Protein interaction networks describe communication and signaling networks where the basic reaction is between two proteins or more. The genetic regulatory network is used to represent the general interaction of genes, gene products, and small molecules. It describes the pathway of gene expression regulation as well as decisions used to turn genes on/off. Deciphering interaction networks is an important task in the post-genomics era. To build genetic networks, one of the hardest problems is the dimensionality issue, which is the exponential number of potential Apoptosis Activator 2 connections among genes. Current solutions include clustering co-regulated genes via unsupervised analysis. The computing methods involve choosing robust mathematical formalisms for inferring the causal connections between genes etc. Bayesian methods are excellent approaches to infer relationship between genes. They rely on prior information concerning genes, however, and it is difficult to analyze gene expression at the whole genome level due to the number of unknown genes. High throughput gene expression analysis involves many operations and at a notinsignificant cost, consequently there are not many datasets that have measured gene expression levels at a large number of time points. As a consequence, we 1-Tigloyltrichilinin believe that the current genetic network models generated based on few points provide limited information. Therefore, integrating diverse data types and exploring new ways to construct genetic networks are required. In this paper, to explore the interaction of gene and environmental factors, we assume that gene expression is a comprehensive process of gene and treatments. Because of the interaction, we can classify all experimental conditions into different subgroups based on the similarity of temporal gene expression profiles. Theoretically, these genes within each subgroup showing similar behaviors may share some regulatory mechanism and regulatory network. Finally, by combining all of the information, we estimated a consensus gene activation order within each subgroup. We illustrated our strategy with an example of a 31 gene set in Pseudomonas aeruginosa, which was expressed in 72 conditions and measured across 48 time points. To avoid conflicting gene connections in different experimental conditions and obtain the most popular genetic networks, we clustered all 72 conditions via clustering analysis based on the gene expression profiles. We used clustering result to guide the formation of environmental condition subgroups, based on the assumption that the condition-dependent expression profiles in each subgroup are similar, and that the genes in each cluster share similar expression pattern and regulatory mechanism. We calculated the transit relationship matrix of the each condition, identified the transit relationship with reference construct pMS402, and then obtained an inferred genetic network for each subgroup. The five constructed interaction networks are shown in Figure 4. The direction of transit relationship is shown by the clockwise turn of the connecting line, and the thickness and color of each connection are proportional to its popularity and strength in the subgroup. The connections among genes in network A�CE are neither uniformly distributed nor random, similar to that observed with genetic regulatory network motifs. There are a lot of short paths between two genes and highly clustered connections, and several genes have more connections than others.