cell signaling networks from the bottom up anthony m.l. liekens biomodeling and bioinformatics...
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Cell Signaling Networks
From the Bottom Up
Cell Signaling Networks
From the Bottom Up
Anthony M.L. Liekens
BioModeling and BioInformatics
Anthony M.L. Liekens
BioModeling and BioInformatics
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ESIGNETESIGNET• European NEST
project with Birmingham, Dublin, Jena
• Signal transduction pathways
• Black box models of conceptual networks
• Computational properties?
• Evolvability?
• European NEST project with Birmingham, Dublin, Jena
• Signal transduction pathways
• Black box models of conceptual networks
• Computational properties?
• Evolvability?
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Signal TransductionSignal Transduction
• Most proteins known for metabolic processes, cell maintenance
• Many proteins responsible for
• transduction of signals
• information processing
• Estimated 5% of human genes
• Elementary and common motif: Phosphorylation cycle
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Phosphorylation Cycle
Phosphorylation Cycle
Phosphorylating kinase
Dephosphorylating phosphatase
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Chemical “Transistor”Chemical “Transistor”
• Kinase concentration = input
• Equilibrium concentration of E-P:
• Phosphorylation acts as switch
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Signaling NetworksSignaling Networks
• Phosphorylation cycle is elementary motif that acts as transistor
• Phosphorylated protein catalyzes other phosphorylations
• Cascading networks of cycles allow for the implementation of “computations”
• Small example: Chemotaxis
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Chemotaxis of E. coli (1)
Chemotaxis of E. coli (1)
• Receptors sample environment
• Chemotaxis controls actuators
• Cell moves to higher concentrations in nutritional gradient
(Bray et al, Computational Cell Group, University of Cambridge)
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Chemotaxis E. coli (2)Chemotaxis E. coli (2)
Signaling network for chemotaxis in E. coli
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Higher OrganismsHigher Organisms• Networks may
compromise >80 kinases and phosphatases
(Gomperts et al, Signal Transduction, 2002)
• Increasing complexity and feedback
➡ hard to infer knowledge
• Numerous applications(Kitano, Science, 2000)
Responses to inflammation
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Modular ApproachModular Approach
• Recognize small, common motifs
➡ behavior is mathematically comprehensive
• Replace motif by “super node” that acts similarly
• Hierarchical integration leads to understanding of complex networks
(Kholodenko et al, FEBS Letters, 1995; Weng et al, Science, 1999; Hartwell et al, Nature, 1999; Kholodenko et al, Topics in Current Genetics, 2005)
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Observed BehaviorsObserved Behaviors
• Boolean operations and simple binary computations
• Integration and amplification of signals
• Bandpass frequency and noise filters
• Bistable switches, oscillators and hysteresis through feedback
• Neural networks(Wolf and Arkin, Current Opinion in Microbiology, 2003)
• Related body of work in gene expression
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Bottom-up ApproachBottom-up Approach
• Construct conceptual motifs from the bottom up, rather than dissecting real networks from the top down
• What elementary mathematical operations can be represented as reaction networks?
• What kind of functions can we construct out of these?
• Are these networks “evolvable”?
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Elementary MotifElementary Motif
• A catalyzes production of X, (rate constant k1) with abundant resources
• X decays (k2) to waste
• ODE model with mass-action kinetics
• If k1 = k2, [ X ] = [ A ] in equilibrium
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Elementary Algebraic Operations
Elementary Algebraic Operations
Addition
Multiplication
Subtraction
Division
nth Root
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Complex Computations
Complex Computations
• Elementary operations can be combined
• Output of one network serves as the input of the next network
• Second network does not influence first, but is dependent on it
• Equilibrium state = composed function
• Allows more complex computations
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Example: ABC Formula
Example: ABC Formula
“Solves”
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Example: PolynomialExample: Polynomial
Network computes
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Algebra of phosphorylation cycles?
Algebra of phosphorylation cycles?
?
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Ongoing ResearchOngoing Research• Behavior of elementary operations,
dropping assumptions
• Feedback mechanisms
• In silico evolution of such networks
• Stochastic models
• Molecular dynamics simulations
• Verification of signaling networks
• Bring understanding to real problems
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People InvolvedPeople Involved
• Peter Hilbers (PI)
• Huub ten Eikelder (UD)
• Dragan Bosnacki (UD)
• Anthony Liekens (Postdoc)
• Marvin Steijaert (AiO)
• Harm Buisman (thesis, finished)
• Jeroen van den Brink (thesis)
• Sander Allon (internship)
• Sjoerd Crijns (internship)
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Questions?Questions?