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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
The Internet Stroke Center, http://www.strokecenter.org/patients/~about-stroke/stroke-statistics (accessed on June 7, 2012)
O’Collins, V.E., Macleod, M.R., Donnan, G.A., Horky, L.L., van der Worp, B.H., Howells, D.W.: 1,026 experimental treatments in acute stroke. Ann. Neurol. 59(3), 467–477 (2006)
Savitz, S.I.: A critical appraisal of the NXY-059 neuroprotection studies for acute stroke: a need for more rigorous testing of neuroprotective agents in animal models of stroke. Exp. Neurol. 205(1), 20–25 (2007)
Fisher, M., Feuerstein, G., Howells, D.W., Hurn, P.D., Kent, T.A., Savitz, S.I., Lo, E.H., STAIR Group: Update of the stroke therapy academic industry roundtable preclinical recommendations. Stroke 40(6), 2244–2250 (2009)
Sahota, P., Savitz, S.I.: Investigational therapies for ischemic stroke: neuroprotection and neurorecovery. Neurotherapeutics 8(3), 434–451 (2011)
Zhang, L., Athale, C., Deisboeck, T.: Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J. Theor. Biol. 244(1), 96–107 (2007)
Engelberg, J., Ropella, G., Hunt, C.: Essential operating principles for tumor spheroid growth. BMC Syst. Biol. 2(1), 110 (2008)
Lollini, P., Motta, S., Pappalardo, F.: Discovery of cancer vaccination protocols with a genetic algorithm driving an agent based simulator. BMC Bioinf. 7(1), 352 (2006)
Li, N., Verdolini, K., Clermont, G., Mi, Q., Rubinstein, E., Hebda, P., Vodovotz, Y.: A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS ONE 3(7), e2789 (2008)
Gonzalez, P., Cardenas, M., Camacho, D., Franyuti, A., Rosas, O., Lagunez-Otero, J.: Cellulat: an agent-based intracellular signalling model. Biosystems 68(2-3), 171–185 (2003)
Pogson, M., Smallwood, R., Qwarnstrom, E., Holcombe, M.: Formal agent-based modelling of intracellular chemical interactions. Biosystems 85(1), 37–45 (2006)
Pogson, M., Holcombe, M., Smallwood, R., Qwarnstrom, E.: Introducing Spatial Information into Predictive NF- κB Modelling-An Agent-Based Approach. PLoS ONE 3(6), e2367 (2008)
Klann, M., Lapin, A., Reuss, M.: Agent-based simulation of reactions in the crowded and structured intracellular environment: Influence of mobility and location of the reactants. BMC Syst. Biol. 5(1), 71 (2011)
Schlitt, T., Brazma, A.: Current approaches to gene regulatory network modelling. BMC Bioinf. 8(suppl. 6), S9 (2007)
Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9(10), 770–780 (2008)
Stenzel-Poore, M.P., Stevens, S.L., Xiong, Z., Lessov, N.S., Harrington, C.A., Mori, M., Meller, R., Rosenzweig, H.L., Tobar, E., Shaw, T.E., Chu, X., Simon, R.P.: Effect of ischaemic preconditioning on genomic response to cerebral ischaemia: similarity to neuroprotective strategies in hibernation and hypoxia-tolerant states. Lancet 362(9389), 1028–1037 (2003)
Stevens, S.L., Ciesielski, T.M., Marsh, B.J., Yang, T., Homen, D.S., Boule, J.L., Lessov, N.S., Simon, R.P., Stenzel-Poore, M.P.: Toll-like receptor 9: a new target of ischemic preconditioning in the brain. J. Cereb. Blood Flow Metab. 28(5), 1040–1047 (2008)
Marsh, B., et al.: Systemic lipopolysaccharide protects the brain from ischemic injury by reprogramming the response of the brain to stroke: a critical role for IRF3. J. Neurosci. 29, 9839–9849 (2009)
Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U., Speed, T.P.: Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics 4(2), 249–264 (2003)
Bonneau, R., et al.: The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7, R36 (2006)
Efron, B., Johnstone, I., Hastie, T., Tibshirani, R.: Least angle regression. Annals of Statistics 32, 407–499 (2003)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B 58, 267–288 (1996)
McDermott, J.E., Archuleta, M., Stevens, S.L., Stenzel-Poore, M.P., Sanfilippo, A.: Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks. In: Pac. Symp. Biocomput., pp. 314–325 (2011a)
Mcdermott, J., Jarman, K., Taylor, R., Lancaster, M., Stevens, S., Vartanian, K., Stenzel-Poore, M., Sanfilippo, A.: Modeling Cumulative Change of Dynamic Regulatory Processes in Stroke. PLoS Computational Biology (forthcoming)
Camazine, S., Deneubourg, J., Franks, N., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press (2011)
Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999), http://ccl.northwestern.edu/netlogo/
Lachmann, A., Xu, H., Krishnan, J., Berger, S.I., Mazloom, A.R., et al.: ChEA: Transcription Factor Regulation Inferred from Integrating Genome-Wide ChIP-X Experiments. Bioinformatics (2010)
Peri, S., Navarro, J.D., Kristiansen, T.Z., Amanchy, R., Surendranath, V., et al.: Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 32, D497–D501 (2004)
Yu, H., Luscombe, N.M., Lu, H.X., Zhu, X., Xia, Y., et al.: Annotation Transfer Between Genomes: Protein-Protein Interologs and Protein-DNA Regulogs. Genome Res. 14, 1107–1118 (2004)
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdottir, H., Tamayo, P., et al.: Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011)
Kim, W.K., Krumpelman, C., Marcotte, E.M.: Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy. Genome Biology 9(suppl. 1), S5 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Sanfilippo, A.P., Haack, J.N., McDermott, J.E., Stevens, S.L., Stenzel-Poore, M.P. (2013). Modeling Emergence in Neuroprotective Regulatory Networks. In: Glass, K., Colbaugh, R., Ormerod, P., Tsao, J. (eds) Complex Sciences. Complex 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-03473-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-03473-7_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03472-0
Online ISBN: 978-3-319-03473-7
eBook Packages: Computer ScienceComputer Science (R0)