Date of Award

Winter 12-10-2021

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Biochemistry and Molecular Biology

Advisor

Joshua B. Kelley

Second Committee Member

Andre Khalil

Third Committee Member

Peter Stechlinski

Additional Committee Members

Robert Gundersen

Abstract

Modeling biological systems furthers our understanding of dynamic relationships and helps us make predictions of the unknown properties of the system. The simple interplay between individual species in a dynamic environment over time can be modeled by equation-based modeling or agent- based modeling (ABM). Equation based modeling describes the change in species quantity using ordinary differential equations (ODE) and is dependent on the quantity of other species in the system as well as a predetermined rates of change. Unfortunately, this method of modeling does not model each individual agent in each species over time so individual dynamics are assumed to be uniform unless explicitly modeled. ABM tracks each individual agent in each species over time and interacts with others using probabilities. Because each agent is kept track of over time, ABM require a longer computation time than equation-based models. In this thesis, we model dynamic biological systems using equation- based and ABM to show how models enhance our understanding of dynamic systems. We show that agent-based modeling is the best method for modeling unknown spatial and temporal dynamic biological relationships.

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