Background
Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles.

Results
We introduce a tool, TimeXNet (http://timexnet.hgc.jp/), which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals¹. TimeXNet has been extensively evaluated for its ability to predict novel regulators and their associated pathways within active gene sub-networks in the innate immune response. Compared to other similar methods, TimeXNet identifies up to 40% more novel regulators from independent experimental datasets. It also predicts paths within a greater number of known pathways with longer overlaps (up to 7 consecutive edges) within these pathways.

Conclusions
TimeXNet is a reliable tool that can be used to study cellular response to stimuli through the identification of time-dependent active gene sub-networks in diverse biological systems. TimeXNet is implemented in Java as a stand-alone application and supported on Linux, MS Windows and Macintosh. It can be downloaded from http://timexnet.hgc.jp/. The output of TimeXNet can be directly viewed in Cytoscape. TimeXNet is freely available for non-commercial users.