Modeling and Analysis of Bio-Molecular Networks

ISBN-13: 9789811591464 / Angielski / Miękka / 2021 / 488 str.

Jinhu Lü; Pei Wang

Spis treści

1 Introduction and Preliminaries.- 2 Reconstruction of Bio-molecular Networks.- 3 Modeling and Analysis of Simple Genetic Circuits.- 4 Modeling and Analysis of Coupled Bio-molecular Circuits.- 5 Modeling and Analysis of Large-scale Networks.- 6 Evolutionary Mechanisms of Network Motifs in PPI Networks.- 7 Identifying Important Nodes in Bio-molecular Networks.- 8 Statistical Analysis of Functional Genes in Human PPI Networks.- 9 Data-driven Statistical Approaches for Omics Data Analysis.

O autorze

Jinhu Lü is a Full Professor and the Dean of the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, and a Professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. He received his Ph.D. in Applied Mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, in 2002. He was a Professor at RMIT University, Melbourne, Australia, and a Visiting Fellow at Princeton University, USA. He is a Chief Scientist in the National Key Research and Development Program of China and a Leading Scientist in the Innovative Research Groups of the National Natural Science Foundation of China. His current research interests include complex networks, cooperation control, and the industrial Internet. He is the author of four research monographs and more than 200 SCI journal papers.

Dr. Lü was a recipient of the prestigious Ho Leung Ho Lee Foundation Award in 2015; the Chinese Government’s State Natural Science Award in 2008, 2012, and 2016; the Australian Research Council Future Fellowships Award in 2009; the National Natural Science Fund for Distinguished Young Scholars Award; and Highly Cited Researcher Awards in engineering from 2014 to 2019. He is/was an Editor for 15 SCI journals, including the Co-Editor-in-Chief of IEEE TII, and served on the Fellows Evaluating Committee of the IEEE CASS, the IEEE CIS, and the IEEE IES. He was the general Co-Chair of IECON 2017. He is a Fellow of IEEE and CAA.

Pei Wang is an Associate Professor at the School of Mathematics and Statistics, Henan University, Kaifeng, China. He received his M.Sc. and Ph.D. in Computational Mathematics from the School of Mathematics and Statistics, Wuhan University, China, in 2009 and 2012, respectively. He was a Visiting Research Fellow at the School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia. His research interests include biostatistics, systems biology, and complex networks. He is the author of more than 30 SCI journal papers, and serves as a reviewer for the American Mathematical Reviews. He is a member of the Technical Committee on Complex Systems and Complex Networks, China Society for Industrial and Applied Mathematics.

Blurb

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.