An Introduction to Modeling Biochemical Signal Transduction Keynote Lecture 2012 CMACS Winter...
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- Slide 1
- An Introduction to Modeling Biochemical Signal Transduction
Keynote Lecture 2012 CMACS Winter Workshop Lehman College
- Slide 2
- Cell as Information Processor
http://en.wikipedia.org/wiki/Cell_signaling
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- The cellular brain
http://www.biochemweb.org/fenteany/research/cell_migration/neutrophil.html
Original film from David Rogers (Vanderbuilt University)
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- Other examples of cellular decisions
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- Evolution in understanding of cell signaling Linear
pathwayBranched pathway / complexes EGF EGFR GRB2 SOS RAS RAF MAPK
MYC Combinatorial complexityBlack box
- Slide 6
- Organization of Signaling Networks Yarden & Sliwkowski,
Nature Rev. Mol. Cell Biol. 02: 127-137 (2001).
- Slide 7
- Figure 5.15 The Biology of Cancer ( Garland Science 2007)
Initiating Events: Receptor Aggregation
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- Figure 6.12 The Biology of Cancer ( Garland Science 2007)
Initiating Events: Complex Formation Effector Activation
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- Figure 6.9 The Biology of Cancer ( Garland Science 2007)
Complexity of Membrane Complexes
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- 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
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- 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
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- 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
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- Ras Structure to Model
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- Ras pi3kral gn sosraf ~GDP ~GTP Sos Ras GAP Ras GAP Raf PI3K
Ral
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- Figure 6.10a The Biology of Cancer ( Garland Science 2007)
Modularity of Signaling Proteins
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- Figure 6.10b The Biology of Cancer ( Garland Science 2007)
Diversity of Modular Elements
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- Syk Lyn Fc RI Transmembrane Adaptors Wiring of Modules Produces
More Complexity
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- AIM: Model the biochemical machinery by which cells process
information (and respond to it). RepresentationSimulation Modeling
cell signaling
- Slide 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
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- Standard Chemical Kinetics Species Reactions
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- Reaction Network Model of Signaling Kholodenko et al., J. Biol.
Chem. 274, 30169 (1999) EGF EGFR GRB2 SOS EGF EGFR GRB2 SOS
SHC
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- Reaction Network Model of Signaling Kholodenko et al., J. Biol.
Chem. 274, 30169 (1999) 22 species 25 reactions
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- 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,
- Slide 24
- Early events in Fc RI signaling
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- 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
- Slide 26
- Standard modeling protocol 1. Identify components and
interactions. 2. Determine concentrations and rate constants 3.
Write and solve model equations.
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- Combinatorial complexity
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- Addressing combinatorial complexity 354 species / 3680
reactions Standard approach writing equations by hand wont work!
New approach Write model by describing interactions. Automatically
generate the equations. Standard approach writing equations by hand
wont work! New approach Write model by describing interactions.
Automatically generate the equations.
- Slide 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.
- Slide 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)
- Slide 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
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- 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)
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- 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)
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- 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:
- Slide 36
- Rules generate events A B + k +1 A B Rule1 A b Y1 B a +
Reaction1 12
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- Rules generate events A B + k +1 A B Rule1 A b Y1 B a +
Reaction1 12
- Slide 38
- Rules generate events A B + k +1 A B Rule1 A b Y1 B a A b B a k
+1 + Reaction1 123
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- 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
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 44
- Automatic Network Generation Seed Species (4) Reaction Rules
(19) New Reactions & Species FcRI Model Network FcRI (IgE) 2
Lyn Syk Network
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- Automatic Network Generation Seed Species (4) Reaction Rules
(19) FcRI Model FcRI (IgE) 2 Lyn Syk 354 Species 3680 Reactions 354
Species 3680 Reactions
- Slide 46
- Automatic Network Generation Seed Species (4) Reaction Rules
(19) FcRI Model FcRI (IgE) 2 Lyn Syk 354 Species 3680 Reactions 354
Species 3680 Reactions
- Slide 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
- Slide 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.
- Slide 49
- Related Work StochSim Moleculizer Simmune -calculus / -factory
little b Stochastic Simulation Compiler meredys
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 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
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- Kinetic Proofreading in Sports Malcolm Gladwell, Outliers. Born
Jan-Mar Born Oct-Dec 2.5-4 fold!
- Slide 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
- Slide 57
- Evidence for Kinetic Proofreading in Mast Cell Responses to Two
Ligands Torigoe, Inman & Metzger, Science, 281, 568 (1998)
Input
- Slide 58
- Evidence for Kinetic Proofreading in Mast Cell Responses to Two
Ligands Torigoe, Inman & Metzger, Science, 281, 568 (1998)
Input Outputs
- Slide 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
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- Large number of reaction events required for Syk
activation
- Slide 61
- Small number of reaction events required for receptor
phosphorylation
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- 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
- Slide 63
- Bimodal dose-response curves B. Goldstein, in Theoretical
Immunology, Part One, Ed. A. S. Perelson
- Slide 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)
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- Dose-response curves for reversibly binding ligand high Syk low
Syk
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- The multivalent scaffold effect *Syk and scaffold
concentrations are equal *
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- Bimodal response occurs when Syk concentration below maximal
number of aggregated receptors R agg = Syk tot
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- Limits of the network generation approach Extending model to
include Lyn regulation results in >20,000 states.
- Slide 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)
- Slide 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.
- Slide 71
- Network-free: A kinetic Monte Carlo approach to simulating
rule-based models Michael Sneddon Yang et al., Phys. Rev E
(2008)
- Slide 72
- NFsim: General implementation of Network-free algorithm
Sneddon, Faeder, and Emonet, in preparation.
- Slide 73
- Cell and Population Level Behavior Molecular Level Interactions
Goal: Multiscale Agent-based, simulation of biological systems,
building up from the stochastic molecular level
- Slide 74
- Complexity in Chemotaxis Signaling Receptor aggregation makes
simulation difficult
- Slide 75
- NFsim NFsim can be embedded into other higher level agents
- Slide 76
- Digital Chemotaxis Experiments 200 E. coli Cells 2mm from
Capillary 10mM Attractant 40 min simulation
- Slide 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.