Synchronous versus asynchronous modeling of gene regulatory networks

Synchronous versus asynchronous modeling of gene regulatory networks

MOTIVATION In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbatio...

Modeling gene regulatory networks: Classical models, optimal perturbation for identification of network

Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption.  On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications.  This is not an unrealistic goal since genes which are regulated by gene regulatory ...

Systems Level Modeling of Gene Regulatory Networks

Inferring Gene Regulatory Networks from Asynchronous Microarray Data

Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little...

Modeling gene regulatory networks using Petri Nets

Petri nets (PNs) are a qualitative approach to modeling the gene regulatory networks (GRNs) capable of characterising the dynamical properties of complex systems while also describing their structure with PN graphs. In this paper we show how to construct basic building blocks for GRNs using PNs. We present solutions to some of the known weaknesses of using conventional PNs for descrbing GRNs by...