Principles of Design and Evolution in Intracellular Signaling Networks
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Principles of Design and Evolution in Intracellular Signaling Networks
Jay Mittenthal
Dept. of Cell and Structural BiologyUniversity of Illinois at Urbana-Champaign
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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.
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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, …)
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genenucleus
cellmembrane
ligand
receptor
process incytoplasm
Intracellular signaling networks of proteins transmit information from receptors to targets.
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Why
are
signaling
networks
so
complicated
?
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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?
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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
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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.
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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.
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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.
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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.
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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.
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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.
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Multistep delay can reduceresponsiveness to a rapid transient
Kinetic proofreading[McKeithan (1995) PNAS 92, 5042]
True ligand:
False ligand:
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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.
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Requiring a conjunction of inputs(AND) reduces responsiveness to
single inputs.
or
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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
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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
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Hypothesis: A real network tends to be the smallest network that can meet all the selection pressures on its operation.
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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.
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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.