Sarojini Attili Kimberly Taylor. 2 Sample D 3 Sample A Sample E Sample B Sample D Sample C

Sarojini AttiliKimberly Taylor

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Computational model of the human body that integrates all the different whole cell models

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Computational model of the pathogen

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Details of mutation/s and Phenotypic data

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Whole cell modeling for personalized

medicine

Modeling Biological Systems• Significant task of systems biology and mathematical

biology• Computational systems biology aims to develop and

use AlgorithmsData structuresVisualizationCommunication tools

• Goal: Perform computer modeling of biological systems.

• It involves the use of computer simulations of biological systems to both analyze and visualize the complex connections of these cellular processes.

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Whole cell modelingDeveloping a complete model

for a specific cell that includes all pathways, processes and functionality.

The whole cell model explains the entire lifecycle of the cell.

The authors of this paper have modeled the life cycle of individual Mycoplasma genitalium cells.

Whole cell modeling although challenging, is very important for the future of medicine.

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Why whole cell modeling?• Whole cell models comprehensively predict how

phenotypes emerge from genotypes.• Whole-cell modeling could enable rational bioengineering

and precision medicine.• Whole cell models could also enable clinicians to

individualize therapy. • Combined with genome synthesis and transplantation,

whole-cell models could enable bioengineers to produce biofuels.

• Overall, whole-cell models could be powerful scientific tools.

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History of whole cell modeling• Beginning in the late 1970s, researchers began

modeling cell physiology, primarily using ordinary differential equations, creating increasingly detailed models over the next three decades.

• Later on, other groups introduced frameworks that require fewer parameters than ODE systems including constraint-based and Boolean methods.

• Combining these approaches the authors of this paper developed a hybrid methodology to model the life cycle of individual Mycoplasma genitalium cells – Individual biological processes were modeled, each with its own mathematical representation and individual outputs were merged to compute the overall state of the cell.

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Some existing computational modelsCardiac system models – The first model

developed for the heart was the Hodgkin–Huxley model, today we have more sophisticated models

Computational models for different cellular processes or parts of the cell such as:The dynamics of Ca2+ wave propagation during

xenopus oocyte maturationDynamics of calcium sparks and calcium leak in

the heartMetabolic model of the mitochondrion

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OutlineCore principles of whole-cell modeling

Model construction process outline

Example of a whole cell modelChallenges to achieving complete models

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The principles of whole-cell modeling

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Whole cell models should represent individual cells. Single cell models can account for how temporal dynamics and stochastic variation affect behavior. Single cells are also tractable because they are independent and directly result from molecular biochemistry.

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Single cellularity:

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Behavior is determined by interacting pathways and genes. Consequently, whole-cell models should represent every known cellular and gene function. Models which represent every known function are powerful tools. For example, genome-scale metabolic models which represent every known metabolic reaction and enzyme have been used to identify missing reactions and enzymes.

Functional closure:

Whole-cell models should represent the cell and its environment as a closed system. Models should explicitly account for exchanges among pathways and the environment and not have arbitrary sources and sinks.

Whole-cell models should also represent the entire cell cycle. This ensures that models account for how cells regulate pathways in time to coordinate their life cycle. For example, models should account for how the dynamics of DNA replication affect dNTP concentrations and metabolism.

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Molecular closure:

Temporal closure:

In addition, whole-cell models should represent cellular biochemistry and biophysics, including mass conservation, thermodynamics, and spatial organization. Some of the methods capable of representing cellular biophysics are - molecular dynamics, Brownian dynamics, lattice-based models. Below are some representations of space:

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Biophysics:

In particular, whole-cell models should be constructed from differential descriptions of molecular biochemistry and predict the emergence of cellular-scale dynamics. Emergent dynamics are valuable opportunities for experimental validation and discovery.

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k1S + E C

k-1

P + Ek2

ckdt

dp

ckksekdt

dc

sekckkdt

de

sekckdt

ds

2

121

121

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)(

)(

Dynamics:

Furthermore, whole-cell models should be discrete and stochastic. Stochastic models naturally predict the emergence of cellular variation. For example, stochastic models can account for how stochastic transcription initiation creates variation in gene expression and growth. This variation is another valuable opportunity for experimental validation.

Whole-cell models must be evaluated by comparison to experimental data. Consequently, whole-cell models should represent specific genomes. This constrains the space of training data.

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Stochasticity:

Species specificity:

Despite the explosion in experimental data, limited data is available. For example, there is little data about non-coding RNA. Consequently, models should be parsimonious. This minimizes the need to identify unmeasured parameters.

Like other large engineered systems, whole-cell

models are best developed by combining multiple pathway submodels. This enables submodels to be developed and tested independently by different investigators using different representations.

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Parsimony:

Modularity:

Finally, whole-cell models should be transparent, well-annotated, and reproducible. Researchers should be able to reproduce models from their primary sources, as well as reproduce simulations using multiple simulators. Models should also be described using transparent languages, this is essential for collaborative modeling. Example: SBML

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Reproducibility:

Model constructionExperimental

curationMathematical

formulationSubmodel

integrationParameter

estimationModel

refinement and validation

Visualization and analysis

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The first step to constructing a model is to choose an organism and assess the feasibility of modeling it by assembling the available experimental knowledge.

Some examples of experimental data sources include - organism database tools such as Pathway Tools, WholeCellKB, BioMart and Intermine.

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Experimental curation:

• A mathematical description of how cells evolve over time must be constructed.

• Describing the cell as thoroughly as possible using existing knowledge avoids unknown parameters and expensive computations.

• One model can be used for many scientific questions. • Individual submodels must be implemented and/or constructed

from experimental data. • Databases like BioModels and CellML contain many existing

pathway models. • Rule-based modeling is a powerful and scalable approach for

assembling genome-scale models. Some of the tools that can be used to generate mathematical

models include:• BioNetGen• BioUML• CellDesigner• CobraPy• COPASI• E-Cell• iBioSim

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Mathematical formulation:

• The individual submodels that were developed as part of the mathematical formulation must be combined.

• Homogeneous submodels can be merged analytically. • Heterogeneous submodels must be combined in a

multistep approach; hybrid simulators have been developed recently which are capable of integrating heterogeneous submodels.

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Submodel Integration:

Once the model's structure has been implemented, the model's parameters must be identified by matching the model's predictions to experimental data.

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Parameter Estimation:

Next, an important step to constructing a whole-cell model is to iteratively evaluate the model's predictions and refine the model.

Predicted phenotypes of genetic perturbations should be evaluated.

Methods used to automate model refinement:

Robotic and microfluidic experimentation

Computational gap filling30

Model refinement and validation:

The last step in whole-cell modeling is to simulate the model, analyze simulation results to construct new hypotheses, and conduct experiments to test those

hypotheses.

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Visualization and analysis:

Modelling exampleKarr, JR et al. (2012) A whole-cell

computational model predicts phenotype from genotype. Cell 150: 389-401

One of the most complete and rigorous models to date

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Modelling of Mycoplasma genitaliumSmall bacterium isolated from urethra in

1980Causes urethritis (inflammation of the

urethra) in both men and womenImplicated in HIV transmissionGenome is single circular dsDNA with 525

genes

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Goals of M. genitalium modelling Describe the complete life cycle of a single

cell on level of single molecules Replicate function of every known gene

product Predict multiple cellular behaviors,

including macromolecular synthesis and the complete cell cycle

Models were based on 900+ publications and 1900+ observed parameters

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How did they do it?Identified 28 modules covering cellular functionsAll modules independently built, parameterized and tested

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How did they do it?Modules were assumed independent at time

scales < 1 s

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Model integration

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Why modules?• Module-based modelling proposed in 1999

– 668 citations in PubMed

• Model is based on experimental observations1.Action potentials2.Decision making in bacteriophage λ

38Hartwell et al. (1999) From molecular to modular cell biology. Nature 402: C47-C52

Experimental validation

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Exploring the cell cycle

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Global energy analysis

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Phenotype studiesDisruption studies performed for all 525

genes284 essential genes and 117 non-essentialModel allows prediction of phenotype from

known genotype

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Challenges in whole-cell modellingMacklin, DN et al. (2014) The future of

whole-cell modeling. Current Opinion in Biotechnology 28:111–115

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Challenges in whole-cell modeling

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Experimental interrogationM. genitalium was chosen because of its

small genome, but there are relatively few papers on this pathogen

Future research will focus on well-studied organisms such as E. coli, S. cerevisiae or Mycoplasma pneumoniae

E. coli:347,790 articles in PubMed (as of 11/12/2015)4,288 protein-coding genes

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Data curationWhere and how do you store the data?Data for M. genitalium available at

http://www.wholecellkb.org/Automatic curation will be needed

Human and machine involvementSeparate database for each organism

Question: how should the data be formatted?

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WholeCellKB

47 http://www.wholecellkb.org/

WholeCellKB

48http://www.wholecellkb.org/

Other considerationsMathematical models could use a Boolean

“switch” for communication between modules and sub-modules

Cell behavior cannot violate physical lawsModel must be consistent with biological

phenotypes

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Accelerated computation~10 hours were required for each simulation

of M. genitaliumSimulation of 525 single-gene disruptions

required 5250 hours, or ~220 daysIn theory, a single simulation of E. coli would

take 81 days!

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Speeding up computationHigh performance parallel computingCustom hardware platformsCould this be an application for quantum

computing?

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Analysis and visualizationExtensive analysis of raw data is needed to

interpret resultsMachine learningDynamic systems analysis

How do you visualize large data sets?

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Model validationFeedback between model and experimental

resultsM. genitalium work was more intuitive than

rigorousQuantitative metrics must be developed

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Collaboration and community developmentCode base for the M. genitalium whole-cell

model released under MIT licenseWhole cell modelling will proceed faster with

collaboration between all researchersWill competing groups want to share their

results before publication?Is a uniform format needed?

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ConclusionsWhole-cell modelling has been shown to be

successfulValidated by experimental resultsPhenotype predicted from genotypePotential for study of cell processes that cannot

be addressed experimentallyImprovements in analysis, visualization and

technology are needed

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Future directionsNon-bacterial cellsCells with large genomes

With current technology, simulation of single human cell (3 billion bp, ~25k genes) would take 476 hours or ~20 days!

Cell-cell interactionsModelling of organs and organ systemsModelling of multicellar organisms

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ReferencesKarr, JR et al. (2015) The principles of whole-cell

modeling. Current Opinion in Microbiology 27: 18-24http://www.sciencedirect.com/science/article/pii/

S1369527415000685Karr, JR et al. (2012) A whole-cell computational model

predicts phenotype from genotype. Cell 150: 389-401http://www.sciencedirect.com/science/article/pii/

S0958166914000251Macklin, DN et al. (2014) The future of whole-cell

modeling. Current Opinion in Biotechnology 28:111–115http://www.biomedcentral.com/1471-2105/14/253

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Referenceshttp://biologypop.com/the-evolution-of-the-cells/http://www.biomedcentral.com/1752-0509/2/72/

figure/F4?highres=yhttp://oreillyscienceart.com/figures-for-publication/http://www.elveflow.com/microfluidic-tutorials/cell-

biology-imaging-reviews-and-tutorials/microfluidic-for-cell-biology/concepts-and-methodologies/

https://openi.nlm.nih.gov/detailedresult.php?img=3224382_1752-0509-5-155-1&req=4

http://www.biomedcentral.com/1471-2105/14/253https://www.google.com/imghp

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Thank you!

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Documents

Sarojini Attili Kimberly Taylor. 2 Sample D 3 Sample A Sample E Sample B Sample D Sample C