1st Edition

Computational Systems Biology Approaches in Cancer Research

Edited By Inna Kuperstein, Emmanuel Barillot Copyright 2020
    186 Pages
    by Chapman & Hall

    185 Pages 19 B/W Illustrations
    by Chapman & Hall

    185 Pages 19 B/W Illustrations
    by Chapman & Hall

    Praise for Computational Systems BiologyApproaches in Cancer Research:

    "Complex concepts are written clearly and with informative illustrations and useful links. The book is enjoyable to read yet provides sufficient depth to serve as a valuable resource for both students and faculty."

    Trey Ideker, Professor of Medicine, UC Xan Diego, School of Medicine

    "This volume is attractive because it addresses important and timely topics for research and teaching on computational methods in cancer research. It covers a broad variety of approaches, exposes recent innovations in computational methods, and provides acces to source code and to dedicated interactive web sites."

    Yves Moreau, Department of Electrical Engineering, SysBioSys Centre for Computational Systems Biology, University of Leuven

    With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states. This book focuses on computational systems biology approaches, with a particular lens on tackling one of the most challenging diseases - cancer.  The book provides an important reference and teaching material in the field of computational biology in general and cancer systems biology in particular.

    The book presents a list of modern approaches in systems biology with application to cancer research and beyond. It is structured in a didactic form such that the idea of each approach can easily be grasped from the short text and self-explanatory figures. The coverage of topics is diverse: from pathway resources, through methods for data analysis and single data analysis to drug response predictors, classifiers and image analysis using machine learning and artificial intelligence approaches.


    • Up to date using a wide range of approaches

    • Applicationexample in each chapter

    • Online resources with useful applications’

    Pathway Databases and Network Resources in Cancer. Signor and Disnor – Causal Interaction Networks for Disease Analysis. Reactome: A Free and Reliable Database to Analyze Biological Pathways. Atlas of Cancer Signalling Network: An Encyclopedia of Knowledge on Cancer Molecular Mechanisms. Tumour Microenvironment Studies in Immuno-Oncology Research. Network Analysis of the Immune Landscape of Cancer. Integrative Cancer Immunology and Novel Concepts of Cancer Evolution. Systems Biology Approach to Study Heterogeneity and Cell Communication Networks in the Tumour Microenvironment. Tools and Approaches. The Cytoscape Platform for Network Analysis and Visualization. Disease Perception: Personalized Comorbidity Exploration. Deconvolution of Heterogeneous Cancer Omics Data. Mathematical Modelling of Signalling Networks in Cancer. Qualitative Dynamical Modelling of T-Helper Cell Differentiation and Reprogramming. Mathematical Models of Signalling Pathways and Gene Regulation Involved in Cancer. Dynamic Logic Models Complement Machine Learning to Improve Cancer Treatment. Framework for High-Throughput Personalization of Logical Models Using Multi-Omics Data. Single-Cell Analysis in Cancer. Tracing Stem Cell Differentiation with Single-Cell Resolution. Phylogeny-Guided Single-Cell Mutation Calling. Patient Stratification and Treatment Response Prediction. Integrative Network-Based Analysis for Subtyping and Cancer Driver Identification. Patient Stratification from Somatic Mutations. Evaluating Growth and Risk of Relapse of Intracranial Tumours. Machine Learning for Systems Microscopy.


    Inna Kuperstein is a researcher at Institut Curie, Paris, France, she is a coordinator of the Atlas of Cancer Signalling Networks (ACSN) project for construction and analysis of detailed signalling maps, development of tools and modelling the maps to predict drug response. She participates in multidisciplinary projects to decipher cell mechanisms rewiring in cancer.

    Emmanuel Barillot is the head of the Cancer and Genome: Bioinformatics, Biostatistics and Epidemiology of a Complex System department and scientific director of the bioinformatics platform at Institut Curie. His research focuses on methodological development and statistical analysis of high-throughput biological data and modeling with the aim to improve therapeutic treatments of cancer.