Abstract
The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.
Highlights
With the rapid advancement of technologies such as RNA-seq using generation sequencers, it has become possible to measure the expression levels of thousands of genes
The proposed method divided the parameter estimation problem of the target network here into 4 subproblems, each of which is defined as a two-dimensional function optimization problem
As we think that the averaged gene expression levels weaken the effect of the intrinsic noise, the method proposed in this study infers genetic networks without considering it
Summary
With the rapid advancement of technologies such as RNA-seq using generation sequencers, it has become possible to measure the expression levels of thousands of genes. These data implicitly contain enormous amounts of information on biological systems. The inference of genetic networks is considered a promising approach for extracting useful information from these data. In the genetic network inference, the information is extracted by inferring mutual interactions among genes from the time-series of the gene expression levels. Many researchers have taken an interest in the inference of genetic networks, and the development of this methodology has become a major topic in the field of bioinformatics and systems biology
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