Defining the players in higher-order networks

Predictive modeling for reverse engineering functional influence networks

Jason E. McDermott, Michelle Archuleta, Susan L. Stevens, Mary P. Stenzel-Poore, Antonio Sanfilippo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationPacific Symposium on Biocomputing 2011, PSB 2011
Pages314-325
Number of pages12
Publication statusPublished - 2011
Externally publishedYes
Event16th Pacific Symposium on Biocomputing, PSB 2011 - Kohala Coast, HI
Duration: 3 Jan 20117 Jan 2011

Other

Other16th Pacific Symposium on Biocomputing, PSB 2011
CityKohala Coast, HI
Period3/1/117/1/11

Fingerprint

Gene Ontology
Reverse engineering
Stroke
Genes
Ontology
Dynamical systems
Blood
Throughput
Topology
Neuroprotection

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Biomedical Engineering
  • Medicine(all)

Cite this

McDermott, J. E., Archuleta, M., Stevens, S. L., Stenzel-Poore, M. P., & Sanfilippo, A. (2011). Defining the players in higher-order networks: Predictive modeling for reverse engineering functional influence networks. In Pacific Symposium on Biocomputing 2011, PSB 2011 (pp. 314-325)

Defining the players in higher-order networks : Predictive modeling for reverse engineering functional influence networks. / McDermott, Jason E.; Archuleta, Michelle; Stevens, Susan L.; Stenzel-Poore, Mary P.; Sanfilippo, Antonio.

Pacific Symposium on Biocomputing 2011, PSB 2011. 2011. p. 314-325.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

McDermott, JE, Archuleta, M, Stevens, SL, Stenzel-Poore, MP & Sanfilippo, A 2011, Defining the players in higher-order networks: Predictive modeling for reverse engineering functional influence networks. in Pacific Symposium on Biocomputing 2011, PSB 2011. pp. 314-325, 16th Pacific Symposium on Biocomputing, PSB 2011, Kohala Coast, HI, 3/1/11.
McDermott JE, Archuleta M, Stevens SL, Stenzel-Poore MP, Sanfilippo A. Defining the players in higher-order networks: Predictive modeling for reverse engineering functional influence networks. In Pacific Symposium on Biocomputing 2011, PSB 2011. 2011. p. 314-325
McDermott, Jason E. ; Archuleta, Michelle ; Stevens, Susan L. ; Stenzel-Poore, Mary P. ; Sanfilippo, Antonio. / Defining the players in higher-order networks : Predictive modeling for reverse engineering functional influence networks. Pacific Symposium on Biocomputing 2011, PSB 2011. 2011. pp. 314-325
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