Topics in Stochastic and Biological Modeling
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The Ohio State University / OhioLINK
2021
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1619116183745921 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu16191161837459212021-11-02T05:17:54Z Topics in Stochastic and Biological Modeling Whitman, John A. Biophysics Bioinformatics Physics systems biology viral titer host-virus interaction recurrences epidemiology modeling stochastic modeling codon optimization genetic signaling bursting genes In this dissertation, we develop models for biological processes at several spatial and temporal scales. Codon optimization is a procedure in which genetic sequences are altered (without affecting protein identity) in an attempt/effort to increase protein expression. Our goals are to identify if a given single input sequence (for example, of a pathogenic protein of interest) has been codon optimized, and if so, to identify the target organism. In Chapter 2, we present multiple metrics that we have devised to identify codon optimization. Using information from publicly available databases, we define methods both on the scale of an entire sequence/genome and on the scale of individual codon differences between two matched sequences; these methods are shown to perform with high levels of success (>85%) on optimization routines centered around codon usage as well as maximization of the codon adaptive index.It is known experimentally that information about different external stimuli to cells are transmitted to the interior through the temporal patterns of transcription factors (TFs). In Chapter 3, we address the question of how genes can decode information contained in different aspects of the temporal patterns of single transcription factors and initiate downstream responses with specificity. We focus on amplitude and duration variation of the TF signals and construct a two-gene module that produces protein distribution that have minimal overlap for different input signals; it can distinguish between four types of signals reliably (>90% success) in the presence of intrinsic stochastic fluctuations inherent in biochemical reactions and extrinsic temporal fluctuations. We provide information-theoretic measures of the performance including capacity obtaining values consistent with experimental measurements on yeast.In Chapter 4, we define a model which explores an interesting observation: replication of influenza A virus in infected epithelial cells on a cell plate produces a quantity of virus in the surrounding medium (viral titer) that increases non-monotonically with the initial level of infection. Our work defines a spatially extended host-virus model that describes the early innate immune response of epithelial cells to viral infection; in particular, the spatio-temporal dynamics of the competition between the antiviral immune response and viral antagonism to it. We demonstrate one mechanism for the nonmonotonicity of the experimental data through a confluence of the time scale of infection and cytokine production, and the length scale of cytokine diffusion. Finally, we present an stochastic agent-based epidemiological model designed to investigate infection spread in a population of individual agents. We investigate the spread as a function of the details of the model: the contact between agents, the level of immunity of the agents, etc. Inspired by previous work on infection recurrences, we demonstrate the possibility of prevalent recurrent waves of infection through a sparsely connected population. The occurrence, though not the timing, of the waves is consistent across independent simulations of the model corresponding to different stochastic realizations with a mean separation between the peaks that can be estimated. 2021-10-20 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1619116183745921 http://rave.ohiolink.edu/etdc/view?acc_num=osu1619116183745921 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
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| topic |
Biophysics Bioinformatics Physics systems biology viral titer host-virus interaction recurrences epidemiology modeling stochastic modeling codon optimization genetic signaling bursting genes |
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Biophysics Bioinformatics Physics systems biology viral titer host-virus interaction recurrences epidemiology modeling stochastic modeling codon optimization genetic signaling bursting genes Whitman, John A. Topics in Stochastic and Biological Modeling |
| author |
Whitman, John A. |
| author_facet |
Whitman, John A. |
| author_sort |
Whitman, John A. |
| title |
Topics in Stochastic and Biological Modeling |
| title_short |
Topics in Stochastic and Biological Modeling |
| title_full |
Topics in Stochastic and Biological Modeling |
| title_fullStr |
Topics in Stochastic and Biological Modeling |
| title_full_unstemmed |
Topics in Stochastic and Biological Modeling |
| title_sort |
topics in stochastic and biological modeling |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2021 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1619116183745921 |
| work_keys_str_mv |
AT whitmanjohna topicsinstochasticandbiologicalmodeling |
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1719492027563900928 |