Principles of Design and Evolution in Intracellular Signaling Networks

1. Principles of Design and Evolution in Intracellular Signaling Networks Jay Mittenthal Dept. of Cell and Structural Biology University of Illinois at Urbana-Champaign

2. The Cell Net metabolism:protein net: proteins metabolites proteins gene net:cell net: proteins proteins metabolites genes genes Aim: To find general principles of design for the cell net.

3. Design of Network Topology • Motivations for understanding design: • Design is aesthetic, aids teaching, aids modification. • Approaches to design: • 1. Evolutionary computation • 2. Reverse engineering • For network dynamics, need • Topology: Connectivity among reactions  • Kinetics: Parameter values (rate constants, …)

4. Intracellular signaling networks of proteins transmit information from receptors to targets. ligand receptor cell membrane process in cytoplasm nucleus gene

5. Why are signaling networks so complicated ?

6. Evolutionary Computation • Goal: Seek the best solution to a problem through variation and selection in a population of alternative solutions. • Our approach: Each cell in a population contains proteins that may form networks. Each protein is a set of domains. • The cells undergo iterated cycles of • mutation, by transfer or deletion of domains; • evaluation of the networks’ fitness; • selection: preferential survival of fitter cells. • Problem: Why do signaling networks use so many reaction steps in long pathways?

7. Evolutionary Computation: Questions • Do long pathways evolve without selection? • Does selection for more pathways favor R1 T1 the evolution of longer pathways? R2 T2 • What pathways maximize information R3 T3 transfer between receptors and targets? R4 T4 • Do long pathways evolve to gate a transition between functional modes? • G O • Aj or Bj • mode 1 mode 2

8. Evolutionary Computation: Results The network that could evolve through the fewest mutations evolved earliest and became predominant in the population. Typically the shortest favorable pathways evolved: RA  A’T The evolution of such networks corresponds to using maximum parsimony (minimum evolution) to reconstruct phylogenetic trees.

9. The evolution of longer pathways must depend on specific selection pressures. Reverse Engineering studies the organization and behavior of a system, to identify the functions for which it may have been selected.

10. Some possible functions of long signaling pathways Signal through several compartments Amplify an initially small signal: Adapter proteins Overlapping redundancy: Modulate the response: rate, adaptation, recovery  Avoid false positives -- output without input. 

17. Strategy for avoiding F+ varies with the kind of F+ to avoid. Discrimination: Strategy AvoidRespond to sigmoid: subthreshold suprathreshold multistep delay: rapid transient slow transient negative feedback: short delay: rapid transient slow transient long delay: slow transient rapid transient AND incomplete complete prerequisites prerequisites

18. Conclusions So Far • Evolutionary computation typically generates the shortest pathways that can connect receptors to targets. • Longer pathways and networks may do various jobs: • Signal through several compartments • Amplify an initially small signal • Provide flexibility through adapter proteins • Provide reliability through overlapping redundancy • Modulate the response: rate, adaptation, recovery • Avoid false positives

19. Hypothesis: A real network tends to be the smallest network that can meet all the selection pressures on its operation.

20. Limited space, time, and coding capacity • favor the smallest network for each job. • A cell must perform many processes with limited resources: • volume; molecules/volume • time for processes (competition; stability of molecules) • coding capacity of DNA (errors in replication) • functionality of proteins (errors in transcription and translation) • A cell can perform more processes faster with smaller networks that use fewer kinds of molecules, in higher concentrations, more closely associated.

21. References Kosorukoff, A. 2001 Modeling of evolution of signaling networks in living cells by evolutionary computation. www-illigal.ge.uiuc.edu/~alex3/thesis.ps Mittenthal, J., B. Clarke, A. Scheeline. 2003. How cells avoid errors in metabolic and signaling networks. Int. J. Modern Physics B 17: 2005-2022.