TY - GEN
T1 - Defining the players in higher-order networks
T2 - 16th Pacific Symposium on Biocomputing, PSB 2011
AU - McDermott, Jason E.
AU - Archuleta, Michelle
AU - Stevens, Susan L.
AU - Stenzel-Poore, Mary P.
AU - Sanfilippo, Antonio
PY - 2011
Y1 - 2011
N2 - Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach to expand the set of regulators considered and show that addition of topological bottlenecks improves the performance of the predictive model. Finally, we explore how improvements in definition of functional modules may be achieved through an integration of inferred network relationships and functional relationships defined using Gene Ontology similarity. We show that appropriate integration of these two types of relationships can result in models with improved performance.
AB - Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach to expand the set of regulators considered and show that addition of topological bottlenecks improves the performance of the predictive model. Finally, we explore how improvements in definition of functional modules may be achieved through an integration of inferred network relationships and functional relationships defined using Gene Ontology similarity. We show that appropriate integration of these two types of relationships can result in models with improved performance.
UR - http://www.scopus.com/inward/record.url?scp=82555167845&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82555167845&partnerID=8YFLogxK
M3 - Conference contribution
C2 - 21121059
AN - SCOPUS:82555167845
SN - 9814335053
SN - 9789814335058
T3 - Pacific Symposium on Biocomputing 2011, PSB 2011
SP - 314
EP - 325
BT - Pacific Symposium on Biocomputing 2011, PSB 2011
Y2 - 3 January 2011 through 7 January 2011
ER -