Whole Cell in silico Modelling

Whole Cell in silico Modelling

Literature seminar at the European Bioinformatics Institute, Cambridge, UK

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Vladimir Kiselev

August 17, 2012
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  1. Whole-Сell in silico Modelling Literature seminar Vladimir Kiselev 29/08/12

  2. • Number of compartments Cell Complexity E. coli HeLa •

    Number of components/states/interactions
  3. Recent Progress Systems Biology • ‘-omics’ experiments • math. modelling

    • deterministic • stochastic • hybrid Ventura, B. D., Lemerle, C., Michalodimitrakis, K. & Serrano, L. From in vivo to in silico biology and back. Nature 443, 527–533 (2006). No space!!!
  4. Recent Progress • 118 reactions • more than 236 parameters

    Borisov, N. et al. Systems-level interactions between insulin–EGF networks amplify mitogenic signaling. Mol Syst Biol 5, (2009). 3 years of life Kiselev, V. Y. Computational Study of Electrostatic Contribution to Membrane Dynamics. My thesis 1–124 (2011). Diffusion of a pentapeptide on cell membrane
  5. E. coli

  6. First Whole-Cell Model • based on data from 900 scientific

    papers ! • includes more than1900 experimentally observed parameters ‘This is potentially the new Human Genome Project. It's going to take a really large community effort to get close to a human model.’ J.R. Karr et al. A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150, 389–401 (2012) First Author - J. Karr
  7. M. Genitalium • second-smallest known bacterium • smallest genome –

    only 525 genes, as opposed to the 4,288 of E. coli • contains one circular chromosome of 582,970 base pairs
  8. Main principles • 28 sub-models • 16 cell variables 1

     second time step (sub-models are independent) - the variables are allocated among the processes at each time step
  9. Algorithm Each module was modeled using the most appropriate mathematical

    representation: • Metabolism - flux balance • RNA/protein degradation - Poisson process
  10. Predictions:  Chromosome exploration • 90% of chromosome is bound

    by at least one protein in first 20 min • RNA pol binds 90% of chromosome in first 49 min • 90% of genes are expressed in first 143 min
  11. Predictions: DBPs collisions • 30,000 collision per cell cycle •

    displacement of 0.93 proteins per sec • the majority of collisions are caused by RNA pol (84%) and DNA pol (8%)
  12. Predictions: cell cycle regulation Amount of dNTP produced prior to

    replication controls the duration of replication Cell-cycle duration is independent of genetic regulation!
  13. Predictions: global distribution of energy ATP and GTP synthesis is

    1,000-fold higher than others 44% discrepancy between total energy usage and production ‘If growth is limited by nutrients other than energy, however, bacteria can spill ATP in reactions that cannot be readily categorized as maintenance per se. Recent work indicated that bacteria utilize futile cycles of ions through the cell membrane as a means of hydrolyzing ATP’ Russel, J.B. et al. Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol. Rev. vol. 59, №1, p. 48-62, (1995).
  14. Predictions: in silico single- gene disruption strains • 284 genes

    are essential •177 genes are nonessential
  15. Model-Driven  Biological Discovery Refined Kcat values are consistent with

    more closely related species The higher consistency reflects novel insights into M. genitalium biology Gene disruption strains reveal discrepancy between the model and experimental data
  16. Cell Cycle

  17. Data explosion 0 27 53 80 107 133 160 1982

    1992 2002 2011 GenBank sequences (millions) 0 233 467 700 933 1167 1400 1996 2001 2006 2011 NAR databases number http://www.ncbi.nlm.nih.gov/genbank/genbankstats.html http://www.oxfordjournals.org/nar/database/a/
  18. Super Cell

  19. How it works Stimuli Response ArrayExpress Ensembl GenBank Virtual Cell

    Biomodels UniProt Reactome ... EGA ... Drugs Medicine Industry Food Research Students Knowledge