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An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational and Systems Biology University of Pittsburgh School.

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Presentation on theme: "An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational and Systems Biology University of Pittsburgh School."— Presentation transcript:

1 An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational and Systems Biology University of Pittsburgh School of Medicine 2014 CMACS Winter Workshop Lehman College

2 Cell as Information Processor

3 The cellular brain Original film from David Rogers (Vanderbuilt University)

4 Organization of Signaling Networks Yarden & Sliwkowski, Nature Rev. Mol. Cell Biol. 02: (2001).

5 Ras in network context The Biology of Cancer (© Garland Science 2007)

6 Figure 5.15 The Biology of Cancer (© Garland Science 2007) Initiating Events: Receptor Aggregation

7 Figure 6.12 The Biology of Cancer (© Garland Science 2007) Initiating Events: Complex Formation  “Effector” Activation

8 Ras at Multiple Scales The Biology of Cancer (© Garland Science 2007) >20% human tumors carry Ras point mutations. >90% in pancreatic cancer. >20% human tumors carry Ras point mutations. >90% in pancreatic cancer. Transformed

9 Video of Ras Activation Ras structure and function

10 Ras Structure to Model

11 Ras pi3kral gn sosraf ~GDP ~GTP Sos Ras GAP Ras GAP Raf PI3K Ral

12 Ras Biochemistry to Rules Ras bound to GDP binds to Sos nuc Ras eff + Sos cat RasGEF RasSos Sos binding catalyzes GDP/GTP exchange RasSos RasSos RasGTP binds Raf Ras + Raf Ras Raf RBD

13 BioNetGen Language Formalizes Object- Oriented Description of Biochemistry Ras Sos Ras Raf Molecules SpeciesPatterns Raf Sos(RasGEF) Ras(cat,nuc~GDP~GTP,eff)Raf(RBD) RasSos Sos(RasGEF!1).Ras(cat!1,nuc~GTP) Ras(nuc~GTP,eff!1).Raf(RBD!1)

14 BioNetGen Language Formalizes Object- Oriented Description of Biochemistry Ras Sos Ras Raf Molecules SpeciesPatterns Raf Sos(RasGEF) Ras(cat,nuc~GDP~GTP,eff)Raf(RBD) RasSos By leaving out a component this graph becomes a selector for multiple graphs. Sos(RasGEF!1).Ras(cat!1,nuc~GTP) Ras(nuc~GTP,eff!1).Raf(RBD!1)

15 BioNetGen Language Formalizes Object- Oriented Description of Biochemistry Rules Sos binding catalyzes GDP/GTP exchange RasSos RasSos RasGTP binds Raf Ras + Raf Ras Raf Sos(RasGEF!1).Ras(cat!1,nuc~GDP,eff)-> \ Sos(RasGEF!1).Ras(cat!1,nuc~GTP,eff) k2 Ras(nuc~GTP,eff)+Raf(RBD) Ras(nuc~GTP,eff!1).Raf(RBD!1) kp3,km3

16 “Object-Oriented” Representation of Signaling Molecules IgE(a,a) FceRI(a,b~U~P,g2~U~P) Lyn(U,SH2) Syk(tSH2,lY~U~P,aY~U~P) B IO N ET G EN Language Faeder et al., Meth. Mol. Biol. (2009)http://bionetgen.org

17 Concise and Precise Description of Biochemical Knowledge Transphosphorylation component state change Lyn(U!1).FceRI(b!1).FceRI(b~U)-> \ Lyn(U!1).FceRI(b!1).FceRI(b~P) Rules can query the local environment. Transformation only takes place when conditions are favorable.

18 Composition of a Rule-Based Model MoleculesReaction Rules begin reaction_rules # Ligand-receptor binding 1 Rec(a) + Lig(l,l) Rec(a!1).Lig(l!1,l) kp1, km1 Rec(a) + Lig(l,l) Rec(a!1).Lig(l!1,l) kp1, km1 # Receptor-aggregation 2 Rec(a) + Lig(l,l!1) Rec(a!2).Lig(l!2,l!1) kp2,km2 # Constitutive Lyn-receptor binding 3 Rec(b~Y) + Lyn(U,SH2) Rec(b~Y!1).Lyn(U!1,SH2) kpL, kmL … begin molecules Lig(l,l) Lyn(U,SH2) Syk(tSH2,l~U~P,a~U~P) Rec(a,b~U~P,g~U~P) end molecules BioNetGen language

19 AIM: Model the biochemical machinery by which cells process information (and respond to it). RepresentationSimulation Modeling cell signaling How do we simulate dynamics of signaling networks?

20 Standard Chemical Kinetics Species Reactions

21 Reaction Network Model of Signaling Kholodenko et al., J. Biol. Chem. 274, (1999) EGF EGFR GRB2 SOS EGF EGFR GRB2 SOS SHC

22 Reaction Network Model of Signaling Kholodenko et al., J. Biol. Chem. 274, (1999) 22 species 25 reactions

23 General formulation of chemical kinetics (continuum limit) x is vector of species concentrations S is the “stoichiometry matrix”, S ij = number of molecules of species i consumed by reaction j. v is the “reaction flux vector”, v j is the rate of reaction j. For an elementary reaction,

24 RepresentationSimulation Modeling cell signaling Reaction Network How does set of Molecules and Rules get transformed into a Reaction Network of Species and Reactions?

25 BioNetGen A b Y1 B A(b,Y1) B(a) Molecules are structured objects (hierarchical graphs) a BNGL: Faeder et al., In Methods in Molecular Biology: Systems Biology, Ed. I.V. Maly (2009)

26 BioNetGen A b Y1 B A(b,Y1) B(a) Molecules are structured objects (hierarchical graphs) Rules define interactions (graph rewriting rules) A B + k +1 k -1 A B A(b) + B(a) A(b!1).B(a!1) kp1,km1 a bond between two components a Faeder et al., In Methods in Molecular Biology: Systems Biology, Ed. I.V. Maly (2009) BNGL:

27 Rules generate events A B + k +1 A B Rule1 A b Y1 B a + Reaction1 12

28 Rules generate events A B + k +1 A B Rule1 A b Y1 B a + Reaction1 12

29 Rules generate events A B + k +1 A B Rule1 A b Y1 B a A b B a k +1 + Reaction1 123

30 Rules may specify contextual requirements A b Y1 Rule2 p1p1 A b Y1 P context not changed by rule must be bound A b Y1 B a 3 Reaction2 A(b!+,Y1~U) -> A(b!+,Y1~P) p1 BNGL: context

31 Rules may specify contextual requirements A b Y1 Rule2 p1p1 A b Y1 P context not changed by rule must be bound A b Y1 B a 3 Reaction2 A(b!+,Y1~U) -> A(b!+,Y1~P) p1 BNGL: context

32 Rules may specify contextual requirements A b Y1 Rule2 p1p1 A b Y1 P context not changed by rule must be bound A b Y1 B a 3 Reaction2 p1p1 A b Y1 B a 4 P A(b!+,Y1~U) -> A(b!+,Y1~P) p1 BNGL: context

33 Rules may generate multiple events Second reaction generated by Rule 1 A B + k +1 A B Rule1 A b Y1 B a A b B a k +1 + Reaction3 425 P absence of context P

34 More complex rules Lyn Fc  RI 22  P SH2 p* L  P P Lyn Fc  RI Transphosphorylation of  2 by SH2-bound Lyn Generates 36 reactions (dimer model) with same rate constant Lyn Fc  RI 22 P SH2 p* L  Lyn Fc  RI 22 P SH2 P example

35 Automatic Network Generation Seed Species (4) Reaction Rules (19) New Reactions & Species FcεRI Model Network FcεRI (IgE) 2 Lyn Syk Network

36 Automatic Network Generation Seed Species (4) Reaction Rules (19) FcεRI Model FcεRI (IgE) 2 Lyn Syk 354 Species 3680 Reactions 354 Species 3680 Reactions

37 Automatic Network Generation Seed Species (4) Reaction Rules (19) FcεRI Model FcεRI (IgE) 2 Lyn Syk 354 Species 3680 Reactions 354 Species 3680 Reactions


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