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A Data-Driven Modeling Pipeline for ...

Denaro, Christopher,

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A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology // Christopher Denaro.
Author:	Denaro, Christopher,
Description:	1 electronic resource (205 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32003272
ISBN:	9798314897805

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /
Denaro, Christopher,

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /Christopher Denaro. - 1 electronic resource (205 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Computational methods are becoming commonly used in many areas of medical research. Recently, the field of Quantitative Systems Pharmacology (QSP) emerged to address the need for mathematical models to capture the complex interplay between therapeutic approaches and underlying disease pathophysiology. In a sense, QSP modeling incorporates approaches from physiologically based pharmacokinetics (PBPK), systems biology, and disease modeling, with the explicit goal of model design and output being motivated by quantitative data. In this thesis, we propose a computational framework utilizing a generalization of mathematical graphs called hypergraphs to model metabolic and disease networks; we apply this framework directly to the combination therapy treatment of Tuberculosis, combining heterogeneous data sources of gene expression levels via microarray data and metabolic networks. Next, we demonstrate that machine learning architectures that have classically been applied to natural language problems can be trained to learn network simulation equilibria, providing a proof-of-concept of traditional machine learning approaches to a biological setting. We transition into a literature review that examines the connection between infection with SARS-CoV-2 and neurodegeneration through the lens of mitochondrial function. Next, we motivate and explore applications of the computational framework used in treatment optimization of Tuberculosis to therapeutic biomarker identification in genetic variants of Parkinson's Disease (PD). We also review publicly available data sources for genetic variants of PD and propose a collaborative and integrative computational platform for future model development.

English

ISBN: 9798314897805Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Computational biology

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /
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