Year of Publication

2020

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Arts and Sciences

Department

Statistics

First Advisor

Dr. Arnold Stromberg

Second Advisor

Dr. Chi Wang

Abstract

Kinetic modeling of the time dependence of metabolite concentrations including the unstable isotope labeled species is an important approach to simulate metabolic pathway dynamics. It is also essential for quantitative metabolic flux analysis using tracer data. However, as the metabolic networks are complex including extensive compartmentation and interconnections, the parameter estimation for enzymes that catalyze individual reactions needed for kinetic modeling is challenging. As the pa- rameter space is large and multi-dimensional while kinetic data are comparatively sparse, the estimation procedure (especially the point estimation methods) often en- counters multiple local maximum such that standard maximum likelihood methods may yield unreliable results. We proposed a Bayesian approach that leverages existing expert-constructed kinetic models for specifying an informative prior distribution for kinetic parameters. This prior knowledge prioritizes regions of parameter space that encompass the most likely parameter values, thereby facilitating robust parameter es- timation. A component-wise [1] adaptive Metropolis algorithm was used to generate the posterior samples of the kinetic parameters and conduct hypothesis tests under different treatments. Simulation studies using defined networks were used to test the performance of this algorithm under conditions of variable noise.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.064

Funding Information

This research was supported by NIH 1P01CA163223-01A1 from Jan 2018 to April 2019.

Available for download on Friday, March 25, 2022

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