Exact and heuristic methods for network completion for time-varying genetic networks.

Natsu Nakajima,Tatsuya Akutsu

doi:10.1155/2014/684014

Abstract

Robustness in biological networks can be regarded as an important feature of living systems. A system maintains its functions against internal and external perturbations, leading to topological changes in the network with varying delays. To understand the flexibility of biological networks, we propose a novel approach to analyze time-dependent networks, based on the framework of network completion, which aims to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We have developed a novel network completion method for time-varying networks by extending our previous method for the completion of stationary networks. In particular, we introduce a double dynamic programming technique to identify change time points and required modifications. Although this extended method allows us to guarantee the optimality of the solution, this method has relatively low computational efficiency. In order to resolve this difficulty, we developed a heuristic method for speeding up the calculation of minimum least squares errors. We demonstrate the effectiveness of our proposed methods through computational experiments using synthetic data and real microarray gene expression data. The results indicate that our methods exhibit good performance in terms of completing and inferring gene association networks with time-varying structures.

Highlights

Computational analysis of gene regulatory networks is an important topic in systems biology
We present two novel methods for the completion and inference of time-varying networks using dynamic programming and least squares fitting (DPLSQ): DPLSQ-TV (DPLSQ-TV was presented in a preliminary version of this paper [25]; in this paper, more detailed computational experiments are performed and DPLSQ-HS is newly introduced) and DPLSQ-HS, where TV and HS stand for time varying and heuristics
In order to evaluate the potential effectiveness of DPLSQ-TV and DPLSQ-HS, we begin with network completion for time-varying networks using artificial data

Summary

Introduction

Computational analysis of gene regulatory networks is an important topic in systems biology. A gene regulatory network is a collection of genes and their correlations and causal interactions. Gene regulatory networks play important roles in cells. Deciphering gene regulatory network structures is important for understanding cellular systems, which might be useful for the prediction of adverse effects of new drugs and the detection of target genes for the development of new drugs. In Boolean networks, the state of each gene is simplified into 0 or 1 and the gene regulation rules are given as Boolean functions, where 0 and 1 mean that a gene is active (in high expression) and inactive (in low expression), respectively. In Bayesian networks, the states of genes are usually classified into discrete values and the gene regulation rules are given

Methods

Results

Conclusion