Modeling emergence in neuroprotective regulatory networks

Antonio Sanfilippo, Jereme N. Haack, Jason E. McDermott, Susan L. Stevens, Mary P. Stenzel-Poore

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

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 in silico 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.

Original languageEnglish
Title of host publicationComplex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers
PublisherSpringer Verlag
Pages291-302
Number of pages12
ISBN (Print)9783319034720
Publication statusPublished - 1 Jan 2013
Event2nd International Conference on Complex Sciences, COMPLEX 2012 - Santa Fe, United States
Duration: 5 Dec 20127 Dec 2012

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume126 LNICST
ISSN (Print)1867-8211

Other

Other2nd International Conference on Complex Sciences, COMPLEX 2012
CountryUnited States
CitySanta Fe
Period5/12/127/12/12

Fingerprint

Drug therapy
Gene expression
Dynamical systems
Metabolic Networks and Pathways

Keywords

  • Agent-based modeling
  • Complex systems
  • Emergence
  • Neuroprotection
  • Regulatory networks
  • Stroke

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Sanfilippo, A., Haack, J. N., McDermott, J. E., Stevens, S. L., & Stenzel-Poore, M. P. (2013). Modeling emergence in neuroprotective regulatory networks. In Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers (pp. 291-302). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 126 LNICST). Springer Verlag.

Modeling emergence in neuroprotective regulatory networks. / Sanfilippo, Antonio; Haack, Jereme N.; McDermott, Jason E.; Stevens, Susan L.; Stenzel-Poore, Mary P.

Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers. Springer Verlag, 2013. p. 291-302 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 126 LNICST).

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

Sanfilippo, A, Haack, JN, McDermott, JE, Stevens, SL & Stenzel-Poore, MP 2013, Modeling emergence in neuroprotective regulatory networks. in Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 126 LNICST, Springer Verlag, pp. 291-302, 2nd International Conference on Complex Sciences, COMPLEX 2012, Santa Fe, United States, 5/12/12.
Sanfilippo A, Haack JN, McDermott JE, Stevens SL, Stenzel-Poore MP. Modeling emergence in neuroprotective regulatory networks. In Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers. Springer Verlag. 2013. p. 291-302. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).
Sanfilippo, Antonio ; Haack, Jereme N. ; McDermott, Jason E. ; Stevens, Susan L. ; Stenzel-Poore, Mary P. / Modeling emergence in neuroprotective regulatory networks. Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers. Springer Verlag, 2013. pp. 291-302 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).
@inproceedings{0a588a478bf44a05811a4336c10bc2c6,
title = "Modeling emergence in neuroprotective regulatory networks",
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 in silico 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.",
keywords = "Agent-based modeling, Complex systems, Emergence, Neuroprotection, Regulatory networks, Stroke",
author = "Antonio Sanfilippo and Haack, {Jereme N.} and McDermott, {Jason E.} and Stevens, {Susan L.} and Stenzel-Poore, {Mary P.}",
year = "2013",
month = "1",
day = "1",
language = "English",
isbn = "9783319034720",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "291--302",
booktitle = "Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers",

}

TY - GEN

T1 - Modeling emergence in neuroprotective regulatory networks

AU - Sanfilippo, Antonio

AU - Haack, Jereme N.

AU - McDermott, Jason E.

AU - Stevens, Susan L.

AU - Stenzel-Poore, Mary P.

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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 in silico 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.

AB - 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 in silico 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.

KW - Agent-based modeling

KW - Complex systems

KW - Emergence

KW - Neuroprotection

KW - Regulatory networks

KW - Stroke

UR - http://www.scopus.com/inward/record.url?scp=85025150425&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85025150425&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85025150425

SN - 9783319034720

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 291

EP - 302

BT - Complex Sciences - 2nd International Conference, COMPLEX 2012, Revised Selected Papers

PB - Springer Verlag

ER -