We propose to develop and experimentally validate computational methods for reconstructing dynamic regulatory networks in multiple species. Transcriptional gene regulation is a dynamic process and its proper functioning is essential for all living organisms. Several diseases are associated with partial or complete loss of appropriate transcriptional regulation. Unlike prior methods that focused on static representation of these networks we hypothesize that by combining the abundant static regulatory data with time series expression we will be able to reconstruct dynamic representations for these networks. This will lead to identification of the regulators of these processes, the set of interactions taking place, and on their timing. The models generate testable temporal hypotheses and the results of these experiments will be used to further refine the network leading to accurate models for the systems and responses studied. This global dynamic view of regulatory networks will be a very useful tool for researchers studying a wide range of biological systems and disease states. The proposed project will be carried out in a framework of a well-established, tightly integrated multidisciplinary effort among research groups of comutational biologists, molecular biologists and physician-scientists. Our team has extensive experience in the theoretical and experimental analysis of temporal regulatory networks, including various clustering and modeling computational methods (Z. Bar-Joseph), theoretical and experimental studies of regulatory networks in bacteria (Z. N. Oltvai), and measurements of temporal binding and mapping out regulatory networks in budding yeast (I. Simon) and in human diaseses (N. Kaminski). Reconstructing dynamic regulatory networks.
The aim of the proposed research program is to develop and experimentally test new computational methods for reconstructing dynamic regulatory networks. The methods would be used to study response programs and diseases in several species. By the end of the program we will have (a.) DREM, a unified computational method that combines static and time series data for reconstructing dynamic regulatory maps (b) Support for multiple species and (c.) A software package allowing users to model dynamic networks using new time series data. This software will be of practical use to the biomedical research community.
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|Zhong, Shan; He, Xin; Bar-Joseph, Ziv (2013) Predicting tissue specific transcription factor binding sites. BMC Genomics 14:796|
|Zhou, Yi; Vazquez, Alexei; Wise, Aaron et al. (2013) Carbon catabolite repression correlates with the maintenance of near invariant molecular crowding in proliferating E. coli cells. BMC Syst Biol 7:138|
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|Le, Hai-Son; Schulz, Marcel H; McCauley, Brenna M et al. (2013) Probabilistic error correction for RNA sequencing. Nucleic Acids Res 41:e109|
|Gitter, Anthony; Carmi, Miri; Barkai, Naama et al. (2013) Linking the signaling cascades and dynamic regulatory networks controlling stress responses. Genome Res 23:365-76|
|Gitter, Anthony; Bar-Joseph, Ziv (2013) Identifying proteins controlling key disease signaling pathways. Bioinformatics 29:i227-36|
|Chang, Katherine Noelani; Zhong, Shan; Weirauch, Matthew T et al. (2013) Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in Arabidopsis. Elife 2:e00675|
|Navlakha, Saket; Suhan, Joseph; Barth, Alison L et al. (2013) A high-throughput framework to detect synapses in electron microscopy images. Bioinformatics 29:i9-17|
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