Please use this identifier to cite or link to this item: http://hdl.handle.net/11023/270
Title: Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection
Author: Sarpe, Vladimir
Advisor: Jacob, Christian
Keywords: Computer Science
Issue Date: 3-Oct-2012
Abstract: Using 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.
URI: http://hdl.handle.net/11023/270
Appears in Collections:Electronic Theses

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