Modeling and Analysis of Bio-molecular Networks

£159.50

Modeling and Analysis of Bio-molecular Networks

Computational biology / bioinformatics Computer modelling and simulation

Authors: Jinhu Lu, Pei Wang

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Collection: Computer Science

Language: English

Published by: Springer

Published on: 6th December 2020

Format: LCP-protected ePub

Size: 82 Mb

ISBN: 9789811591440


Questions from the Perspective of Complex Systems

This book addresses a number of questions from the perspective of complex systems: How can we quantitatively understand the life phenomena? How can we model life systems as complex bio-molecular networks? Are there any methods to clarify the relationships among the structures, dynamics and functions of bio-molecular networks? How can we statistically analyse large-scale bio-molecular networks?

Modeling and Analysis of Bio-molecular Networks

Focusing on the modeling and analysis of bio-molecular networks, the book presents various sophisticated mathematical and statistical approaches. The life system can be described using various levels of bio-molecular networks, including gene regulatory networks, and protein-protein interaction networks. It first provides an overview of approaches to reconstruct various bio-molecular networks, and then discusses the modeling and dynamical analysis of simple genetic circuits, coupled genetic circuits, middle-sized and large-scale biological networks, clarifying the relationships between the structures, dynamics and functions of the networks covered. In the context of large-scale bio-molecular networks, it introduces a number of statistical methods for exploring important bioinformatics applications, including the identification of significant bio-molecules for network medicine and genetic engineering. Lastly, the book describes various state-of-art statistical methods for analysing omics data generated by high-throughput sequencing.

Intended Audience

This book is a valuable resource for readers interested in applying systems biology, dynamical systems or complex networks to explore the truth of nature.

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