國立虎尾科技大學 |

Bayesian Inference of Virus Evolutionary Models from Next-Generation Sequencing Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Bayesian Inference of Virus Evolutionary Models from Next-Generation Sequencing Data./
作者:	King, Emily Anne.
面頁冊數:	1 online resource (216 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Biostatistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781369879476

Bayesian Inference of Virus Evolutionary Models from Next-Generation Sequencing Data.
King, Emily Anne.

Bayesian Inference of Virus Evolutionary Models from Next-Generation Sequencing Data. - 1 online resource (216 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This item is not available from ProQuest Dissertations & Theses.

There is a rich tradition in mathematical biology of modeling virus population dynamics within hosts. Such models can reproduce trends in the progression of viral infections such as HIV-1, and have also generated insights on the emergence of drug resistance and treatment strategies. Existing mathematical work has focused on the problem of predicting dynamics given model parameters. The problem of estimating model parameters from observed data has received little attention. One reason is likely the historical difficulty of obtaining high-resolution samples of virus diversity within hosts. Now, next-generation sequencing (NGS) approaches developed in the past decade can supply such data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369879476Subjects--Topical Terms:

783654
Biostatistics.
Index Terms--Genre/Form:

554714
Electronic books.

Bayesian Inference of Virus Evolutionary Models from Next-Generation Sequencing Data.
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Karin S. Dorman.
502 $a Thesis (Ph.D.) $c Iowa State University $d 2017.
504 $a Includes bibliographical references
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a There is a rich tradition in mathematical biology of modeling virus population dynamics within hosts. Such models can reproduce trends in the progression of viral infections such as HIV-1, and have also generated insights on the emergence of drug resistance and treatment strategies. Existing mathematical work has focused on the problem of predicting dynamics given model parameters. The problem of estimating model parameters from observed data has received little attention. One reason is likely the historical difficulty of obtaining high-resolution samples of virus diversity within hosts. Now, next-generation sequencing (NGS) approaches developed in the past decade can supply such data.
520 $a This thesis presents two Bayesian methods that harness classical models to generate testable hypotheses from NGS datasets. The quasispecies equilibrium explains genetic variation in virus populations as a balance between mutation and selection. We use this model to infer fitness effects of individual mutations and pairs of interacting mutations. Although our method provides a high resolution and accurate picture of the fitness landscape when equilibrium holds, we demonstrate the common observation of populations with coexisting, divergent viruses is unlikely to be consistent with equilibrium. Our second statistical method estimates virus growth rates and binding affinity between viruses and antibodies using the generalized Lotka-Volterra model. Immune responses can explain coexistence of abundant virus variants and their trajectories through time. Additionally, we can draw inferences about immune escape and antibody genetic variants responsible for improved virus recognition.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Biostatistics. $3 783654
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650 4 $a Statistics. $3 556824
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710 2 $a Iowa State University. $b Statistics. $3 1182662
773 0 $t Dissertation Abstracts International $g 78-11B(E).
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