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...

Genes communicate with each other through different regulatory effects, which lead to the emergence of complex network structures in cells, and such are expected be for normal cancerous cells. To study these differences, we have investigated Gene Regulatory Network (GRN) cells as inferred from RNA-sequencing data. The GRN is a signed weighted corresponding inductive or inhibitory interactions. ...

Epithelial-to-Mesenchymal Transition (EMT) plays a key role in epithelial-cancer. The state trajectory of its underlying Gene Regulatory Network (GRN) includes three fixed-point attractors characterizing epithelial, senescent, and mesenchymal, cell phenotypes, which implies specific cell-to-cell cell-to-tissue interactions. interplay between the GRN driving EMT one regulating Mammalian Cell Cyc...

Explanation of a process of development must ultimately be couched in the terms of the genomic regulatory code. Specification of an embryonic cell lineage is driven by a network of interactions among genes encoding transcription factors. Here, we present the gene regulatory network (GRN) that directs the specification of the skeletogenic micromere lineage of the sea urchin embryo. The GRN now i...

Gene Ontology (GO) provides a controlled vocabulary and hierarchy of terms to facilitate the annotation of gene functions and molecular attributes. Given a set of genes, a Gene Ontology Network (GON) can be constructed from the corresponding GO annotations and semantic relations among GO terms. Transitive rules can be applied to GO semantic relations to infer transitive regulations among genes....

The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-...

As a reverse engineering field, reconstructing a Gene Regulatory Network (GRN) from time series gene data has been a challenging issue in bioinformatics. This paper proposes a novel engineering framework that infers and reconstructs a gene regulatory network in terms of regulatory accuracy. Different from other statistical methods, the proposed framework uses features that represent the charact...

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...

Gene expression evolution through gene regulatory network (GRN) changes has gained appreciation as a driver of morphological evolution. However, understanding how GRNs evolve is hampered by finding relevant cis-regulatory element (CRE) mutations, and interpreting the protein-DNA interactions they alter. We investigated evolutionary changes in the duplicated Bric-à-brac (Bab) transcription facto...