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Network Integration and Optimization...

Shi, Tiange,

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Network Integration and Optimization for Biological System Control /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Network Integration and Optimization for Biological System Control // Tiange Shi.
Author:	Shi, Tiange,
Description:	1 electronic resource (147 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31769663
ISBN:	9798304960311

Network Integration and Optimization for Biological System Control /
Shi, Tiange,

Network Integration and Optimization for Biological System Control /Tiange Shi. - 1 electronic resource (147 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

Advancements in high-throughput omics technologies have significantly expanded our understanding of biological systems. By utilizing biological network models, researchers can explore complex interactions and regulatory mechanisms, providing insights into disease pathology and enabling the development of advanced therapeutic strategies. However, achieving precise control of biological systems using network knowledge remains a significant challenge.This thesis seeks to address this challenge by introducing novel methodologies that integrate machine learning and network analysis to optimize biological networks for precise control. First, we propose the incorporation of belief propagation in Bayesian Networks to refine model spaces for constraint-based metabolic models and fluxome estimation. This framework is vital for multi-omics model integration, offering insights into how genetic interventions influence pathological states and informing therapeutic target prioritization, combination therapies, and drug discovery efforts.Additionally, we present Network Optimization via Lasso-based Sparse Propagation (NOLSP), a methodology for prediction and control in biological systems. NOLSP constructs a network representation of the biological system and employs propagation to generate a sensitivity matrix, guiding system transitions. By leveraging LASSO regression, NOLSP identifies sparse perturbations required to efficiently transition a biological system from an initial to a desired target state. This approach demonstrates promise in achieving precise disease control and discovering therapeutic targets.In summary, the network methodologies proposed in this thesis-focused on multi-omics biological network integration and system control-provide effective tools for evaluating perturbation effects and steering biological systems toward desired states. These advancements deepen our understanding of biological processes, enhance the precision of disease treatments, and accelerate the development of novel therapeutics.

English

ISBN: 9798304960311Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Network control

Network Integration and Optimization for Biological System Control /
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500 $a Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
500 $a Advisors: Hageman Blair, Rachael Committee members: Miecznikowski, Jeffrey; Landsittel, Douglas.
502 $b Ph.D. $c State University of New York at Buffalo $d 2025.
520 $a Advancements in high-throughput omics technologies have significantly expanded our understanding of biological systems. By utilizing biological network models, researchers can explore complex interactions and regulatory mechanisms, providing insights into disease pathology and enabling the development of advanced therapeutic strategies. However, achieving precise control of biological systems using network knowledge remains a significant challenge.This thesis seeks to address this challenge by introducing novel methodologies that integrate machine learning and network analysis to optimize biological networks for precise control. First, we propose the incorporation of belief propagation in Bayesian Networks to refine model spaces for constraint-based metabolic models and fluxome estimation. This framework is vital for multi-omics model integration, offering insights into how genetic interventions influence pathological states and informing therapeutic target prioritization, combination therapies, and drug discovery efforts.Additionally, we present Network Optimization via Lasso-based Sparse Propagation (NOLSP), a methodology for prediction and control in biological systems. NOLSP constructs a network representation of the biological system and employs propagation to generate a sensitivity matrix, guiding system transitions. By leveraging LASSO regression, NOLSP identifies sparse perturbations required to efficiently transition a biological system from an initial to a desired target state. This approach demonstrates promise in achieving precise disease control and discovering therapeutic targets.In summary, the network methodologies proposed in this thesis-focused on multi-omics biological network integration and system control-provide effective tools for evaluating perturbation effects and steering biological systems toward desired states. These advancements deepen our understanding of biological processes, enhance the precision of disease treatments, and accelerate the development of novel therapeutics.
546 $a English
590 $a School code: 0656
650 4 $a Biostatistics. $3 1002712
650 4 $a Systematic biology. $3 3173492
650 4 $a Bioinformatics. $3 553671
650 4 $a Biochemistry. $3 518028
650 4 $a Genetics. $3 530508
653 $a Network control
653 $a Network integration
653 $a Network modeling
653 $a Network optimization
653 $a Biological systems
690 $a 0308
690 $a 0423
690 $a 0487
690 $a 0369
690 $a 0715
710 2 $a State University of New York at Buffalo. $b Biostatistics. $e degree granting institution. $3 3826175
720 1 $a Hageman Blair, Rachael $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-08B.
790 $a 0656
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31769663