Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach

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dc.citation.issue2
dc.citation.volume498
dc.contributor.authorPerez-Acle T
dc.contributor.authorFuenzalida I
dc.contributor.authorMartin AJM
dc.contributor.authorSantibaƱez R
dc.contributor.authorAvaria R
dc.contributor.authorBernardin A
dc.contributor.authorBustos AM
dc.contributor.authorGarrido D
dc.contributor.authorDushoff J
dc.contributor.authorLiu JH
dc.coverage.spatialUnited States
dc.date.accessioned2024-01-30T00:13:25Z
dc.date.accessioned2024-07-25T06:34:52Z
dc.date.available2017-11-21
dc.date.available2024-01-30T00:13:25Z
dc.date.available2024-07-25T06:34:52Z
dc.date.issued2018-03-29
dc.description.abstractComputational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.
dc.format.pagination342-351
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/29175206
dc.identifier.citationPerez-Acle T, Fuenzalida I, Martin AJM, SantibaƱez R, Avaria R, Bernardin A, Bustos AM, Garrido D, Dushoff J, Liu JH. (2018). Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach.. Biochem Biophys Res Commun. 498. 2. (pp. 342-351).
dc.identifier.doi10.1016/j.bbrc.2017.11.138
dc.identifier.eissn1090-2104
dc.identifier.elements-typejournal-article
dc.identifier.issn0006-291X
dc.identifier.piiS0006-291X(17)32317-3
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70464
dc.languageeng
dc.publisherElsevier Inc
dc.relation.isPartOfBiochem Biophys Res Commun
dc.rights(c) The author/sen
dc.rights.licenseCC BY-NC-ND 4.0en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectAgents
dc.subjectGame theory
dc.subjectGene regulation
dc.subjectInfectious disease
dc.subjectPrisoner's dilemma
dc.subjectRules
dc.subjectTrust
dc.subjectAlgorithms
dc.subjectDisease Outbreaks
dc.subjectEscherichia coli
dc.subjectGame Theory
dc.subjectGene Expression Regulation, Bacterial
dc.subjectGene Regulatory Networks
dc.subjectHemorrhagic Fever, Ebola
dc.subjectHumans
dc.subjectModels, Biological
dc.subjectPrisoner Dilemma
dc.subjectStochastic Processes
dc.subjectTrust
dc.titleStochastic simulation of multiscale complex systems with PISKaS: A rule-based approach
dc.typeJournal article
pubs.elements-id397351
pubs.organisational-groupOther
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