BioNetGen: a system for modeling the dynamics of protein-protein interactions

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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

• The biochemistry of cell signaling and
  combinatorial complexity
• The conventional approach to modeling
• The rule-based approach to modeling
• Tools

3
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 ? 29512 phosphorylation states
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Multiplicity of sites and binding partners gives
rise to combinatorial complexity
Epidermal growth factor receptor (EGFR)
9 sites ? 29512 phosphorylation states Each
site has 1 binding partner ? more than
3919,683 total states
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Multiplicity of sites and binding partners gives
rise to combinatorial complexity
Epidermal growth factor receptor (EGFR)
9 sites ? 29512 phosphorylation states Each
site has 1 binding partner ? more than
3919,683 total states EGFR must form dimers to
become active ? more than 1.9?108 states
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Signaling proteins typically contain multiple
phosphorylation sites
gt 50 are phosphorylated at 2 or more sites
Source Phospho.ELM database v. 3.0
(http//phospho.elm.eu.org)
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Outline

• The biochemistry of cell signaling and
  combinatorial complexity
• The conventional approach to modeling
• The rule-based approach to modeling
• Tools

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Early events in EGFR signaling
EGF epidermal growth factor EGFR epidermal
growth factor receptor
EGF
1. EGF binds EGFR
ecto
EGFR
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Early events in EGFR signaling
EGF
1. EGF binds EGFR
dimerization
2. EGFR dimerizes
EGFR
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Early events in EGFR signaling
EGF
1. EGF binds EGFR
2. EGFR dimerizes
Y1092
P
P
3. EGFR transphosphorylates itself
Y1172
P
P
EGFR
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Early events in EGFR signaling
Grb2 pathway
EGF
1. EGF binds EGFR
2. EGFR dimerizes
Y1092
P
P
3. EGFR transphosphorylates itself
Grb2
P
P
SH2
4. Grb2 binds phospho-EGFR
EGFR
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Early events in EGFR signaling
Grb2 pathway
EGF
1. EGF binds EGFR
Grb2
2. EGFR dimerizes
Y1092
P
P
3. EGFR transphosphorylates itself
Sos
P
P
SH3
4. Grb2 binds phospho-EGFR
EGFR
5. Sos binds Grb2 (Activation Path 1)
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Early events in EGFR signaling
Shc pathway
EGF
1. EGF binds EGFR
2. EGFR dimerizes
P
P
3. EGFR transphosphorylates itself
Y1172
P
P
4. Shc binds phospho-EGFR
EGFR
Shc
PTB
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Early events in EGFR signaling
Shc pathway
EGF
1. EGF binds EGFR
2. EGFR dimerizes
P
P
3. EGFR transphosphorylates itself
Y1172
P
P
P
4. Shc binds phospho-EGFR
EGFR
Shc
Y317
5. EGFR transphosphorylates Shc
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Early events in EGFR signaling
Shc pathway
EGF
1. EGF binds EGFR
2. EGFR dimerizes
P
P
Shc
3. EGFR transphosphorylates itself
Y1172
P
P
P
4. Shc binds phospho-EGFR
EGFR
Grb2
SH2
5. EGFR transphosphorylates Shc
6. Grb2 binds phospho-Shc
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Early events in EGFR signaling
Shc pathway
EGF
1. EGF binds EGFR
2. EGFR dimerizes
P
P
Sos
Grb2
Shc
3. EGFR transphosphorylates itself
Y1172
P
P
P
4. Shc binds phospho-EGFR
EGFR
SH3
5. EGFR transphosphorylates Shc
6. Grb2 binds phospho-Shc
7. Sos binds Grb2 (Activation Path 2)
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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
2 states
4 states
48 species
6 states
EGFR
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Combinatorial complexity of early events
Monomeric species
2 states
4 states
48 species
6 states
EGFR
Dimeric species
EGF
N?(N1)/2 300 species
24 states
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A conventional model for EGFR signaling
The Kholodenko model
J. Biol. Chem. 274, 30169 (1999)
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Assumptions made to limit combinatorial complexity

1. Phosphorylation inhibits dimer breakup

Bottleneck for dimers
No modified monomers
P
P
P
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Assumptions made to limit combinatorial complexity

1. Adaptor binding is competitive

No dimers with more than one site modified
P
P
P
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
Molecule templates
Grb2
Sos
Shc
EGFR
EGF
L1
CR1
PTB
SH2
SH3
Y317
Y1092
Y1172
Nodes represent components of proteins
Components may have attributes
or
P
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Complexes are connected instances of molecule
templates
An EGFR dimer
P
P
P
P
P
Edges represent bonds between components
Bonds may be internal or external
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Patterns select sets of chemical species with
common features
Pattern that selects EGFR phosphorylated at Y1092.
selects
P
P
P
P
P
P
Y1092
P
P
P
P
P
,
,
,
,
EGFR
twice
inverse indicates any bonding state
suppressed components dont affect match
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Reaction rules, composed of patterns, generalize
reactions
EGF binds EGFR
k1
L1
EGF
CR1

k-1
EGFR
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
EGFR dimerizes (600 reactions)
EGF
k2
dimerization

k-2
EGFR
Dimers form and break up independent of
phosphorylation of cytoplasmic domains
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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) lt-gt L(r!1).R(l!1,d) kp1, km1
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BNGL A textual language for graphical rules
reactant patterns
product pattern
rate law(s)
L(r) R(l,d) lt-gt L(r!1).R(l!1,d) kp1, km1
molecule
components (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