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

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?

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

Phosphorylation Cycle

Phosphorylation Cycle

Phosphorylating kinase

Dephosphorylating phosphatase

Chemical “Transistor”Chemical “Transistor”

• Kinase concentration = input

• Equilibrium concentration of E-P:

• Phosphorylation acts as switch

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

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)

Chemotaxis E. coli (2)Chemotaxis E. coli (2)

Signaling network for chemotaxis in E. coli

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

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)

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

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”?

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

Elementary Algebraic Operations

Elementary Algebraic Operations

Addition

Multiplication

Subtraction

Division

nth Root

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

Example: ABC Formula

Example: ABC Formula

“Solves”

Example: PolynomialExample: Polynomial

Network computes

Algebra of phosphorylation cycles?

Algebra of phosphorylation cycles?

?

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

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)

Questions?Questions?