Computational approaches to understanding biology and bio-inspired problem-solving
- When?
- Friday 12 November 2010, 16:00 to 17:00
- Where?
- 22AA04
- Open to:
- Staff, Students
- Speaker:
- Yaochu Jin (Surrey, Computing)
Abstract: This talk starts with a brief introduction to computational models of gene regulatory networks (GRN), followed by a description of our recent results on analyzing and synthesizing gene regulatory motifs, particularly from the robustness and evolvability perspective. We show that in a feedforward Boolean network, the trade-off between robustness and evolvability cannot be resolved. In contrast, how this trade-off can be resolved in an ODE-based GRN model for cellular growth based on a quantitative evolvability measure. In addition, we demonstrate that robust GRN motifs can emerge from in silico evolution without an explicit selection pressure on robustness. Our results also suggest that evolvability is evolvable without explicit selection.
In the second part of the talk, I will present two examples on how these GRN models can be applied to understanding biology and to solving engineering problems. The long-term target of the first example is to understand the major transitions in evolution of primitive nervous systems. A cellular growth model is developed under the control of a gene regulatory network, which is used for mophological development. We showed that there is a close coupling between the evolved body plan and the control pattern. In the second example, we showed that principles extracted from biological morphogenesis can be employed to solve engineering problems, such as self-organization of multi-robot systems.
