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Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems

Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems. Francis J. Doyle III Dept. of Chemical Engineering Biomolecular Science & Engineering Institute for Collaborative Biotechnologies. Role of Models & Analysis. [Kitano, 2002].

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Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems

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  1. Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems Francis J. Doyle III Dept. of Chemical Engineering Biomolecular Science & Engineering Institute for Collaborative Biotechnologies

  2. Role of Models & Analysis [Kitano, 2002] F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  3. BioSPICE (A Vision)In Silico => In Vitro/Vivo Experimentation

  4. Spectrum of Network Modeling All models are abstractions of reality [Bolouri/Davidson] All models are wrong … some are useful [Box] Models are most useful when they are wrong [Various] [Stelling, 2005] F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  5. Modeling for Analysis • Analysis • Robustness – design principles, hypothesis generation • Sensitivity for design of experiment • Sensitivity for ID of targets • Identifiability analysis for ID of markers • Issues • Context is key • Multi-scale issues • Stochastic issues • Local vs. Global behavior • Model “validation” F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  6. Validation, Verification, Consistency, etc. • Validation or verification is critical step in any model identification problem [Ljung, 1999] • Typically: ~half of data used for regression; ~half for “testing” • Matching of data (to date): “consistency” • In practice, only “invalidation” is possible [Poolla et al., 1994] • Contradiction w/ data is often the most valuable role of a model • Model discrimination can suggest new experiments • Competing hypotheses can be resolved • Data sets can be invalidated • Various statistical tools for model invalidation • Measure of error • Confidence intervals • Likelihood ratios F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  7. Circadian Clock Circuits Across Organisms F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  8. Multi-Scale Systems Analysis of Circadian Rhythm Length, Time Organism Organs Cells Networks Proteins Genes

  9. Mammalian Circadian Clock Circuits Traditional control engineering elements: positive and negative feedback loops redundant loops time delay gain modulation hierarchical architecture But… what is the purpose??? F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  10. Robust Yet Fragile (Gene Level) open=single loop filled=double loop Insights from control-theoretic analysis: [Stelling et al., PNAS, 2004] (i) 2-loop architecture used for clock precision (ii) robustness (local) at the expense of fragility (global) • 3 (modified) architectures • single loop • dual loop • redundant dual loop T=transcription/translation TR=intracellular transport GR=gene regulation P=phosphorylation DP=dephosphorylation DG/DL=degradation F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  11. Robust Yet Fragile (Cell Level) X X X X Insights from control-theoretic analysis: (i) Timekeeping is robust to expected disturbances (Temp) (ii) Timekeeping is fragile to “attack” (VIP) X [Ruoff et al., 2005] [Herzog et al., 2004] F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  12. Testing Old Hypotheses “A daily program is useless (indeed disadvantageous) unless it can be phased correctly to local time. Thus it is the phase-control, more than the period control, inherent in entrainment which is the principal dividend selection has reaped in converting a daily program into an oscillator by assuring its automatic re-initiation…” [Pittendrigh & Daan, 1976] robust to clock error clock precision required Locomotor timing relative to clock F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  13. Other Performance Metrics [Bagheri, Stelling, Doyle III, Bioinformatics, 2007] Drosophila Mouse F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  14. Cellular PerformanceversusCellular Network Performance

  15. Clock “Performance” is Context Dependent Cycle-to-cycle variation Period in vivo explants isolated [Herzog et al., 2004] F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  16. Model Formulation [To, Henson, Herzog, Doyle III, Biophys. J., 2007] Coupling Rule 1 unit 1 1 1/2 1 1/√2 1/√5 1/2 1√5 1/2√2 Modified Neuron Model ICC Module VIP release local VIP profile STN Module VIP/VAPC2 complex receptor saturation equilibrium cAMP GRN Module fraction of phosphorylated CREB F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  17. Entrainment Behavior VIP Entrainment Photic Entrainment • Insights from control-theoretic analysis: • [To et al., Biophys J, 2007] • intercellular coupling allows coherent timekeeping • with relatively heterogeneous cells • (ii) synchronicity depends on cell-specific properties • as well as network (coupling) properties [Aton et al., 2005] F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  18. Reverse “clock genetics” showed that Cry1 and Cry2 are each dispensable in circadian behavior Single SCN neurons Cry1-/-: Cry2-/- WT Cry1-/- Cry2-/- van der Horst, 1999 New data [Kay lab]: Clock defects in single cells are autonomous, but not necessarily in SCN slice or animal behavior WT Cry2-/- Cry1-/- Per1-/-

  19. Stochastic Cellular Network Model Coupling via Per transcription rate Core (molecular) oscillator Continuum M-M kinetics Stochastic firing of elementary reactions F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  20. Stochastic Mutant Response[Liu et al., Cell, 2007] cell network (Cry1 -/-) isolated cells (Cry1 -/-) } stochastic simulation model

  21. The Ultimate Level: Organism Performance F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  22. Multi-scale Robust Performance Issues Protein activity/level control • Insights from control analyses: • Robust performance requirements vary across scales • (context is key!) • (ii) Analysis of upper level in hierarchy requires • appropriate detail at lower level (different from reductionism!) Phase/period control Distribution control Context-dependent control Organism Activity Control Length, Time Organism Organs Cells Networks Proteins Genes

  23. Summary – Infrastructure Needs • Modeling/Analysis • Get beyond intracellular focus • Efficient/hierarchical/multi-scale/stochastic models • Seamless incorporation of analysis tools • Modular model merging? (a la CAPE-OPEN) • Formalized hypothesis testing? F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

  24. Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems Dr. Rudi Gunawan Neda Bagheri Kirsten Meeker Henry Mirsky Stephanie Taylor Tsz Leung To Melanie Zeilinger Dr. Peter Chang Collaborators: M. Henson (UMass), E. Herzog (WashU), S. Kay (Scripps), L. Petzold (UCSB), J. Stelling (ETH) Francis J. Doyle III Dept. of Chemical Engineering Biomolecular Science & Engineering Institute for Collaborative Biotechnologies

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