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Modeling and Analysis of Bio-molecular Networks

  • Jinhu Lü
  • , Pei Wang
  • Henan University

Research output: Book/ReportBookpeer-review

Abstract

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

Original languageEnglish
PublisherSpringer Singapore
Number of pages464
ISBN (Electronic)9789811591440
ISBN (Print)9789811591433
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Bioinformatics
  • Biological Network
  • Biomolecular Network
  • Biostatistics
  • Complex Networks
  • Computational Biology
  • Mathematical Biology
  • Systems Biology

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