Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection

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atmire.migration.oldid335
dc.contributor.advisorJacob, Christian
dc.contributor.authorSarpe, Vladimir
dc.date.accessioned2012-10-03T18:43:19Z
dc.date.available2012-11-13T08:01:46Z
dc.date.issued2012-10-03
dc.date.submitted2012en
dc.description.abstractUsing agent-based methodology and a 3-dimensional modelling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model – such as immune cells, viruses and cytokines – interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. One of the challenges of modelling biological systems is choosing the parameter values which lend it biological credibility. As a potential solution, we propose a parameter tuning approach using Particle Swarm Optimization. This approach relies on a graphical representation of an expected outcome as the metric for evaluating the feasibility of a particular set of parameters. As part of our experiments, we apply the optimization approach to the parameters of the clonal selection mechanism within the simulated lymph node. The results of the optimization allow us to understand the benefits and limitations of using this approach, as well as predict its applicability to larger, more complex biological simulations.en_US
dc.identifier.citationSarpe, V. (2012). Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25128en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25128
dc.identifier.urihttp://hdl.handle.net/11023/270
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectComputer Science
dc.subject.classificationSimulationen_US
dc.subject.classificationModellingen_US
dc.subject.classificationImmunologyen_US
dc.subject.classificationInfluenzaen_US
dc.subject.classificationOptimizationen_US
dc.subject.classificationParameteren_US
dc.subject.classificationDiscoveryen_US
dc.titleBiological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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