Principles of Design and Evolution in Intracellular Signaling Networks

Jay Mittenthal

Dept. of Cell and Structural BiologyUniversity of Illinois at Urbana-Champaign

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.

Design of Network TopologyMotivations 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, …)

genenucleus

cellmembrane

ligand

receptor

process incytoplasm

Intracellular signaling networks of proteins transmit information from receptors to targets.

Why

are

signaling

networks

so

complicated

?

Evolutionary ComputationGoal: 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?

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

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.

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.

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.

Causes of a false positive (F+) response

Physiological fluctuations in a functional network:

True ligands at low concentration; noise

False ligands with some affinity for networkmolecules

Transient of inappropriate duration

Mutation in the network can produce aconstitutive response.

A sigmoid response curvereduces the probability of response

to a low-amplitude input

Hyperbolic (Michaelis-Menten kinetics)

% max T response F

ligand conc.

Sigmoid (Ultrasensitivity)

% max T response F

ligand conc.

Various mechanisms give a sigmoid.

[Ferrell (1996) Trends. Biochem. Sci. 21, 460]

Allostery:

Dual phosphorylation: 2 sigmoid, 1 hyperbolic

A BPP CPP A BP CP BP CP vs.

B C B C

Inhibition:

% max response

stimulus conc.

Multistep delay can reduceresponsiveness to a rapid transient

Kinetic proofreading[McKeithan (1995) PNAS 92, 5042]

True ligand:

False ligand:

Negative feedback can modulateresponsiveness to transients

with short delay, may reduce responsiveness to arapid transient.

response of to

time

response of to external ligand time

with long delay, may quench a slow transient.

Requiring a conjunction of inputs(AND) reduces responsiveness to

single inputs.

or

Strategy for avoiding F+ varies with the kind of F+ to avoid.

Discrimination:Strategy Avoid Respond 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

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

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

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.

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.