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Computational Methods for Systems and Synthetic Biology Franois Fages The French National Institute for Research in Computer Science and Control INRIA Paris-Rocquencourt Constraint Programming Group http://contraintes.inria.fr/ Franois

  1. Computational Methods for Systems and Synthetic Biology François Fages The French National Institute for Research in Computer Science and Control INRIA Paris-Rocquencourt Constraint Programming Group http://contraintes.inria.fr/ François Fages – Ecole Jeunes Chercheurs - Porquerolles 24/06/2013 1

  2. Overview of the Lectures 1. Introduction • Transposing concepts from programming to the analysis of living processes 2. Rule-based Modeling in Biocham • Macromolecules, compartments and elementary processes in the cell • Boolean, Differential and Stochastic interpretations of reaction rules • Cell signaling, Gene expression, Retrovirus, Cell cycle 3. Temporal Logic constraints in Biocham • Qualitative properties in propositional Computation Tree Logic CTL • Quantitative properties in quantifier-free Linear Time Logic LTL(R) • Parameter optimization and robustness w.r.t. temporal logic properties 4. Conclusion 5. Killer lecture: abstract interpretation in Biocham 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 2

  3. References A wonderful textbook: Molecular Cell Biology. 5th Edition, 1100 pages+CD, Freeman Publ. Lodish, Berk, Zipursky, Matsudaira, Baltimore, Darnell. Nov. 2003. Formal Cell Biology in BIOCHAM (tutorial). François Fages and Sylvain Soliman. 8th International School on Computational Systems Biology. ISpringer-Verlag, LNCS 5016. Mar. 2008.(pdf) The Biochemical Abstract Machine BIOCHAM. http://contraintes.inria.fr/BIOCHAM Modeling dynamic phenomena in molecular and cellular biology. Segel. Cambridge Univ. Press. 1987. 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 3

  4. Systems Biology ? “Systems Biology aims at systems -level understanding [which] requires a set of principles and methodologies that links the behaviors of molecules to systems characteristics and functions.” H. Kitano, ICSB 2000 • Analyze (post-)genomic data produced with high-throughput technologies (stored in databases like GO, KEGG, BioCyc, etc.); • Integrate heterogeneous data about a specific problem; • Understand and predict the behaviors of large networks of genes and proteins.  Systems Biology Markup Language (SBML): model exchange format  SBML model repositories: e.g. biomodels.net 261 curated models 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 4

  5. Issue of Abstraction in Systems Biology Models are built in Systems Biology with two contradictory perspectives : 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 5

  6. Issue of Abstraction in Systems Biology Models are built in Systems Biology with two contradictory perspectives : 1) Models for representing knowledge : the more concrete the better 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 6

  7. Issue of Abstraction in Systems Biology Models are built in Systems Biology with two contradictory perspectives : 1) Models for representing knowledge : the more concrete the better 2) Models for making predictions : the more abstract the better ! 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 7

  8. Issue of Abstraction in Systems Biology Models are built in Systems Biology with two contradictory perspectives : 1) Models for representing knowledge : the more concrete the better 2) Models for making predictions : the more abstract the better ! La simplicité est la sophistication suprême. Léonard de Vinci. 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 8

  9. Issue of Abstraction in Systems Biology Models are built in Systems Biology with two contradictory perspectives : 1) Models for representing knowledge : the more concrete the better 2) Models for making predictions : the more abstract the better ! La simplicité est la sophistication suprême. Léonard de Vinci. These perspectives can be reconciled by organizing formalisms and models into hierarchies of abstractions . To understand a system is not to know everything about it but to know abstraction levels that are sufficient for answering questions about it 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 9

  10. Issue of Abstraction in Systems Biology Models are built in Systems Biology with two contradictory perspectives : 1) Models for representing knowledge : the more concrete the better 2) Models for making predictions : the more abstract the better ! La simplicité est la sophistication suprême. Léonard de Vinci. These perspectives can be reconciled by organizing formalisms and models into hierarchies of abstractions . To understand a system is not to know everything about it but to know abstraction levels that are sufficient for answering questions about it Karl Popper empirical falsification versus positivist confirmation 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 10

  11. Formal Semantics of Living Processes ? Formally, “the” behavior of a system depends on our choice of observables. ? ? Mitosis movie [Lodish et al. 03] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 11

  12. Boolean Semantics • Formally, “the” behavior of a system depends on our choice of observables. • Presence/absence of molecules • Boolean transitions models 0 1 Mitosis movie [Lodish et al. 03] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 12

  13. Continuous Semantics • Formally, “the” behavior of a system depends on our choice of observables. • Concentrations of molecules • Ordinary Differential Equation models x ý Mitosis movie [Lodish et al. 03] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 13

  14. Stochastic Semantics • Formally, “the” behavior of a system depends on our choice of observables. • Numbers of molecules • Continuous Time Markov Chain models  n Mitosis movie [Lodish et al. 03] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 14

  15. Temporal Logic LTL • Formally, “the” behavior of a system depends on our choice of observables. • Presence/absence of molecules • Temporal logic formulas F x F (x ^ F (  x ^ y)) F x FG (x v y) … Mitosis movie [Lodish et al. 03] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 15

  16. Temporal Logic LTL(R) • Formally, “the” behavior of a system depends on our choice of observables. • Concentrations of molecules • Temporal Logic with Constraints over R F (x >0.2) F x > 1 F (x >0.2 ^ F (x<0.1 ^ y>0.2)) FG (x>0.2 v y>0.2) … Mitosis movie [Lodish et al. 03] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 16

  17. Hierarchy of Semantics abstraction Theory of abstract Interpretation Abstractions as Galois connections Boolean model [Cousot Cousot POPL’77] [Fages Soliman CMSB’06,TCS’07] Discrete model Differential model Stochastic model Syntactical model concretization 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 17

  18. Regulation Graph as Abstraction abstraction Differential regulation graph (pos/neg influences w.r.t. Boolean model the signs of the Jacobian) Discrete model Structural regulation graph Differential model (pos/neg influences w.r.t. the stoichiometric Stochastic model coefficient in reactions) Thm . Same graphs for Syntactical monotonic kinetics model concretization [Fages Soliman FMSB’06] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 18

  19. Regulation Graphs Circuit Analyses Boolean circuit analysis abstraction abstraction Discrete circuit analysis Boolean model abstraction Jacobian circuit analysis Discrete model abstraction Differential model Thm. Positive circuits are a necessary condition for multistationarity Stochastic model [Thomas 81] [Soul é 03] [Remy Ruet Thieffry 05] Syntactical [Richard 07] [Soliman 13] model concretization 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 19

  20. Mammalian Cell Cycle Control Map [Kohn 99] 24/06/2013 François Fages - Ecoles Jeunes Chercheurs - Porquerolles 20

  21. Hierachy of Models / Model Reductions Models of circadian clock in http://www.biomodels.net Reductions as Subgraph Epimorphisms [Gay Fages Soliman ECCB’10 DAM 13] 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 21

  22. Overview of the Lectures 1. Introduction • Transposing concepts from programming to the analysis of living processes 2. Rule-based Modeling in Biocham • Macromolecules, compartments and elementary processes in the cell • Boolean, Differential and Stochastic interpretations of reaction rules • Cell signaling, cell cycle models 3. Temporal Logic constraints in Biocham • Qualitative properties in propositional Computation Tree Logic CTL • Quantitative properties in quantifier-free Linear Time Logic LTL(R) • Model inference from temporal logic properties 4. Conclusion 5. Killer lecture: abstract interpretation in Biocham 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 22

  23. Cell Molecules • Small molecules: covalent bonds 50-200 kcal/mol – 70% water – 1% ions – 6% amino acids (20), nucleotides (5), – fats, sugars, ATP, ADP, … 24/06/2013 François Fages - Ecole Jeunes Chercheurs - Porquerolles 23

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