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Bayesian Inference of Virus Evolutio...

King, Emily Anne.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Inference of Virus Evolutionary Models from Next-Generation Sequencing Data./
作者:	King, Emily Anne.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	216 p.
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:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10266667
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. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 216 p.

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

Thesis (Ph.D.)--Iowa State University, 2017.

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.

ISBN: 9781369879476Subjects--Topical Terms:

1002712
Biostatistics.

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