|InterJournal Complex Systems, 1278
|Manuscript Number: |
Submission Date: 2004
|Evolution of Gene Regulatory Networks: Growth and Dynamics|
We investigate how dynamic gene regulatory networks emerge in evolution. Our model combines network growth and dynamical genotype-phenotype mappings in complex environments. We unify ideas from complex networks, nonlinear dynamics and evolution in fitness landscapes to show the emergence of scale-free topology, canalizing functions, and self-organized criticality in gene regulatory networks. To examine this, we constructed a biologically justified simulation model that utilizes artificial genomes, Boolean dynamics, and NK-fitness landscapes. The genome encodes a Boolean network, whose attractors (transcribed proteins) are used to determine the organism fitness. The simulations show that gene regulatory networks self-organize to a stable regime close to the critical threshold. Canalizing functions, especially those that can form forcing structures, are dominant and govern the dynamic behaviour. The network output distribution is scale-free, while the inputs follow the exponential distribution. With our model we are able to find support for existing theories on the topology and dynamics of gene regulatory networks. In addition, we could find patterns of self-organization that would need further biological verification. In conclusion, we found that growth and dynamics are inseparable in the development of regulatory networks.
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