Coarse Dynamics for Coarse Modeling: An Example From Population Biology

Networks have become a popular way to concisely represent complex nonlinear systems where the interactions and parameters are imprecisely known. One challenge is how best to describe the associated dynamics, which can exhibit complicated behavior sensitive to small changes in parameters. A recently...

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Main Authors: Justin Bush, Konstantin Mischaikow
Format: Article
Language:English
Published: MDPI AG 2014-06-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/16/6/3379
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author Justin Bush
Konstantin Mischaikow
author_facet Justin Bush
Konstantin Mischaikow
author_sort Justin Bush
collection DOAJ
description Networks have become a popular way to concisely represent complex nonlinear systems where the interactions and parameters are imprecisely known. One challenge is how best to describe the associated dynamics, which can exhibit complicated behavior sensitive to small changes in parameters. A recently developed computational approach that we refer to as a database for dynamics provides a robust and mathematically rigorous description of global dynamics over large ranges of parameter space. To demonstrate the potential of this approach we consider two classical age-structured population models that share the same network diagram and have a similar nonlinear overcompensatory term, but nevertheless yield different patterns of qualitative behavior as a function of parameters. Using a generalization of these models we relate the different structure of the dynamics that are observed in the context of biologically relevant questions such as stable oscillations in populations, bistability, and permanence.
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spelling doaj.art-7a6cbd7d6fba407788b49c66eb37c41e2022-12-22T04:22:36ZengMDPI AGEntropy1099-43002014-06-011663379340010.3390/e16063379e16063379Coarse Dynamics for Coarse Modeling: An Example From Population BiologyJustin Bush0Konstantin Mischaikow1Department of Mathematics, Hill Center-Busch Campus, Rutgers, The State University of New Jersey, 110 Frelinghuysen Rd, Piscataway, NJ 08854, USADepartment of Mathematics and BioMaPS, Hill Center-Busch Campus, Rutgers, The State Universityof New Jersey, 110 Frelinghuysen Rd, Piscataway, NJ 08854, USANetworks have become a popular way to concisely represent complex nonlinear systems where the interactions and parameters are imprecisely known. One challenge is how best to describe the associated dynamics, which can exhibit complicated behavior sensitive to small changes in parameters. A recently developed computational approach that we refer to as a database for dynamics provides a robust and mathematically rigorous description of global dynamics over large ranges of parameter space. To demonstrate the potential of this approach we consider two classical age-structured population models that share the same network diagram and have a similar nonlinear overcompensatory term, but nevertheless yield different patterns of qualitative behavior as a function of parameters. Using a generalization of these models we relate the different structure of the dynamics that are observed in the context of biologically relevant questions such as stable oscillations in populations, bistability, and permanence.http://www.mdpi.com/1099-4300/16/6/3379database for dynamicsconley theorypopulation model
spellingShingle Justin Bush
Konstantin Mischaikow
Coarse Dynamics for Coarse Modeling: An Example From Population Biology
Entropy
database for dynamics
conley theory
population model
title Coarse Dynamics for Coarse Modeling: An Example From Population Biology
title_full Coarse Dynamics for Coarse Modeling: An Example From Population Biology
title_fullStr Coarse Dynamics for Coarse Modeling: An Example From Population Biology
title_full_unstemmed Coarse Dynamics for Coarse Modeling: An Example From Population Biology
title_short Coarse Dynamics for Coarse Modeling: An Example From Population Biology
title_sort coarse dynamics for coarse modeling an example from population biology
topic database for dynamics
conley theory
population model
url http://www.mdpi.com/1099-4300/16/6/3379
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