1. BioNetGen: a system for modeling the dynamics of protein-protein interactions Bill Hlavacek Theoretical Biology and Biophysics Group Los Alamos National Laboratory

2. Outline 1. The biochemistry of cell signaling and combinatorial complexity 2. The conventional approach to modeling 3. The rule-based approach to modeling 4. Tools

3. Multiplicity of sites and binding partners gives rise to combinatorial complexity Epidermal growth factor receptor (EGFR)

4. Multiplicity of sites and binding partners gives rise to combinatorial complexity Epidermal growth factor receptor (EGFR) 9 sites  2 9 =512 phosphorylation states

5. Multiplicity of sites and binding partners gives rise to combinatorial complexity Epidermal growth factor receptor (EGFR) 9 sites  2 9 =512 phosphorylation states Each site has ≥ 1 binding partner  more than 3 9 =19,683 total states

6. Multiplicity of sites and binding partners gives rise to combinatorial complexity Epidermal growth factor receptor (EGFR) 9 sites  2 9 =512 phosphorylation states Each site has ≥ 1 binding partner  more than 3 9 =19,683 total states EGFR must form dimers to become active  more than 1.9  10 8 states

7. Signaling proteins typically contain multiple phosphorylation sites > 50% are phosphorylated at 2 or more sites Source: Phospho.ELM database v. 3.0 (http://phospho.elm.eu.org)http://phospho.elm.eu.org

8. Outline 1. The biochemistry of cell signaling and combinatorial complexity 2. The conventional approach to modeling 3. The rule-based approach to modeling 4. Tools

9. Early events in EGFR signaling EGF = epidermal growth factor EGFR = epidermal growth factor receptor 1. EGF binds EGFR EGFR EGF ecto

10. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF dimerization 2. EGFR dimerizes

11. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P Y1092 Y1172

12. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P 4. Grb2 binds phospho-EGFR Grb2 Y1092 SH2 Grb2 pathway

13. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P 4. Grb2 binds phospho-EGFR Grb2 Y1092 SH3 5. Sos binds Grb2 (Activation Path 1) Sos Grb2 pathway

14. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P 4. Shc binds phospho-EGFR Y1172 Shc PTB Shc pathway

15. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P 4. Shc binds phospho-EGFR Y1172 Shc Y317 5. EGFR transphosphorylates Shc P Shc pathway

16. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P 4. Shc binds phospho-EGFR Y1172 Shc 5. EGFR transphosphorylates Shc P 6. Grb2 binds phospho-Shc Grb2 SH2 Shc pathway

17. Early events in EGFR signaling 1. EGF binds EGFR EGFR EGF 2. EGFR dimerizes 3. EGFR transphosphorylates itself P PP P 4. Shc binds phospho-EGFR Y1172 Shc 5. EGFR transphosphorylates Shc P 6. Grb2 binds phospho-Shc Grb2 SH3 7. Sos binds Grb2 (Activation Path 2) Sos Shc pathway

18. A reaction-scheme diagram Species: One for every possible modification state of every complex Reactions: One for every transition among species This scheme can be translated to obtain a set of ODEs, one for each species

19. Combinatorial complexity of early events Monomeric species EGFR 2 states 4 states 6 states 48 species

20. Combinatorial complexity of early events Monomeric species EGFR 2 states 4 states 6 states 48 species Dimeric species EGF 24 states N  (N+1)/2 = 300 species

21. A conventional model for EGFR signaling The Kholodenko model* 5 proteins 18 species 34 reactions *J. Biol. Chem. 274, 30169 (1999)

22. Assumptions made to limit combinatorial complexity 1.Phosphorylation inhibits dimer breakup No modified monomers P P P Bottleneck for dimers

23. Assumptions made to limit combinatorial complexity 2.Adaptor binding is competitive No dimers with more than one site modified P PP P P

24. Assumptions made to limit combinatorial complexity 1.Phosphorylation inhibits dimer breakup 2.Adaptor binding is competitive Experimental evidence contradicts both assumptions.

25. Outline 1. The biochemistry of cell signaling and combinatorial complexity 2. The conventional approach to modeling 3. The rule-based approach to modeling 4. Tools

26. Rule-based Approach: A Graph Grammar for Biochemical Systems Faeder et al., Proc. ACM Symp. Appl. Computing (2005) Blinov et al., Proc. BioCONCUR (2005) http://cellsignaling.lanl.gov/bionetgen

27. Proteins in a model are introduced with molecule templates Shc Grb2 PTB EGF EGFR Y1172 Y1092 CR1 L1 Molecule templates Y317SH2 SH3 Sos Nodes represent components of proteins Components may have attributes: P or

28. Complexes are connected instances of molecule templates P PP P P Edges represent bonds between components Bonds may be internal or external An EGFR dimer

29. Patterns select sets of chemical species with common features P EGFR Y1092 selects PP P suppressed components don’t affect match P PP P P P P,,,, … Pattern that selects EGFR phosphorylated at Y1092. twice inverse indicates any bonding state

30. Reaction rules, composed of patterns, generalize reactions EGF binds EGFR + EGFR L1 EGF CR1 k +1 k -1 Patterns select reactants and specify graph transformation - Addition of bond between EGF and EGFR

31. Rule-based version of the Kholodenko model 5 molecule types 23 reaction rules No new rate parameters (!) 18 species 34 reactions 356 species 3749 reactions Blinov et al. Biosystems 83, 136 (2006).

32. Dimerization rule eliminates previous assumption restricting breakup of receptors + k +2 k -2 EGFR EGF dimerization Dimers form and break up independent of phosphorylation of cytoplasmic domains EGFR dimerizes (600 reactions)

33. Summary of the rule-based approach Rigor – Level of detail (sites) consistent with biological knowledge – Physical basis for parameters – Unbiased simplifications Compositionality – Models can be easily extended by adding new molecules, components, and rules – Reusability of parts Clarity – Easy to visualize, because model elements are graphs – Easy to store and modify in electronic form (graph data structures) Rule-based models have about the same number of parameters as conventional models

34. Outline 1. The biochemistry of cell signaling and combinatorial complexity 2. The conventional approach to modeling 3. The rule-based approach to modeling 4. Tools

35. BioNetGen2: Software for graphical rule-based modeling Graphical interface for composing rules Text-based language Simulation engine

36. BNGL: A textual language for graphical rules L(r) + R(l,d) L(r!1).R(l!1,d) kp1, km1

37. BNGL: A textual language for graphical rules L(r) + R(l,d) L(r!1).R(l!1,d) kp1, km1 reactant patternsproduct patternrate law(s) moleculecomponents (unbound)a bond

38. Graphical Interface to BioNetGen Greatly simplifies construction, visualization, and simulation of complex models

39. Conclusions Biological systems exhibit combinatorial complexity Conventional approaches to modeling selectively ignore this problem Rule-based modeling provides a more general and rigorous approach – Parameter requirements can be modest – Models are easy to modify, extend, and exchange General-purpose software (BioNetGen) is available – Based on formal visual language (portable) – Facilitates precise, intuitive communication about protein interactions – Implements multiple simulation methods Applications of modeling approach in collaboration with experimentalists

40. Grand challenge: A comprehensive rule-based model EGFR Pathway Map Oda et al., Mol. Syst. Biol. (2005).

41. Michael L. Blinov James R. Faeder G. Matthew Fricke William S. Hlavacek Funding NIH DOE