Date of Completion

5-2-2014

Embargo Period

10-29-2014

Keywords

network inference, reaction kinetics, genetic programming, gene expression programming, viral dynamics, human immunodeficiency virus, systems biology

Major Advisor

Dr. Ranjan Srivastava

Associate Advisor

Dr. George Bollas

Associate Advisor

Dr. Douglas Cooper

Field of Study

Chemical Engineering

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

Mechanistic mathematical models of biological systems have been used to describe biological phenomena, including human disease, in the hope that one day these models may be used to better understand diseases, as well as to develop and optimize therapeutic strategies. Evolutionary algorithms, such as genetic programming, may be used to symbolically regress mathematical models describing chemical and biochemical species for which kinetic data are available. However, current evolutionary algorithms are restricted to the formulation of simple or approximate models due to the computational cost of evolving mechanistic models for more complex systems.

It was hypothesized that chemical reaction kinetic theory could be used to sufficiently reduce the model search space for an evolutionary algorithm such that it would be possible to infer mechanistic mathematical models of complex biological interactions. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of n-species using heuristics from chemical reaction kinetic theory and a gene expression programming (GEP) based approach.

The resulting algorithm was then successfully validated on a general model of viral dynamics that accounted for six pathways relating the change in viral template, viral genome, and viral structural protein concentrations over time.

The algorithm was applied to generate cohort-specific models of HIV dynamics from a clinical data set. HIV-1 infection models were defined as sets of two ordinary differential equations describing the change in CD4+ T-cell and HIV-1 concentrations over time. The evolved models were used to generate hypotheses regarding treatment effectiveness and the potential for viral rebound in three cohorts of HIV-1 positive individuals receiving different Highly Active Antiretroviral Therapy (HAART) regimens. It was hypothesized by the algorithm that HAART was effective in stopping HIV-1 propagation in two of the three cohorts studied. In the other cohort, it was hypothesized that HIV-1 continued to propagate and that there was the potential for viral rebound.

The result of this work was the development of an algorithm that can be used for the generation of complex mechanistic biological models based upon kinetic data with potential uses in fields ranging from biomedical to biotechnological.

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