6.1.1.7
2.5.1.18 6.3.2.2
2.5.1.19 6.3.5.5
2.5.1.21 6.4.1.2
107
Appendix V: Classification of annotated drug target discrepancies Discrepancies between annotated potential metabolic drug targets and iMPMP427 predictions of essential
enzymes are classified into categories similar to previous approaches [12]. An enzyme discrepancy designed as
„Alternate pathway‟ signifies that the model contains another pathway to make the required biomass metabolites,
thus enzyme inhibition in silico is not lethal; „Produce non-biomass component‟ signifies that enzyme involved
in the pathway that leads to production of a metabolite not included in the biomass reaction, thus enzyme
inhibition is not lethal in silico; „Blocked reaction‟ also signifies that the enzyme leads to production of
metabolite not present in the biomass reaction but additionally that the pathway is not connected to the rest of
the network.
Fatumo discrepancies
1.2.4.4 Blocked reaction
1.3.99.1 Alternate pathway
1.6.5.3 Produce non-biomass metabolite
2.1.1.100 Blocked reaction
2.1.1.64 Not in model
2.3.1.15 Alternate pathway
2.3.1.41 Not in model
2.5.1.16 Blocked reaction
2.5.1.18 Alternate pathway
2.5.1.21 Not in model
2.7.1.32 Alternate pathway
3.1.3.56 Alternate pathway
3.1.4.17 Produce non-biomass metabolite
3.6.1.17 Blocked reaction
4.1.1.50 Blocked reaction
4.1.2.13 Alternate pathway
4.4.1.5 Blocked reaction
6.3.5.5 Alternate pathway
MPMP discrepancies
1.1.1.8 Alternate pathway
2.4.1.80 Blocked reaction
2.7.8.3 Alternate pathway
3.4.11.1 Alternate pathway
3.4.11.18 Alternate pathway
3.4.11.2 Alternate pathway
3.4.11.21 Alternate pathway
3.4.11.9 Alternate pathway
3.4.21.62 Alternate pathway
3.4.22.- Alternate pathway
3.4.22.1 Alternate pathway
3.4.23.- Alternate pathway
3.4.23.38 Alternate pathway
3.4.23.39 Alternate pathway
