Modeling Dynamic Regulatory Processes in Stroke

Jason E. McDermott, Kenneth Jarman, Ronald Taylor, Mary Lancaster, Harish Shankaran, Keri B. Vartanian, Susan L. Stevens, Mary P. Stenzel-Poore, Antonio Sanfilippo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms.

Original languageEnglish
Article numbere1002722
JournalPLoS Computational Biology
Volume8
Issue number10
DOIs
Publication statusPublished - Oct 2012
Externally publishedYes

Fingerprint

Dynamic Modeling
Stroke
stroke
dynamic models
Dynamic models
Dynamic Model
Computer Simulation
Gene expression
Ordinary differential equations
modeling
Gene Expression
Ordinary differential equation
Neuroprotective Agents
Multigene Family
Transcriptome
gene expression
prediction
Prediction
Biological systems
Gene Expression Data

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Computational Theory and Mathematics

Cite this

McDermott, J. E., Jarman, K., Taylor, R., Lancaster, M., Shankaran, H., Vartanian, K. B., ... Sanfilippo, A. (2012). Modeling Dynamic Regulatory Processes in Stroke. PLoS Computational Biology, 8(10), [e1002722]. https://doi.org/10.1371/journal.pcbi.1002722

Modeling Dynamic Regulatory Processes in Stroke. / McDermott, Jason E.; Jarman, Kenneth; Taylor, Ronald; Lancaster, Mary; Shankaran, Harish; Vartanian, Keri B.; Stevens, Susan L.; Stenzel-Poore, Mary P.; Sanfilippo, Antonio.

In: PLoS Computational Biology, Vol. 8, No. 10, e1002722, 10.2012.

Research output: Contribution to journalArticle

McDermott, JE, Jarman, K, Taylor, R, Lancaster, M, Shankaran, H, Vartanian, KB, Stevens, SL, Stenzel-Poore, MP & Sanfilippo, A 2012, 'Modeling Dynamic Regulatory Processes in Stroke', PLoS Computational Biology, vol. 8, no. 10, e1002722. https://doi.org/10.1371/journal.pcbi.1002722
McDermott JE, Jarman K, Taylor R, Lancaster M, Shankaran H, Vartanian KB et al. Modeling Dynamic Regulatory Processes in Stroke. PLoS Computational Biology. 2012 Oct;8(10). e1002722. https://doi.org/10.1371/journal.pcbi.1002722
McDermott, Jason E. ; Jarman, Kenneth ; Taylor, Ronald ; Lancaster, Mary ; Shankaran, Harish ; Vartanian, Keri B. ; Stevens, Susan L. ; Stenzel-Poore, Mary P. ; Sanfilippo, Antonio. / Modeling Dynamic Regulatory Processes in Stroke. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 10.
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