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

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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

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

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

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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

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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

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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

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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

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

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

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

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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

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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

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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

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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

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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

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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

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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

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

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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

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

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

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

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

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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

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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

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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

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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

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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

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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

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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).

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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)

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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

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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

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

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

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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

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

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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

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

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

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