Gene expression pattern based controller design for gene regulatory networks

Investigating gene regulatory networks is important for understanding mechanism of cellular functions. Recently, there have been increasing research interests in synthesizing gene regulatory networks and several studies have been done. Synthesizing gene regulatory networks is a complementary approach for understanding functions of gene regulatory networks, and it could be the first step in controlling and monitoring living cells. We have proposed the controller design method for gene regulatory networks, in which the desired behavior for the target gene regulatory network are given by gene expression pattern sequences. In the method, we formulate the controller design problem as an optimization problem and propose a method to solve it by network learning. However, the longer the desired gene expression pattern sequence is, extremely the larger the value of the cost function becomes, which causes numerical problems in solving the optimization problem. In this paper, we propose a controller design method for gene regulatory networks. We formulate the controller design problem as an optimization problem. In the proposed method, we introduce a different cost function which does not cause the numerical problems. We solve the optimization problem by using Particle Swarm Optimization because the cost function is not differentiable.