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Network Integration and Optimization...

Shi, Tiange,

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Network Integration and Optimization for Biological System Control /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Network Integration and Optimization for Biological System Control // Tiange Shi.
作者:	Shi, Tiange,
面頁冊數:	1 electronic resource (147 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
標題:	Biostatistics. -
電子資源:	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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653 $a Network modeling
653 $a Network optimization
653 $a Biological systems
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