Analysis And Reconstruction Of Gene Regulatory Networks

As the completion of the multiple genome sequencing projects, the study emphasis of scientists is now changing to a functional understanding of genes and their network interactions. Microarray technology provides bases for our study of large-scale gene experimentation. Using this technology it is possible to find the expression levels of genes across different conditions. Gene network reconstruction is just based on this data to find the inter-dependent relationships between genes. In addition, in order to understand the functions of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent, so on and so forth. The regulation of gene expression is achieved through genetic regulatory systems, which are structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, so it's hard to understand the behavior of their dynamics. So the formal methods and computer tools for the modeling and simulation of genetic regulatory networks is indispensable.This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, such as directed graphs and undirected graphs, linear combination model, weight matrices model, Bayesian networks model, and mutual-information networks model, so on and so forth. This paper investigated the use of a genetic algorithm using a local searching mechanism to reconstruct gene networks from test data. This study demonstrates that the genetic algorithm approach shows promise in that the method can infer gene networks that fit test data closely. In addition, an algorithm called Particle Swarm Optimization (PSO) is introduced to the weight matrices, and somewhat more perfect results are acquired comparing to the Genetic Algorithms (GA). But because of the current bioinformatics, especially the gene regulatory networks analysis and reconstruction are not mature, and evaluating the biological accuracy of predicted networks from currently available test data is mot possible. The best that can be achieved is to produce a set of possible networks to pass to a biologist For experimental verification. But with the development of scientific technology, scientist must be able to form perfect method to evaluate the gene regulatory networks we build.