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Modeling adaptive biological systems

dc.contributor.authorBagley, R. J.en_US
dc.contributor.authorFarmer, J. D.en_US
dc.contributor.authorKauffman, S. A.en_US
dc.contributor.authorPackard, N. H.en_US
dc.contributor.authorPerelson, Alan S.en_US
dc.contributor.authorStadnyk, I. M.en_US
dc.date.accessioned2006-04-07T20:56:42Z
dc.date.available2006-04-07T20:56:42Z
dc.date.issued1989en_US
dc.identifier.citationBagley, R. J., Farmer, J. D., Kauffman, S. A., Packard, N. H., Perelson, A. S., Stadnyk, I. M. (1989)."Modeling adaptive biological systems." Biosystems 23(2-3): 113-137. <http://hdl.handle.net/2027.42/28135>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6T2K-49NH6P4-J/2/db338096166bc0f4841abe4c155b1e28en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/28135
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=2627562&dopt=citationen_US
dc.description.abstractDuring the evolution of many systems found in nature, both the system composition and the interactions between components will vary. Equating the dimension with the number of different components, a system which adds or deletes components belongs to a class of dynamical systems with a finite dimensional phase space of variable dimension. We present two models of biochemical systems with a variable phase space, a model of autocatalytic reaction networks in the prebiotic soup and a model of the idiotypic network of the immune system. Each model contains characteristic meta-dynamical rules for constructing equations of motion from component properties. The simulation of each model occurs on two levels. On one level, the equations of motion are integrated to determine the state of each component. On a second level, algorithms which approximate physical processes in the real system are employed to change the equations of motion. Models with meta-dynamical rules possess several advantages for the study of evolving systems. First, there are no explicit fitness functions to determine how the components of the model rank in terms of survivability. The success of any component is a function of its relationship to the rest of the system. A second advantage is that since the phase space representation of the system is always finite but continually changing, we can explore a potentially infinite phase space which would otherwise be inaccessible with finite computer resources. Third, the enlarged capacity of systems with meta-dynamics for variation allows us to conduct true evolution experiments. The modeling methods presented here can be applied to many real biological systems. In the two studies we present, we are investigating two apparent properties of adaptive networks. With the simulation of the prebiotic soup, we are most interested in how a chemical reaction network might emerge from an initial state of relative disorder. With the study of the immune system, we study the self-regulation of the network including its ability to distinguish between species which are part of the network and those which are not.en_US
dc.format.extent2065177 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleModeling adaptive biological systemsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelNatural Resources and Environmenten_US
dc.subject.hlbsecondlevelMolecular, Cellular and Developmental Biologyen_US
dc.subject.hlbsecondlevelEcology and Evolutionary Biologyen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumComputer Science Department, University of Michigan, Ann Arbor, MI 48103, U.S.A.en_US
dc.contributor.affiliationotherTheoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USAen_US
dc.contributor.affiliationotherTheoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USAen_US
dc.contributor.affiliationotherDepartment of Biochemistry and Biophysics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USAen_US
dc.contributor.affiliationotherCenter For Complex Systems Research and Department of Physics, University of Illinois, Champaign-Urbana, IL 61826, USAen_US
dc.contributor.affiliationotherTheoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USAen_US
dc.identifier.pmid2627562en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/28135/1/0000586.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0303-2647(89)90016-6en_US
dc.identifier.sourceBiosystemsen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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