Volkan Sevim : Modeling Gene Regulatory Networks and Evolution of Genetic Robustness

Robustness to mutations and noise has been shown to be evolvable through stabilizing selection for optimal phenotypes in model gene regulatory networks. The ability to evolve robust mutants is known to depend on the network architecture. How do the dynamical properties and state space structures of these networks with high and low robustness differ? Does selection operate on the global dynamical behavior of the networks? What kind of state space structures are favored by the selection? Using an extensive statistical analysis of state spaces of these model networks and damage-propagation analysis, I show that the change in their dynamical properties due to stabilizing selection for optimal phenotypes is minor. In agreement with recent studies, robustness to noise evolves along with robustness to mutations. Most notably, the networks that are most robust to both mutations and noise are highly chaotic. Certain properties of chaotic systems, such as being able to produce large attractor basins, seem to be useful to maintain a stable gene expression pattern.

• Category: Nonlinear and Complex Systems
• Duration: 01:39:40
• Date:  October 2, 2007 at 2:45 PM
• Views: 152
• Tags:   seminar, CNCS Seminar