Evolution of evolvability in gene regulatory networks
Anton Crombach1, Paulien Hogeweg
1Theoretical Biology and Bioinformatics Group, Utrecht University, The Netherlands. a.b.m.crombach@uu.nl
Plos Computational Biology
|July 12, 2008
Summary
Evolutionary models show that gene regulatory networks adapt to changing environments by becoming more efficient at generating beneficial mutations. This process enhances evolvability while maintaining robustness.
Area of Science:
- Systems Biology
- Evolutionary Biology
- Genetics
Background:
- Gene regulatory networks (GRNs) integrate cellular and environmental signals to control gene expression.
- Understanding GRN evolution, evolvability, and robustness is a key challenge in theoretical biology.
- Previous models explored GRN dynamics, topology, and evolvability but not their evolutionary origins.
Purpose of the Study:
- To investigate how gene regulatory networks evolve and become more evolvable over time.
- To model the long-term evolution of GRNs in populations adapting to changing environments.
Main Methods:
- An individual-oriented evolutionary model simulating Darwinian selection.
- Genomes encode GRNs; mutations (duplications, deletions) alter genomes and network structure.
- Simulated populations evolving in a time-varying environment, with fitness determined by gene expression patterns.
Main Results:
- Long-term evolution in changing environments significantly increases the efficiency of generating beneficial mutations.
- Populations evolve towards genotype-phenotype maps enabling coordinated network-wide gene expression changes via few mutations.
- Mutations affecting network hubs or their direct influencers are key; mutational robustness is maintained throughout evolution.
Conclusions:
- Evolution in alternating environments drives the development of gene regulatory networks with enhanced evolvability.
- These evolved networks are sensitive to beneficial mutations while remaining robust to most others.
- This study provides an example of the evolution of evolvability in complex biological systems.
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