Date of Award
12-2014
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Computer Science
Committee Chair/Advisor
Dean, Brian C
Committee Member
Dimitrova , Elena S
Committee Member
Jenkins , Eleanor W
Abstract
Study of microbial populations has always been topic of interest for researchers. This is because microorganisms have been of instrumental use in the various studies related to population dynamics, artificial bio-fuels etc. Comparatively short lifespan and availability are two big advantages they have which make them suitable for aforementioned studies. Their population dynamic helps us understand evolution. A lot can be revealed about resource consumption of a system by comparing it to the similar system where bacteria play the role of different factors in the system. Also, study of population dynamics of bacteria can reveal necessary initial conditions for the desired state of microbial population at some reference point in future. This makes it interesting for ecological and evolutionary disciplines. Chaos is a mathematical concept which characterizes behavior of dynamical systems that are highly sensitive to the initial conditions. Small differences in the initial conditions such as those due to rounding errors of values of initial parameters yield widely diverging outcomes for such dynamical systems. The way biological systems behave in nature, there is a reason to believe that they do indeed follow chaotic regime. Various mathematical models have been proposed to mimic biological systems in nature. We believe that models which follow chaotic regime represent the biological systems in better way and also are more efficient. We propose a new software tool which may help simulate the mathematical model at hand and provide view of different set of parameters which can keep the system in chaotic state. This may help researchers design better and efficient biological models or use existing models in better way.
Recommended Citation
Galande, Akshay, "Computational Exploration of Chaotic Dynamics with an Associated Biological System" (2014). All Theses. 1896.
https://tigerprints.clemson.edu/all_theses/1896