Abstract
Advances in microarray technology have resulted in an exponential rise in gene expression data. Partially as a result of this, full genome sequences have been reported for many organisms. In addition several methods have been developed (a) for assigning functionality to previously unknown genes and (b) for measuring the output (i.e. gene expression) of the gene regulatory network. The knowledge of the gene regulatory network further gives insights about gene pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. Also, gene regulatory networks allow comparison of expression patterns of many uncharacterized genes; this comparison provides clues to gene function. A variety of models (such as neural networks, Boolean networks, and Bayesian) have been proposed in recent times. Although each of these models have individual strengths, none of them addresses important issues such as time delay, or make use of available biological information. In the work presented here we demonstrate that networks can efficiently model natural biological processes, specifically gene regulatory systems. Through the modeling approach, we have inferred gene regulatory networks using a time course data set for (a) lambda bacteriophage infection, (b) osteoblast study, and (c) rat central nervous system (CNS) development. The results compare favorably with experimental results from the literature.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.