Hierarchical Bayesian Inference of Stochastic Biochemical Processes

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dc.contributor.advisorJosić, Krešimir
dc.contributor.committeeMemberOtt, William
dc.contributor.committeeMemberAzencott, Robert
dc.contributor.committeeMemberKim, Jae Kyoung
dc.creatorCortez, Mark Jayson Vasquez
dc.creator.orcid0000-0002-5719-1210
dc.date.accessioned2023-05-26T15:05:45Z
dc.date.available2023-05-26T15:05:45Z
dc.date.createdMay 2022
dc.date.issued2022-04-14
dc.date.updated2023-05-26T15:05:46Z
dc.description.abstractData from population measurements of gene network dynamics have shown that cells exhibit variability even in clonal lines. A reliable mathematical reconstruction of a biological process requires the inference of parameters characterizing this process in a single cell while considering the observed heterogeneity of the population from which data was obtained. Parameter inference, however, is complicated by the fact the outcomes of constituent reactions in a gene circuit are only partially observed in time or are detected indirectly in experiments. One approach is to replace unobserved reactions with time delays, a technique that also simplifies inference through the reduction of model dimension. This simplification, however, results in a non-Markovian model that requires the development of new inference methods. Here, we propose a hierarchical Bayesian inference framework for quantifying the variability of cellular processes within and across cells in a population in a non-Markovian setting, such as a reaction system with delays. We demonstrate our framework using a delayed birth-death process with birth delays which are either fixed or distributed, and show that a model with distributed delays is better when dealing with experimental systems since inference assuming fixed delays lead to underestimation when the true delays are variable. Using synthetic and experimental data, we show that the proposed hierarchical framework is robust and leads to improved estimates as compared to its non-hierarchical analog. We apply our method to data obtained using time-lapse microscopy and infer the parameters that describe the dynamics of fluorescent protein production at the individual cell and population level.
dc.description.departmentMathematics, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Cortez, M., Hong, H., Choi, B., Kim, J., and Josic, K. Hierarchical Bayesian models of transcriptional and translational regulation processes with delays. Bioinformatics 38(1) (2022), 187–195.
dc.identifier.urihttps://hdl.handle.net/10657/14266
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectHierarchical model
dc.subjectBayesian inference
dc.subjectBiochemical networks
dc.subjectDelayed stochastic processes
dc.titleHierarchical Bayesian Inference of Stochastic Biochemical Processes
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentMathematics, Department of
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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