Download presentation
Presentation is loading. Please wait.
Published byMaryann Hudson Modified over 9 years ago
1
An Introduction to Modeling Biochemical Signal Transduction Keynote Lecture 2012 CMACS Winter Workshop Lehman College
2
Cell as Information Processor http://en.wikipedia.org/wiki/Cell_signaling
3
The cellular brain http://www.biochemweb.org/fenteany/research/cell_migration/neutrophil.html Original film from David Rogers (Vanderbuilt University)
4
Other examples of cellular decisions
5
Evolution in understanding of cell signaling Linear pathwayBranched pathway / complexes EGF EGFR GRB2 SOS RAS RAF MAPK MYC Combinatorial complexityBlack box
6
Organization of Signaling Networks Yarden & Sliwkowski, Nature Rev. Mol. Cell Biol. 02: 127-137 (2001).
7
Figure 5.15 The Biology of Cancer (© Garland Science 2007) Initiating Events: Receptor Aggregation
8
Figure 6.12 The Biology of Cancer (© Garland Science 2007) Initiating Events: Complex Formation “Effector” Activation
9
Figure 6.9 The Biology of Cancer (© Garland Science 2007) Complexity of Membrane Complexes
10
Figure 6.9 The Biology of Cancer (© Garland Science 2007) The “curse” of complexity Number of States 3 3 3 4 3 4 3 3,888 7,560,216 Monomers Dimers
11
Figure 6.9 The Biology of Cancer (© Garland Science 2007) The “curse” of complexity Number of States 3 3 3 4 3 4 3 3,888 7,560,216 Monomers Dimers Number of States 3 4 3 6 6 3 5 19,440 188,966,520
12
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
13
Ras Structure to Model
14
Ras pi3kral gn sosraf ~GDP ~GTP Sos Ras GAP Ras GAP Raf PI3K Ral
15
Figure 6.10a The Biology of Cancer (© Garland Science 2007) Modularity of Signaling Proteins
16
Figure 6.10b The Biology of Cancer (© Garland Science 2007) Diversity of Modular Elements
17
Syk Lyn Fc RI Transmembrane Adaptors Wiring of Modules Produces More Complexity
18
AIM: Model the biochemical machinery by which cells process information (and respond to it). RepresentationSimulation Modeling cell signaling
19
Goals Knowledge representation Predictive understanding ◦ Different stimulation conditions ◦ Protein expression levels ◦ Manipulation of protein modules ◦ Site-specific inhibitors Mechanistic insights ◦ Why do signal proteins contain so many diverse elements? ◦ How do feedback loops affect signal processing? Drug development ◦ New targets ◦ Combination therapies
20
Standard Chemical Kinetics Species Reactions
21
Reaction Network Model of Signaling Kholodenko et al., J. Biol. Chem. 274, 30169 (1999) EGF EGFR GRB2 SOS EGF EGFR GRB2 SOS SHC
22
Reaction Network Model of Signaling Kholodenko et al., J. Biol. Chem. 274, 30169 (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
Early events in Fc RI signaling
25
Syk activation model Mol. Immunol.,2002 J. Immunol., 2003 Key variables ligand properties protein expression levels multiple Lyn-FceRI interactions transphosphorylation Key variables ligand properties protein expression levels multiple Lyn-FceRI interactions transphosphorylation
26
Standard modeling protocol 1. Identify components and interactions. 2. Determine concentrations and rate constants 3. Write and solve model equations.
27
Combinatorial complexity
29
Addressing combinatorial complexity 354 species / 3680 reactions Standard approach – writing equations by hand – won’t work! New approach Write model by describing interactions. Automatically generate the equations. Standard approach – writing equations by hand – won’t work! New approach Write model by describing interactions. Automatically generate the equations.
30
Rule-based modeling protocol 1. Define components as structured objects and interactions as rules. 2. Determine concentrations and rate constants 3. Generate and simulate the model.
31
Rule-based modeling protocol 1. Define components as structured objects and interactions as rules. 2. Determine concentrations and rate constants 3. Generate and simulate the model. Objects and rules B IO N ET G EN Reaction Network ODE Solver Stochastic Simulator (Gillespie) Output http://bionetgen.org Faeder, Blinov, and Hlavacek, Methods Mol. Biol. (2009)
32
Defining 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
33
Defining Interaction Rules IgE(a,a)+ FceRI(a) IgE(a,a!1).FceRI(a!1) … B IO N ET G EN Language binding and dissociation Transphosphorylation component state change Lyn(U!1).FceRI(b!1).FceRI(b~U)-> \ Lyn(U!1).FceRI(b!1).FceRI(b~P)
34
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)
35
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:
36
Rules generate events A B + k +1 A B Rule1 A b Y1 B a + Reaction1 12
37
Rules generate events A B + k +1 A B Rule1 A b Y1 B a + Reaction1 12
38
Rules generate events A B + k +1 A B Rule1 A b Y1 B a A b B a k +1 + Reaction1 123
39
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
40
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
41
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
42
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
43
More complex rules Lyn Fc RI 22 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 22 P SH2 p* L Lyn Fc RI 22 P SH2 P example
44
Automatic Network Generation Seed Species (4) Reaction Rules (19) New Reactions & Species FcεRI Model Network FcεRI (IgE) 2 Lyn Syk Network
45
Automatic Network Generation Seed Species (4) Reaction Rules (19) FcεRI Model FcεRI (IgE) 2 Lyn Syk 354 Species 3680 Reactions 354 Species 3680 Reactions
46
Automatic Network Generation Seed Species (4) Reaction Rules (19) FcεRI Model FcεRI (IgE) 2 Lyn Syk 354 Species 3680 Reactions 354 Species 3680 Reactions
47
AIM: Model the biochemical machinery by which cells process information (and respond to it). RepresentationSimulation Modeling cell signaling B IO N ET G EN Language kappa etc. ODE, PDE Stochastic Simulation Algorithm Kinetic Monte Carlo Brownian dynamics
48
Advantages of Formal Representations Precise interaction-based language for biochemistry – knowledge representation Concise representation of combinatorially complex systems Documentation and model readability Modularity and reusability Accuracy and rigor Hlavacek et al. (2006) Sci. STKE, 2006, re6.
49
Related Work StochSim Moleculizer Simmune -calculus / -factory little b Stochastic Simulation Compiler meredys …
50
Systems Modeled IgE Receptor (Fc RI) – Faeder et al. J. Immunol. (2003) – Goldstein et al. Nat. Rev. Immunol. (2004) – Torigoe et al., J. Immunol. (2007) – Nag et al., Biophys. J., (2009) [LAT] Receptor aggregation – Yang et al., Phys. Rev. E (2008) Growth Factor Receptors, other – Blinov et al. Biosyst. (2006) [EGFR] – Barua et al. Biophys. J. (2006) [Shp2] – Barua et al. J. Biol. Chem. (2008) [PI3K] – Barua et al., PLoS Comp. Biol (2009). [GH / SH2B] Carbon Fate Maps – Mu et al., Bioinformatics (2007) TCR ( Lipniacki, J. Theor. Biol., 2008 ) TLR4 (An & Faeder, Math. Biosci., 2009) See http://bionetgen.org for complete list.http://bionetgen.org
51
T. W. McKeithan, PNAS, 92, 5042-5046 (1995). k p k on k off k k k k B 2 B 1 B N-1 B N B 0 k p k p k p k p... receptor + ligand Modifications Ligand dissociation rate can determine ligand efficacy Kinetic Proofreading in Receptor Signaling Signal See also Chapter 9 of Alon, Introduction to Systems Biology
52
T. W. McKeithan, PNAS, 92, 5042-5046 (1995). k p k on k off k k k k B 2 B 1 B N-1 B N B 0 k p k p k p k p... receptor + ligand Modifications Ligand dissociation rate can determine ligand efficacy Kinetic Proofreading in Receptor Signaling Signal
53
T. W. McKeithan, PNAS, 92, 5042-5046 (1995). k p k on k off k k k k B 2 B 1 B N-1 B N B 0 k p k p k p k p... receptor + ligand Modifications Ligand dissociation rate can determine ligand efficacy Kinetic Proofreading in Receptor Signaling Signal Enhancement ratio
54
Kinetic Proofreading in Sports Malcolm Gladwell, Outliers. Many sports (and education systems) have cutoff dates to establish eligibility Having a birthdate close to the cutoff date confers a small but tangible advantage 12345NYear Probability to make the cut (p I /p IV ) N …
55
Kinetic Proofreading in Sports Malcolm Gladwell, Outliers. Born Jan-Mar Born Oct-Dec 2.5-4 fold!
56
T. W. McKeithan, PNAS, 92, 5042-5046 (1995). k p k on k off k k k k B 2 B 1 B N-1 B N B 0 k p k p k p k p... receptor + ligand Modifications Ligand dissociation rate can determine ligand efficacy Kinetic Proofreading in Receptor Signaling Signal Output state of Syk activation model
57
Evidence for Kinetic Proofreading in Mast Cell Responses to Two Ligands Torigoe, Inman & Metzger, Science, 281, 568 (1998) Input
58
Evidence for Kinetic Proofreading in Mast Cell Responses to Two Ligands Torigoe, Inman & Metzger, Science, 281, 568 (1998) Input Outputs
59
Evidence for Kinetic Proofreading in Mast Cell Responses to Two Ligands Ligand with shorter dwell time gives low Syk phosphorylation Torigoe, Inman & Metzger, Science, 281, 568 (1998) Input Outputs
60
Large number of reaction events required for Syk activation
61
Small number of reaction events required for receptor phosphorylation
62
Rapid fall in efficiency of Syk phosphorylation Goldstein et al. (2004) Nat. Rev. Immunol. 4, 445-456. Kinetic proofreading of Syk activation but not receptor phosphorylation Ligand dissociation rate (“off rate”) Fraction of aggregated receptors
63
Bimodal dose-response curves B. Goldstein, in Theoretical Immunology, Part One, Ed. A. S. Perelson
64
Bimodal dose-response curves B. Goldstein, in Theoretical Immunology, Part One, Ed. A. S. Perelson Syk expression is highly variable in human basophils (5,000-60,000 copies per cell) MacGlashan (2007) Syk expression is highly variable in human basophils (5,000-60,000 copies per cell) MacGlashan (2007)
65
Dose-response curves for reversibly binding ligand high Syk low Syk
66
The multivalent scaffold effect *Syk and scaffold concentrations are equal *
67
Bimodal response occurs when Syk concentration below maximal number of aggregated receptors R agg = Syk tot
68
Limits of the network generation approach Extending model to include Lyn regulation results in >20,000 states.
69
Limits of the network generation approach Extending model to include Lyn regulation results in >20,000 states. LAT may form large oligomers under physiological conditions. Houtman et al., Nat. Struct. Mol. Biol. (2006) Nag et al., Biophys. J. (2009)
70
Limits of the network generation approach Extending model to include Lyn regulation results in >20,000 states. LAT may form large oligomers under physiological conditions. Many more components are still missing.
71
“Network-free”: A kinetic Monte Carlo approach to simulating rule-based models Michael Sneddon Yang et al., Phys. Rev E (2008)
72
NFsim: General implementation of Network-free algorithm Sneddon, Faeder, and Emonet, in preparation.
73
Cell and Population Level Behavior Molecular Level Interactions Goal: Multiscale Agent-based, simulation of biological systems, building up from the stochastic molecular level
74
Complexity in Chemotaxis Signaling Receptor aggregation makes simulation difficult
75
NFsim NFsim can be embedded into other higher level agents
76
Digital Chemotaxis Experiments 200 E. coli Cells 2mm from Capillary 10mM Attractant 40 min simulation
77
Conclusions Kinetics and stoichiometry of complex formation can have a profound effect in signal transduction. Modeling these effects requires a new approach to modeling that addresses the issue of combinatorial complexity. Rule-based (or interaction-based) modeling is such an approach. Network-free simulation is a powerful technique that circumvents combinatorial complexity.
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.