Computational Systems Biology

Inference and Modelling
 
 
Woodhead Publishing
  • 1. Auflage
  • |
  • erschienen am 29. Juli 2016
  • |
  • 180 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-08-100115-8 (ISBN)
 

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.


  • Provides a unified presentation of network inference, analysis, and modeling
  • Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems
  • Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology
  • Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications


Paola Lecca received a M.S. in Theoretical Physics from the University of Trento (Italy) in 1997 and a PhD in Computer Science in 2006 from the International Doctorate School in Information and Communication Technologies at the University of Trento (Italy). Since 1998 she held Researcher and Principal Investigator positions in research centers and in academia. From 1998 to 2000 she was Research Assistant at the Fondazione Bruno Kessler - Center for Information Technologies of Trento by the research unit of Predictive Models for Biomedicine & Environment. From 2001 to 2002 Dr. Lecca worked at the Department of Physics of University of Trento in the area of data manipulation and predictive modelling in research programs of the National Institute of Nuclear Physics. In 2006 she joined to The Microsoft-Research University of Trento Centre for Computational and Systems Biology (COSBI), Italy. At COSBI Dr. Lecca led the group of Data Manipulation and Knowledge Inference. From 2012 to 2015 Dr. Lecca continued her researches at the Laboratory of Computational Oncology of the Centre for Integrative Biology (CIBIO) of University of Trento, Italy. She is currently collaborating with the Department of Mathematics of University of Trento, where she develops optimized techniques of simulation of hybrid (stochastic and deterministic) dynamical biochemical systems.She is a Professional Member of Association for Computing Machinery and author of seventy publications including books and journal and conference papers on international journals in computational biology, bioinformatics, and biophysics. She carries on an intense editorial activity as editor and reviewer for high impact factor journals in these subjects, and leads the organization of school and symposia of bioinformatics.
  • Englisch
  • Cambridge
Elsevier Science
  • 4,61 MB
978-0-08-100115-8 (9780081001158)
0081001150 (0081001150)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Computational Systems Biology: Inference and Modeling
  • Copyright
  • Dedication
  • Contents
  • About the Authors
  • Preface
  • Acknowledgments
  • Chapter 1: Overview of Biological Network Inference and Modeling of Dynamics
  • 1.1 Introduction to Inference of Topologies, Causalities, and Dynamic Models
  • 1.2 The Data
  • 1.2.1 Features of Optimal Datasets
  • 1.2.2 Performance and Reliability Issues
  • Chapter 2: Network Inference From Steady-State Data
  • 2.1 Median-Corrected Z Scores
  • 2.1.1 The Data
  • 2.1.2 The Performance
  • 2.2 Multiple Regression Method
  • 2.2.1 The Data
  • 2.2.2 The Performance
  • 2.3 Bayesian Variable Selection Method
  • 2.3.1 The Data
  • 2.3.2 The Performance
  • Chapter 3: Network Inference From Time-Course Data
  • 3.1 Time-Lagged-Correlation-Based Network Inference
  • 3.1.1 Network Inference
  • 3.1.2 Inference of Kinetic Parameters
  • 3.1.3 The Use Case: Gemcitabine Metabolism
  • 3.2 Bayesian Approaches
  • 3.3 The Method of Variational Bayesian Inference
  • 3.3.1 A Markov Jump Model for Reaction Systems
  • 3.3.2 A Formulation of Variational Inference
  • 3.3.3 The Diffusion Approximation
  • 3.3.4 The KL Divergence for Diffusion Processes
  • 3.3.5 The Use Case: Mechanisms of Cancer Chemoresistance
  • Chapter 4: Network-Based Conceptualization of Observational Data
  • 4.1 Biological Network Data, Sampling, and Predictability
  • 4.2 Characteristics of Biological Networks
  • 4.2.1 Basic Network Features
  • 4.2.2 Network Models
  • 4.2.3 Network Motifs
  • 4.3 Module Discovery Approaches
  • 4.4 Categorization of Network Inference Methods
  • 4.5 Performance of Network Inference Methods
  • 4.6 Comparison of Network Inference Methods
  • 4.7 Applications of Network-Based Data Integration
  • Chapter 5: Deterministic Differential Equations
  • 5.1 The Rationale of Deterministic Modeling
  • 5.1.1 Structural Information
  • 5.1.2 Quantitative Information
  • 5.1.3 The Continuous-Deterministic Interpretation of Systems
  • 5.2 Modeling Elemental and Abstract Biological Phenomena
  • 5.2.1 Elementary Reactions
  • 5.2.2 Abstract Reactions
  • 5.3 Analysis of Deterministic Differential Models
  • 5.3.1 Steady-State Solution
  • 5.3.2 Transient Analysis
  • 5.3.3 Phase-Plane Analysis
  • 5.4 Case Studies
  • 5.4.1 The Sporulation Initiation Network in Bacillus subtilis
  • 5.4.2 The NF-B Oscillating Behavior
  • Chapter 6: Stochastic Differential Equations
  • 6.1 Reaction Kinetics: The Molecular Approach to Kinetics
  • 6.1.1 Reactions are Collisions
  • 6.1.2 Reaction Rates
  • 6.1.3 The Reaction Rate Constant in the StochasticFormulation of Chemical Kinetics
  • 6.2 Stochastic Differential Equations
  • 6.2.1 The Master Equation
  • 6.2.2 The Chemical Master Equation
  • 6.2.3 The Langevin Equation
  • Chapter 7: From Network Inference to the Study of Human Diseases
  • 7.1 Introduction to Network Medicine
  • 7.1.1 Disease Network Properties
  • 7.1.2 Network Analysis of Human Diseases
  • 7.2 Databases and Tools for Network Medicine
  • 7.3 A Case Study of Neurodegenerative Diseases
  • 7.3.1 Network Reconstruction for NeurodegenerativeDiseases
  • 7.3.2 Analysis of the Neurodegenerative Disease Gene Network
  • 7.3.3 Network Modeling for Neurodegenerative Diseases
  • 7.3.4 Discussion
  • 7.4 Conclusion and Perspectives
  • Chapter 8: Conclusions
  • 8.1 Network Inference, Modeling, and Simulation in the Era of Big Data and High-Throughput Experiments
  • Bibliography
  • Index
  • Back Cover

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