Inference of S-system Models of Genetic Networks from Noisy Time-series Data

Abstract

In this paper, we propose a new method for the inference of S-system models of large-scale genetic networks from the observed time-series data of gene expression patterns. The proposed method employs a technique to decompose the genetic network inference problem into several subproblems. The S-system parameters are estimated by solving these decomposed subproblems. In addition, the proposed method estimates the initial levels of the gene expression. The estimation of the initial gene expression levels is necessary when the noisy time-series data are given. We verify the effectiveness of the proposed method through the genetic network inference problems.