Complex Sciences. Second International Conference, COMPLEX 2012, Santa Fe, NM, USA, December 5-7, 2012, Revised Selected Papers

Research Article

Modeling Emergence in Neuroprotective Regulatory Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-03473-7_26,
        author={Antonio Sanfilippo and Jereme Haack and Jason McDermott and Susan Stevens and Mary Stenzel-Poore},
        title={Modeling Emergence in Neuroprotective Regulatory Networks},
        proceedings={Complex Sciences. Second International Conference, COMPLEX 2012, Santa Fe, NM, USA, December 5-7, 2012, Revised Selected Papers},
        proceedings_a={COMPLEX},
        year={2013},
        month={11},
        keywords={regulatory networks emergence complex systems agent-based modeling neuroprotection stroke},
        doi={10.1007/978-3-319-03473-7_26}
    }
    
  • Antonio Sanfilippo
    Jereme Haack
    Jason McDermott
    Susan Stevens
    Mary Stenzel-Poore
    Year: 2013
    Modeling Emergence in Neuroprotective Regulatory Networks
    COMPLEX
    Springer
    DOI: 10.1007/978-3-319-03473-7_26
Antonio Sanfilippo1,*, Jereme Haack1,*, Jason McDermott1,*, Susan Stevens2,*, Mary Stenzel-Poore2,*
  • 1: Pacific Northwest National Laboratory
  • 2: Oregon Health & Sciences University
*Contact email: antonio@pnnl.gov, jereme@pnnl.gov, jason.mcdermott@pnnl.gov, stevensu@ohsu.ed, poorem@ohsu.ed

Abstract

The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling experimentation to test disease triggers and potential drug therapies. Techniques such as agent-based modeling and multi-agent simulations are of particular interest as they support the discovery of emergent pathways, as opposed to other dynamic modeling approaches such as dynamic Bayesian nets and system dynamics. Thus far, emergence-modeling techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks which can advance the discovery of acute treatments for stroke and other diseases.