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Course Name:
Systems Biology IConducted by-
Shigehiko kanaya
&
Md. Altaf-Ul-Amin
Dates of Lectures:April: 7, 14, 21, 28
May: 12, 19, 26
June: 2
Lecture Time: Tuesdays 9:20-10:50
Website
http://csblab.naist.jp/library/
SyllabusIntroduction to Graphs/Networks, Different network models, Properties of Protein-Protein Interaction Networks, Different centrality measuresProtein Function prediction using network concepts, Application of network concepts in DNA sequencing, Line graphs Concept and types of metric, Hierarchical Clustering, Finding clusters in undirected simple graphs: application to protein complex detection Introduction to KNApSAcK database, Metabolic Reaction system as ordinary differential equations, Metabolic Reaction system as stochastic processMetabolic network and stoichiometric matrix, Information contained in stoichiometric matrix, Elementary flux modes and extreme pathwaysGraph spectral analysis/Graph spectral clustering and its application to metabolic networks Normalization procedures for gene expression data, Tests for differential expression of genes, Multiple testing and FDR, Reverse Engineering of genetic networks. Introduction to next generation sequencing.Finding Biclusters in Bipartite Graphs, Properties of transcriptional/gene regulatory networks, Introduction to software package ExpanderIntroduction to signaling pathways, Selected biological processes: Glycolytic oscillations, Sustained oscillation in signaling cascades
Central dogma of molecular biology
The crowded Environment inside the cell
Some of the physical characteristics are as follows:Viscosity > 100 × μ H20Osmotic pressure < 150 atmElectrical gradient ~300000 V/cmNear crystalline state
The osmotic pressure of ocean water is about 27 atm and that of blood is 7.7 atm at 25oC
Source: Systems biology by Bernhard O. Palsson
Without a complicated regulatory system all the processes inside the cell cannot be controlled properly.
Genome (DNAs)Genome (DNAs)
Transcriptome (mRNAs) Transcriptome (mRNAs)
Proteome (peptides) Proteome (peptides)
Metabolome (Metabolites) Metabolome (Metabolites)
Phenome (Phenotype) Phenome (Phenotype)
Big Picture of Hierarchy in Systems Biology
Nucleotide sequences---Double Helix
Bio-chemical molecules
Nucleotide sequences-Single Stranded
Proteins-Amino Acid Sequences
Bioinofomatics
a
b c
d e f g
h i k m
j l
5’
5’3’
3’
A B C D E F G H I J K L MProtein
A B C D EF
G H I JK L MFunctionUnit
Metabolite 1 Metabolite 2 Metabolite 3
Metabolite 4
Metabolite 5
Metabolite 6
B C
D EF
I L
H KMetabolic Pathway
G
Activation (+)A
GRepression (-)
ab c
d e f gh i k m
j l5’
5’3’3’
Genome:
Transcriptome ：
Proteome, Interactome
MetabolomeFT-MS
Integration of omicsto define elements(genome, mRNAs, Proteins, metabolites)
Understanding organism as a system (Systems Biology)
Understanding species-species relations (Survival Strategy)
comprehensive and global analysis of diverse metabolites produced in cells and organisms
Introduction to Graphs/Networks
Representing as a network often helps to understand a system
Konigsberg bridge problem
Konigsberg was a city in present day Germany encompassing two islands and the banks of Pregel River. The city was connected by 7 bridges.
Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?
Konigsberg bridge problem
Konigsberg was a city in present day Germany including two islands and the banks of Pregel River. The city was connected by 7 bridges.
Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?
Konigsberg bridge problem
Konigsberg was a city in present day Germany including two islands and the banks of Pregel River. The city was connected by 7 bridges.
Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?
Konigsberg bridge problem
Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?
This problem was solved by Leonhard Eular in 1736 by means of a graph.
Konigsberg bridge problem
Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?
This problem was solved by Leonhard Eular in 1736 by means of a graph.
A
B
C
D
Konigsberg bridge problem
Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?
A
B
C
D The necessary condition for the existence of the desired route is that each land mass be connected to an even number of bridges.
A, B, C, D circles represent land masses and each line represent a bridge
The graph of Konigsberg bridge problem does not hold the necessary condition and hence there is no solution of the above problem.
This notion has been used in solving DNA sequencing problem
A graph G=(V,E) consists of a set of vertices V={v1, v2,…) and a set of edges E={e1,e2, …..) such that each edge ek is identified by a pair of vertices (vi, vj) which are called end vertices of ek.
A graph is an abstract representation of almost any physical situation involving discrete objects and a relationship between them.
Definition
A
B
C
D
It is immaterial whether the vertices are drawn rectangular or circular or the edges are drawn staright or curved, long or short.
A
B
C
D
Both these graphs are the same
Many systems in nature can be represented as networks
The internet is a network of computers
Very high degree nodeNo such node exists
Road Network Air route Network
Many systems in nature can be represented as networks
Printed circuit boards are networks
Many systems in nature can be represented as networks
Network theory is extensively used to design the wiring and placement of components in electronic circuits
Protein-protein interaction network of e.coli
Many systems in nature can be represented as networks
Some Basic Concepts regarding networks:
•Average Path length
•Diameter
•Eccentricity
•Clustering Coefficient
•Degree distribution
a
db f
e
c
Distance between node u and v called d(u,v) is the least length of a path from u to v.
d(a,e) = ?
Average Path length
a
db f
e
c
Distance between node u and v called d(u,v) is the least distance of a path from u to v.
d(a,e) = ?Length of a-b-c-d-f-e path is 5
Average Path length
a
db f
e
c
Distance between node u and v called d(u,v) is the least distance of a path from u to v.
d(a,e) = ?Length of a-b-c-d-f-e path is 5
Length of a-c-d-f-e path is 4
Average Path length
a
db f
e
c
Distance between node u and v called d(u,v) is the least length of a path from u to v
d(a,e) = ?
Length of a-b-c-d-f-e path is 5
Length of a-c-d-f-e path is 4
Length of a-c-d-e path is 3
The minimum length of a path from a to e is 3 and therefore
d(a,e) = 3.
Average Path length
a
db f
e
cThere are 6 nodes and
6C2 = (6!)/(2!)(4!)=15 distinct pairs for example (a,b), (a,c)…..(e,f).
We have to calculate distance between each of these 15 pairs and average them
Average Path length
Average path length L of a network is defined as the mean distance between all pairs of nodes.
Average Path length
Average path length L of a network is defined as the mean distance between all pairs of nodes.
a to b 1
a to c 1
a to d 2
a to e 3
a to f 3--------------------------------------------____________________15 pairs 27(total length)
L=27/15=1.8
Average path length of most real complex network is small
a
db f
e
c
Finding average path length is not easy when the network is big enough. Even finding shortest path between any two pair is not easy.
A well known algorithm is as follows:
Dijkstra E.W., A note on two problems in connection with Graphs”, Numerische Mathematik, Vol. 1, 1959, 269-271.
Dijkstra’s algorithm can be found in almost every book of graph theory.
There are other algorithms for finding shortest paths between all pairs of nodes.
Average Path length
Diameter
a
db f
e
c
Distance between node u and v called d(u,v) is the least length of a path from u to v.
The longest of the distances between any two node is called Diameter
a to b 1
a to c 1
a to d 2
a to e 3
a to f 3--------------------------------------------15 pairs
Diameter of this graph is 3
Eccentricity And Radius
a
db f
e
c
Eccentricity of a node u is the maximum of the distances of any other node in the graph from u.
The radius of a graph is the minimum of the eccentricity values among all the nodes of the graph.
a to b 1
a to c 1
a to d 2
a to e 3
a to f 3
Therefore eccentricity of node a is 3Radius of this graph is 2
3
3
3
3
2
2
The degree distribution is the probability distribution function P(k), which shows the probability that the degree of a randomly selected node is k.
Degree Distribution
1 2 43
10
# of
nod
es
havi
ng d
egre
e k
Degree Distribution
Degree
1 2 43
1
P(k
)
Degree Distribution
Any randomness in the network will broaden the shape of this peak
Degree
1 2 43
2
4
# of
nod
es
havi
ng d
egre
e k
Degree Distribution
Degree
1 2 43
0.25
0.5
P(k
)
Degree Distribution
Degree
Degree Distribution
( )!
k
P k ek
Poisson’s Distribution
Degree distribution of random graphs follow Poisson’s distribution
e = 2.71828..., the Base of natural Logarithms
Connectivity k
P(k)
P(k) ~ k-γ
Power Law Distribution
Degree distribution of many biological networks follow Power Law distribution
Degree Distribution
Power Law Distribution on log-log plot is a straight line
Clustering coefficient
2
( 1)i
ii i
EC
k k
1
1 N
ii
C CN
ki = # of neighbors of node i
Ei = # of edges among the neighbors of node i
a
db f
e
c
Clustering coefficient
2
( 1)i
ii i
EC
k k
1
1 N
ii
C CN
Ca=2*1/2*1= 1
ki = # of neighbors of node i
Ei = # of edges among the neighbors of node i
a
db f
e
c
Clustering coefficient
2
( 1)i
ii i
EC
k k
1
1 N
ii
C CN
Ca=2*1/2*1= 1
Cb=2*1/2*1= 1
Cc=2*1/3*2= 0.333
Cd=2*1/3*2= 0.333
Ce=2*1/2*1= 1
Cf=2*1/2*1= 1
Total = 4.666
C =4.666/6= 0.7776
ki = # of neighbors of node i
Ei = # of edges among the neighbors of node i
a
db f
e
c
Clustering coefficient
By studying the average clustering C(k) of nodes with a given degree k, information about the actual modular organization can be extracted.
a
db f
e
c
Ca=2*1/2*1= 1
Cb=2*1/2*1= 1
Cc=2*1/3*2= 0.333
Cd=2*1/3*2= 0.333
Ce=2*1/2*1= 1
Cf=2*1/2*1= 1C(1)=0
C(2)=(Ca+Cb+Ce+Cf)/4=1
C(3)=(Cc+Cd)/2=0.333
Clustering coefficient
By studying the average clustering C(k) of nodes with a given degree k, information about the actual modular organization can be extracted.
For most of the known metabolic networks the average clustering follows the power-law.
C(k) ~ k-γ
Power Law Distribution
Subgraphs
Consider a graph G=(V,E). The graph G'=(V',E') is a subgraph of G if V' and E' are respectively subsets of V and E.
a
db f
e
c
a
b
c
df
c
Graph G
Subgraph of G
Subgraph of G
Induced Subgraphs
An induced subgraph on a graph G on a subset S of nodes of G is obtained by taking S and all edges of G having both end-points in S.
a
db f
e
c
a
b
c
df
c
Graph G
Induced subgraph of G for S={a, b, c}
Induced subgraph of G for S={c, d, f}
Graphlets
Graphlets are non-isomprphic induced subgraphs of large networks
T. Milenkovic, J. Lai, and N. Przulj, GraphCrunch: A Tool for Large Network Analyses, BMC Bioinformatics, 9:70, January 30, 2008.
Partial subgraphs/Motifs
A partial subgraph on a graph G on a subset S of nodes of G is obtained by taking S and some of the edges in G having both end-points in S. They are sometimes called edge subgraphs.
a
db f
e
c
a
b
c
Graph G
Partial subgraph of G
For S={a, b, c}
Partial subgraphs/Motifs
SIM MIM FFL
Genomic analysis of regulatory network dynamics reveals large topological changesNicholas M. Luscombe, M. Madan Babu, Haiyuan Yu, Michael Snyder, Sarah
A. Teichmann & Mark Gerstein, NATURE | VOL 431| 2004
SIM=Single input motif
MIM= Multiple input motif
FFL=Feed forward loop
This paper searched for these motifs in transcriptional regulatory network of Saccharomyces cerevisiae
Junker, Björn H., and Falk Schreiber. Analysis of biological networks. Vol. 2. John Wiley & Sons, 2011.
Three node motifs with bi-directional edges found in regulatory network of yeast (Saccharomyces cerevisiae)
Harary, Frank, and Edgar M. Palmer. Graphical enumeration. Elsevier, 2014
Genomic analysis of regulatory network dynamics reveals large topological changesNicholas M. Luscombe, M. Madan Babu, Haiyuan Yu, Michael Snyder, Sarah
A. Teichmann & Mark Gerstein, NATURE | VOL 431| 2004
Partial subgraphs/Motifs
Introduction to Cytoscape
http://www.cytoscape.org/
Data Types in computational biology/Systems biologyUseful websites
What is systems biology?
Each lab/group has its own definition of systems biology.
This is because systems biology requires the understanding and integration of different levels of OMICS information utilizing the knowledge from different branches of science and individual labs/groups are working on different area.
Theoretical target: Understanding life as a system.Practical Targets: Serving humanity by developing new generation medical tests, drugs, foods, fuel, materials, sensors, logic gates……
Understanding life or even a cell as a system is complicated and requires comprehensive analysis of different data types and/or sub-systems.Mostly individual groups or people work on different sub-systems---
Some of the currently partially available and useful data types:
Genome sequencesBinding motifs in DNA sequences or CIS regulatory regionCODON usageGene expression levels for global gene sets/microRNAsProtein sequencesProtein structuresProtein domainsProtein-protein interactionsBinding relation between proteins and DNARegulatory relation between genesMetabolic PathwaysMetabolite profilesSpecies-metabolite relationsPlants usage in traditional medicines
Usually in wet labs, experiments are conducted to generate such dataIn dry labs like ours we analyze these data to extract targeted information using different algorithms and statistics etc.
Data Types in computational biology/Systems biology
>gi|15223276|ref|NP_171609.1| ANAC001 (Arabidopsis NAC domain containing protein 1); transcription factor [Arabidopsis thaliana]MEDQVGFGFRPNDEELVGHYLRNKIEGNTSRDVEVAISEVNICSYDPWNLRFQSKYKSRDAMWYFFSRRENNKGNRQSRTTVSGKWKLTGESVEVKDQWGFCSEGFRGKIGHKRVLVFLDGRYPDKTKSDWVIHEFHYDLLPEHQRTYVICRLEYKGDDADILSAYAIDPTPAFVPNMTSSAGSVVNQSRQRNSGSYNTYSEYDSANHGQQFNENSNIMQQQPLQGSFNPLLEYDFANHGGQWLSDYIDLQQQVPYLAPYENESEMIWKHVIEENFEFLVDERTSMQQHYSDHRPKKPVSGVLPDDSSDTETGSMIFEDTSSSTDSVGSSDEPGHTRIDDIPSLNIIEPLHNYKAQEQPKQQSKEKVISSQKSECEWKMAEDSIKIPPSTNTVKQSWIVLENAQWNYLKNMIIGVLLFISVISWIILVG
Sequence data (Genome /Protein sequence)
Usually BLAST algorithms based on dynamic programming are used to determine how two or more sequences are matching with each other
Sequence matching/alignments
Twenty amino acids
Four nucleotides
Four nucleotides
CODONS
CODON USAGE
CODON USAGE
Multivariate data (Gene expression data/Metabolite profiles)
There are many types of clustering algorithms applicable to multivariate data e.g. hierarchical, K-mean, SOM etc.
Multivariate data also can be modeled using multivariate probability distribution function
Binary relational Data (Protein-protein interactions, Regulatory relation between genes, Metabolic Pathways) are networks.
Clustering is usually used to extract information from networks.
Multivariate data and sequence data also can be easily converted to networks and then network clustering can be applied.
AtpB AtpAAtpG AtpEAtpA AtpHAtpB AtpHAtpG AtpHAtpE AtpH
Useful Websites
www.geneontology.org www.genome.ad.jp/kegg www.ncbi.nlm.nih.gov www.ebi.ac.uk/databases http://www.ebi.ac.uk/uniprot/ http://www.yeastgenome.org/ http://mips.helmholtz-muenchen.de/proj/ppi/ http://www.ebi.ac.uk/trembl http://dip.doe-mbi.ucla.edu/dip/Main.cgi www.ensembl.org
Some websites
Some websites where we can find different types of data and links to other databases
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
NETWORK TOOLSSource: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
NETWORK TOOLSSource: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)
