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Biological Simulation and Evolutiona...

Sarpe, Vladimir.

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection./
Author:	Sarpe, Vladimir.
Description:	124 p.
Notes:	Source: Masters Abstracts International, Volume: 51-05.
Contained By:	Masters Abstracts International51-05(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR92654
ISBN:	9780494926543

Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection.
Sarpe, Vladimir.

Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection. - 124 p.

Source: Masters Abstracts International, Volume: 51-05.

Thesis (M.Sc.)--University of Calgary (Canada), 2012.

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.

ISBN: 9780494926543Subjects--Topical Terms:

626642
Computer Science.

Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection.
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020 $a 9780494926543
035 $a (MiAaPQ)AAIMR92654
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100 1 $a Sarpe, Vladimir. $3 2105533
245 1 0 $a Biological Simulation and Evolutionary Optimization: Modelling the Physiology Behind Influenza A Infection.
300 $a 124 p.
500 $a Source: Masters Abstracts International, Volume: 51-05.
500 $a Adviser: Christian Jacob.
502 $a Thesis (M.Sc.)--University of Calgary (Canada), 2012.
520 $a 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.
520 $a 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.
590 $a School code: 0026.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Evolution and Development. $3 1675612
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710 2 $a University of Calgary (Canada). $3 1017619
773 0 $t Masters Abstracts International $g 51-05(E).
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791 $a M.Sc.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR92654