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Whole-cell models: combining genomics and dynamical modeling
Networks WC model Jonathan Karr July 10, 2016 KarrLab.org
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Outline Introduction to whole-cell (WC) models State-of-the-art
Systemizing and accelerating WC modeling Role of genomics/bioinformatics in WC modeling
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Motivation: precision medicine
Source: Sunney Xie, Harvard University
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Bioengineering and medicine require whole-cell (WC) models
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What is a whole-cell (WC) model?
Single-cell Mechanistic/dynamic Genetically complete Stochastic Molecularly precise Accurate Toward this goal we have built a gene-complete, computational model of a single bacterial cell which Integrates all cellular processes into a single computational model, providing a unified understanding of cellular physiology, which Predicts the dynamics of every molecule and process, Can be used to guide experimental design and inform data analysis Hopefully in the future, in concert with emerging genome-scale DNA synthesis techniques, can be used to guide rational engineering of biological systems Temporally complete Species-specific Karr et al., 2015
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Challenges to WC models
Incomplete knowledge Parts list Interactome Kinetic data Few tools for building large models
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WC models are now feasible
Genomics Networks WC model
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M. genitalium model represents 28 pathways
Karr et al., 2012
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WC models predict fine-grained behaviors
Karr et al., 2012
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WC models predict systems-level behaviors
Karr et al., 2012
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WC models can design novel strains
M. mycoides M. pneumoniae M. genitalium
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WC models can reposition drugs
Kazakiewicz et al., 2015
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Pilot WC models for bioengineering and medicine
Precision medicine Mycoplasma pneumoniae Human embryonic stem cells
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Limitations of our WC modeling approaches
Methods are not rigorous Time-consuming to build Hard to understand, reuse, reproduce
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Systemizing and accelerating WC modeling
Genomics Networks WC model
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Data aggregation tools
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High-level rule-based WC language
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Reusable, high-performance simulator
Goldberg et al., 2016
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WC modeling needs genomics & bioinformatics
Networks needed to seed submodels Protein complex Transcriptional regulation Genomics data needed to constrain parameters RNA, protein expression and half-lives Protein DNA binding motifs Prediction tools needed to impute and extrapolate data Protein localization: PSORT Protein half-lives: N-end rule
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WC modeling needs genomics & bioinformatics
Workflows needed to build models reproducibly PGDBs needed to organize training data Big Data methods needed to visualize and analyze simulations
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WC modeling needs genomics & bioinformatics
More extensive characterization of cells Cell-scale metabolomics Cell-scale kinetics Networks of poorly studied pathways RNA modification DNA repair
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Benefits of WC modeling for genomics & bioinformatics
Integrate heterogeneous datasets and networks Contextual analysis of datasets and networks Identify poorly-characterized pathways and pathway interactions Provide aggregated datasets
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WC modeling trajectory
Phase Activity 1 Formalize WC modeling 2 Develop collaborative modeling platform 3 Pilot consortium models 4 Community-wide WC modeling
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Summary WC models could guide bioengineering and medicine
Developing tools to systemize and accelerate every aspect of WC modeling Piloting models of Mycoplasmas and H1-hESC
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Acknowledgements Yin Hoon Chew Arthur Goldberg Graeme Gossel
Yosef Roth Pablo Meyer IBM/Sinai Roger Rodriguez UNAM Luis Serrano Maria Lluch-Senar Veronica Llorens
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Central Park Positions available KarrLab.org
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Summary WC models could guide bioengineering and medicine
Developing tools to systemize and accelerate every aspect of WC modeling Piloting models of Mycoplasmas and H1-hESC
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