Parameter Estimation Methods for Ordinary Differential Equation Models with Applications to Microbiology
| dc.contributor.author | Krueger, Justin Michael | en |
| dc.contributor.committeechair | Chung, Matthias | en |
| dc.contributor.committeemember | Pop, Mihai | en |
| dc.contributor.committeemember | Gugercin, Serkan | en |
| dc.contributor.committeemember | Chung, Julianne | en |
| dc.contributor.department | Mathematics | en |
| dc.date.accessioned | 2017-08-05T08:00:24Z | en |
| dc.date.available | 2017-08-05T08:00:24Z | en |
| dc.date.issued | 2017-08-04 | en |
| dc.description.abstract | The compositions of in-host microbial communities (microbiota) play a significant role in host health, and a better understanding of the microbiota's role in a host's transition from health to disease or vice versa could lead to novel medical treatments. One of the first steps toward this understanding is modeling interaction dynamics of the microbiota, which can be exceedingly challenging given the complexity of the dynamics and difficulties in collecting sufficient data. Methods such as principal differential analysis, dynamic flux estimation, and others have been developed to overcome these challenges for ordinary differential equation models. Despite their advantages, these methods are still vastly underutilized in mathematical biology, and one potential reason for this is their sophisticated implementation. While this work focuses on applying principal differential analysis to microbiota data, we also provide comprehensive details regarding the derivation and numerics of this method. For further validation of the method, we demonstrate the feasibility of principal differential analysis using simulation studies and then apply the method to intestinal and vaginal microbiota data. In working with these data, we capture experimentally confirmed dynamics while also revealing potential new insights into those dynamics. We also explore how we find the forward solution of the model differential equation in the context of principal differential analysis, which amounts to a least-squares finite element method. We provide alternative ideas for how to use the least-squares finite element method to find the forward solution and share the insights we gain from highlighting this piece of the larger parameter estimation problem. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12583 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/78674 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Parameter Estimation | en |
| dc.subject | Ordinary Differential Equations | en |
| dc.subject | Microbiota | en |
| dc.subject | Least-Squares Finite Element Method | en |
| dc.title | Parameter Estimation Methods for Ordinary Differential Equation Models with Applications to Microbiology | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mathematics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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