Computational Systems Biology Approaches in Cancer Research

Description

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. Features Up to date using a wide range of approaches Applicationexample in each chapter Online resources with useful applications' Cover Half Title Series Page Title Page Copyright Page Contents Cancer: A New Old Story in the Era of Big Data Chapter 1: Pathway Databases and Network Resources in Cancer 1.1 SIGNOR and DISNOR – Causal Interaction Networks for Disease Analysis 1.1.1 Summary 1.1.2 Introduction 1.1.3 Approach and Application Example 1.1.4 Discussion and Perspectives Useful Resources References 1.2 Reactome: A Free and Reliable Database to Analyze Biological Pathways 1.2.1 Summary 1.2.2 Introduction 1.2.3 Approach and Application Example 1.2.4 Discussion and Perspectives Useful Resources References 1.3 Atlas of Cancer Signalling Network: An Encyclopedia of Knowledge on Cancer Molecular Mechanisms 1.3.1 Summary 1.3.2 Introduction 1.3.3 Approach and Application Example 1.3.4 Discussion and Perspectives Useful Resources References Chapter 2: Tumour Microenvironment Studies in Immuno-Oncology Research 2.1 Network Analysis of the Immune Landscape of Cancer 2.1.1 Summary 2.1.2 Introduction 2.1.3 Approach and Application Examples 2.1.4 Discussion and Perspectives Useful Resources References 2.2 Integrative Cancer Immunology and Novel Concepts of Cancer Evolution 2.2.1 Summary 2.2.2 Introduction 2.2.3 Approach and Application Example 2.2.4 Discussion and Perspectives Useful Resources References 2.3 Systems Biology Approach to Study Heterogeneity and Cell Communication Networks in the Tumour Microenvironment 2.3.1 Summary 2.3.2 Introduction Concepts of Communication Steady State versus Inflammation Cancer as a Dysregulation of Cell–Cell Communication 2.3.3 Approach and Application Example Cell Heterogeneity Molecular Heterogeneity Connection Ligand/Receptors 2.3.4 Discussion and Perspectives References Chapter 3: Multi-Level Data Analysis in Cancer: Tools and Approaches 3.1 The Cytoscape Platform for Network Analysis and Visualization 3.1.1 Summary 3.1.2 Introduction Molecular Pathways and Functional Interaction Networks 3.1.3 Approach and Application Example A Brief Tour of Cytoscape 3.1.4 Discussion and Perspectives Towards Comprehensive Understanding of Complex Disease References 3.2 Disease PERCEPTION: PERsonalized Comorbidity ExPloraTION 3.2.1 Summary 3.2.2 Introduction 3.2.3 Approach and Application Example 3.2.4 Discussion and Perspectives Useful Resources References 3.3 Deconvolution of Heterogeneous Cancer Omics Data 3.3.1 Summary 3.3.2 Introduction 3.3.3 Approach and Application Example 3.3.4 Discussion and Perspectives Useful Resources References Chapter 4: Mathematical Modelling of Signalling Networks in Cancer 4.1 Qualitative Dynamical Modelling of T-helper Cell Differentiation and Reprogramming 4.1.1 Summary 4.1.2 Introduction 4.1.3 Approach and Application Example CoLoMoTo Virtual Machine and Jupyter Notebook Application to Th Cell Differentiation Computation of Th Cell Asymptotic Behaviours Treg to Th17 Cellular Reprogramming Analysis at the Population Level 4.1.4 Discussion and Perspectives Useful Resources References 4.2 Mathematical Models of Signalling Pathways and Gene Regulation Involved in Cancer 4.2.1 Summary 4.2.2 Introduction 4.2.3 Approach and Application Example 4.2.4 Discussion and Perspectives References 4.3 Dynamic Logic Models Complement Machine Learning to Improve Cancer Treatment 4.3.1 Summary 4.3.2 Introduction 4.3.3 Approach and Application Example 4.3.4 Discussion and Perspectives Useful Resources References 4.4 Framework for High-Throughput Personalization of Logical Models Using Multi-Omics Data 4.4.1 Summary 4.4.2 Introduction 4.4.3 Approach and Application Example 4.4.4 Discussion and Perspectives Useful Resources References Chapter 5: Single-Cell Analysis in Cancer 5.1 Tracing Stem Cell Differentiation with Single-cell Resolution 5.1.1 Summary 5.1.2 Introduction 5.1.3 Approach and Application Example 5.1.4 Discussion and Perspectives Useful Resources References 5.2 Phylogeny-Guided Single-cell Mutation Calling 5.2.1 Summary 5.2.2 Introduction 5.2.3 Approach and Application Example 5.2.4 Discussion and Perspectives Useful Resources References Chapter 6: Patient Stratification and Treatment Response Prediction 6.1 Integrative Network-Based Analysis for Subtyping and Cancer Driver Identification 6.1.1 Summary 6.1.2 Introduction 6.1.3 Approach and Application Example 6.1.4 Discussion and Perspectives Useful Resources References 6.2 Patient Stratification from Somatic Mutations 6.2.1 Summary 6.2.2 Introduction 6.2.3 Approach and Application Example 6.2.4 Discussion and Perspectives Useful Resources References 6.3 Evaluating Growth and Risk of Relapse of Intracranial Tumours 6.3.1 Summary 6.3.2 Introduction 6.3.3 Approach and Application Example 6.3.4 Discussion and Perspectives 6.4 Machine Learning for Systems Microscopy 6.4.1 Summary 6.4.2 Introduction 6.4.3 Approach and Application Example 6.4.4 Discussion and Perspectives Conclusions and Future Perspectives List of Acronyms Glossary Index