Two distinct approaches are being used to study complex cellular systems. The first approach automatically searches large datasets for correlations between genes and proteins and represents these as a graph with nodes and edges. The second approach painstakingly crafts detailed models that can be simulated by computer. These approaches have largely been developed separately until now. This project will meld these two approaches into a single framework, thereby allowing fast database searches to augment models that can be simulated. Specifically, the project will 1. Develop fast algorithms to search databases of molecular data to suggest extensions to models of cellular control systems 2. Develop new principles to test how well these extended models match experimental data and 3. Design experimental tests that can validate the predictions made by the first two steps. The project will validate this system by studying the mechanism of cell division, a system involved in the development of cancer. In the long term, the methods developed by this project can be used to study any complex cellular system, e.g., those implicated in infectious diseases.

Public Health Relevance

Project Narrative This project will meld two distinct approaches for studying complex cellular systems, one top-down and the other bottom-up, into a single framework. The project will combine the power of fast database searches with hand-crafted models. The project will validate this system by studying the mechanism of cell division, a system involved in the development of cancer.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM095955-01
Application #
8027139
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Lyster, Peter
Project Start
2011-05-01
Project End
2016-04-30
Budget Start
2011-05-01
Budget End
2012-04-30
Support Year
1
Fiscal Year
2011
Total Cost
$367,031
Indirect Cost
Name
Virginia Polytechnic Institute and State University
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
003137015
City
Blacksburg
State
VA
Country
United States
Zip Code
24061
Bharadwaj, Aditya; Singh, Divit P; Ritz, Anna et al. (2017) GraphSpace: stimulating interdisciplinary collaborations in network biology. Bioinformatics 33:3134-3136
Ritz, Anna; Avent, Brendan; Murali, T M (2017) Pathway Analysis with Signaling Hypergraphs. IEEE/ACM Trans Comput Biol Bioinform 14:1042-1055
Sam, Sarah A; Teel, Joelle; Tegge, Allison N et al. (2017) XTalkDB: a database of signaling pathway crosstalk. Nucleic Acids Res 45:D432-D439
Gil, Daniel P; Law, Jeffrey N; Murali, T M (2017) The PathLinker app: Connect the dots in protein interaction networks. F1000Res 6:58
Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T et al. (2016) A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks. PLoS One 11:e0153738
Tegge, Allison N; Sharp, Nicholas; Murali, T M (2016) Xtalk: a path-based approach for identifying crosstalk between signaling pathways. Bioinformatics 32:242-51
Ritz, Anna; Poirel, Christopher L; Tegge, Allison N et al. (2016) Pathways on demand: automated reconstruction of human signaling networks. NPJ Syst Biol Appl 2:16002
Adames, Neil R; Schuck, P Logan; Chen, Katherine C et al. (2015) Experimental testing of a new integrated model of the budding yeast Start transition. Mol Biol Cell 26:3966-84
Tyson, John J; Novák, Béla (2015) Models in biology: lessons from modeling regulation of the eukaryotic cell cycle. BMC Biol 13:46
Kraikivski, Pavel; Chen, Katherine C; Laomettachit, Teeraphan et al. (2015) From START to FINISH: computational analysis of cell cycle control in budding yeast. NPJ Syst Biol Appl 1:15016

Showing the most recent 10 out of 21 publications