Elucidation of functional consequences of signalling pathway interactions Export

@article{citeulike:6083182, abstract = {BACKGROUND:A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information; which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein-protein interactions. RESULTS:A computational framework is presented which allows for the description of embedded networks, and identification of common components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases acted as connecting proteins most likely responsible for propagation of transduction signals across the networks were identified. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links within these systems as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks. CONCLUSION:It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request.}, author = {Ihekwaba, Adaoha and Nguyen, Phuong and Priami, Corrado}, citeulike-article-id = {6083182}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-370}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19895694}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19895694}, doi = {10.1186/1471-2105-10-370}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {modelling, pathway, signaling}, number = {1}, pages = {370+}, posted-at = {2009-11-07 18:00:01}, priority = {2}, title = {Elucidation of functional consequences of signalling pathway interactions}, url = {http://dx.doi.org/10.1186/1471-2105-10-370}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:6083182 L3 - citeulike-article-id:6083182 N2 - BACKGROUND:A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information; which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein-protein interactions. RESULTS:A computational framework is presented which allows for the description of embedded networks, and identification of common components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases acted as connecting proteins most likely responsible for propagation of transduction signals across the networks were identified. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links within these systems as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks. CONCLUSION:It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Elucidation of functional consequences of signalling pathway interactions VL - 10 SP - 370 KW - modelling KW - pathway KW - signaling AU - Ihekwaba, Adaoha AU - Nguyen, Phuong AU - Priami, Corrado PY - 2009/// UR - http://dx.doi.org/10.1186/1471-2105-10-370 ER -