Computational systems biology : inference and modelling / Paola Lecca...[et al.].
Material type:
TextSeries: Woodhead Publishing series in biomedicine ; 86.Publication details: Amsterdam : Elsevier, ©2016.Description: xv, 164 pages : illustrations (some color) ; 24 cmISBN: - 9780081000953 (hardback)
- 570.113 23 L457
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 570.113 L457 (Browse shelf(Opens below)) | Available | 137765 |
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| 570.11 Sch399 Biological modeling and simulation: a survey of practical models, algorithms, and numerical methods/ | 570.113 B261 Introduction to modeling for biosciences | 570.113 K92 Computational systems biology | 570.113 L457 Computational systems biology : | 570.113 Se454 Primer on mathematical models in biology / | 570.115 St789 Kybernetics of natural systems | 570.115 W647 Progress in biocybernetics |
Includes bibliographical references and index.
Chapter 1: Overview of Biological Network Inference and Modeling of Dynamics --
Chapter 2: Network Inference From Steady-State Data --
Chapter 3: Network Inference From Time-Course Data --
Chapter 4: Network-Based Conceptualization of Observational Data --
Chapter 5: Deterministic Differential Equations --
Chapter 6: Stochastic Differential Equations --
Chapter 7: From Network Inference to the Study of Human Diseases --
Chapter 8: Conclusions.
Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems. This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling. The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.
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