understanding the genetic regulatory networks, the discovery of interactions between genes, and understanding regulatory processes in a cell at the gene level, is one of the major goals of system biology and computational biology. modeling gene regulatory networks, describing the actions of the cells at the molecular level and is used in medicine and molecular biology applications such as metab...

Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture tw...

Reverse engineering genetic regulatory networks (GRNs) is greatly undetermined by the data available. We need to understand the plausibility of a recovered GRN, but little is known about the correlation between matching the target expression vector and recovery of the target GRN. Here, we explore this and related issues and claim that (i) evolved target GRNs are more reliably reconstructed by e...

As the diversity of reverse engineering methods, we will cover four sophisticated promising modeling approaches. The aim of this paper is to obtain a better understanding of approached strengths and weaknesses on the systems biology community. The rest of the paper is organized as following first we describe models selection criteria which were studied in this paper, Second we cover the descrip...

During last two decades, enormous amount of biological data generated by highthroughput analytical methods in biology produces vast patterns of gene activity, highlighting the need for systematic tools to identify the architecture and dynamics of the underlying GRN (He et al. 2009). Here, the system identification problem falls naturally into the category of reverse engineering; a complex genet...

The correct inference of gene regulatory networks (GRN) remains as a fascinating task for researchers to understand the detailed process of complex biological regulations and functions. With availability of large dimensional microarray data, relationships among thousands of genes can be extracted simultaneously that is a reverse engineering problem. Among the different popular models to infer G...

Much effect has been devoted over the past decade to inference of gene regulatory networks (GRNs). However, the previous methods infer GRNs containing large amount of false positive edges, which could result in awful influence on biological analysis. In this study, we present a novel hybrid framework to improve the accuracy of GRN inference. In our method, network topologies from linear and non...

The reverse engineering of gene regulatory network (GRN) is an important problem in systems biology. While gene expression data provide a main source of insights, other types of data are needed to elucidate the structure and dynamics of gene regulation. Epigenetic data (e.g., histone modification) show promise to provide more insights into gene regulation and on epigenetic implication in biolog...

This paper presents an agile reverse engineering process, referred to as PARFAIT/RE, which has been abstracted from the use of a framework-based agile reengineering process, named PARFAIT. The proposition of PARFAIT/RE has been evidenced from an analysis done in a reengineering case study of a medium size system. Several factors collaborate to make PARFAIT/RE agile: a) active participation of l...

Gene regulatory networks (GRNs) represent complex nonlinear coupled dynamical systems that models gene functions and regulations at the system level. Previous research has described GRNs as coupled nonlinear systems under parametric perturbations without considering the important aspect of stochasticity. However, a realistic model of a GRN is that of a hybrid stochastic retarded system that rep...