In recent years, base sequences have been increasingly unscrambled through attempts represented by the human genome project. Accordingly, the estimation of the genetic network has been accelerated. However, no definitive method has become available for drawing a large e#ective graph.

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

___________________________________________________________________________________ System biology is a relatively new branch of molecular biology. In this field, genes are studied individually and separately. Instead, we are interested in how the genes interact with one another. Since gene interactions are rather complicated and it is not easy at all to find out how the genes influence each other, this field is still in its primitive stage. In this thesis, we apply symbolic logic to the problem. That is, we use Boolean logic formulas to describe how genes activate or inhibit other genes. Usually, based upon experimental results, we can use a Boolean gene regulatory network to describe

In recent years, base sequences have been increasingly unscrambled through attempts represented by the human genome project. Accordingly, the estimation of the genetic network has been accelerated. However, no definitive method has become available for drawing a large effective graph. This paper proposes a method which allows for coping with an increase in the number of nodes by laying out genes on planes of several layers and then overlapping these planes. This layout involves an optimization problem which requires maximizing the fitness function. To demonstrate the effectiveness of our approach, we show some graphs using actual data on 82 genes and 552 genes. We also describe how to lay out nodes by means of stochastic searches, e.g., stochastic hill-climbing and incremental methods. The experimental results show the superiority and usefulness of two search methods in comparison with the simple random search.

Abstract The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.—Ghazalpour,

Abstract—A gene network gives the knowledge of the regulatory relationships among the genes. Each gene has its activators and inhibitors that regulate its expression positively and negatively respectively. Genes themselves are believed to act as activators and inhibitors of other genes. They can even activate one set of genes and inhibit another set. Identifying gene networks is one of the most crucial and challenging problems in Bioinformatics. Most work done so far either assumes that there is no time delay in gene regulation or there is a constant time delay. We here propose a Dynamic Time-Lagged Correlation Based Method (DTCBM) to learn the gene networks, which uses time-lagged correlation to find the potential gene interactions, and then uses a post-processing stage to remove false gene interactions to common parents, and finally uses dynamic correlation thresholds for each gene to construct the gene network. DTCBM finds correlation between gene expression signals shifted in time, and therefore takes into consideration the multi time delay relationships among the genes. The implementation of our method is done in MATLAB and experimental results on Saccharomyces cerevisiae gene expression data and comparison with other methods indicate that it has a better performance. Keywords—Activators, correlation, dynamic time-lagged correlation based method, inhibitors, multi-time delay gene network. I.