Identifying Gene Regulatory Network as Differential Equation by Genetic Programming
Erina Sakamoto, Hitoshi Iba · 2000
This paper proposes an evolutionary method of identifying the gene regulatory network represented as a differential equation system. As the technology in DNA micro arrays has developed, large quantities of gene's expression data are becoming more available. As a result, it is essential to get information as to the gene regulatory network from the observed data of gene's expression. Among many proposed models to describe a gene network, we have chosen the di#erential equation system since it can represent complex relations among components. In the previous studies [1], the form of the di#erential equation is being fixed during the learning so that the ultimate goal of the identification is to optimize parameters, i.e., coe#cients, in the fixed equation. On the other hand, for the sake of the flexibility of the model, we allow an arbitrary form of functions in the right-hand side of the differential equation (eq. (1)). dX i /dt =