Computational Systems Biology Approaches In Cancer Research (chapman & Hall/crc Computational Biology Series)

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 1 Half Title 2 Series Page 3 Title Page 4 Copyright Page 5 Contents 6 Cancer: A New Old Story in the Era of Big Data 14 Chapter 1: Pathway Databases and Network Resources in Cancer 20 1.1 SIGNOR and DISNOR – Causal Interaction Networks for Disease Analysis 20 1.1.1 Summary 20 1.1.2 Introduction 21 1.1.3 Approach and Application Example 23 1.1.4 Discussion and Perspectives 26 Useful Resources 27 References 27 1.2 Reactome: A Free and Reliable Database to Analyze Biological Pathways 29 1.2.1 Summary 29 1.2.2 Introduction 29 1.2.3 Approach and Application Example 30 1.2.4 Discussion and Perspectives 34 Useful Resources 35 References 35 1.3 Atlas of Cancer Signalling Network: An Encyclopedia of Knowledge on Cancer Molecular Mechanisms 36 1.3.1 Summary 36 1.3.2 Introduction 36 1.3.3 Approach and Application Example 37 1.3.4 Discussion and Perspectives 40 Useful Resources 42 References 42 Chapter 2: Tumour Microenvironment Studies in Immuno-Oncology Research 44 2.1 Network Analysis of the Immune Landscape of Cancer 44 2.1.1 Summary 44 2.1.2 Introduction 45 2.1.3 Approach and Application Examples 46 2.1.4 Discussion and Perspectives 49 Useful Resources 50 References 50 2.2 Integrative Cancer Immunology and Novel Concepts of Cancer Evolution 52 2.2.1 Summary 52 2.2.2 Introduction 52 2.2.3 Approach and Application Example 53 2.2.4 Discussion and Perspectives 57 Useful Resources 57 References 58 2.3 Systems Biology Approach to Study Heterogeneity and Cell Communication Networks in the Tumour Microenvironment 60 2.3.1 Summary 60 2.3.2 Introduction 60 Concepts of Communication 60 Steady State versus Inflammation 60 Cancer as a Dysregulation of Cell–Cell Communication 61 2.3.3 Approach and Application Example 61 Cell Heterogeneity 62 Molecular Heterogeneity 63 Connection Ligand/Receptors 64 2.3.4 Discussion and Perspectives 64 References 64 Chapter 3: Multi-Level Data Analysis in Cancer: Tools and Approaches 66 3.1 The Cytoscape Platform for Network Analysis and Visualization 66 3.1.1 Summary 66 3.1.2 Introduction 66 Molecular Pathways and Functional Interaction Networks 67 3.1.3 Approach and Application Example 68 A Brief Tour of Cytoscape 68 3.1.4 Discussion and Perspectives 70 Towards Comprehensive Understanding of Complex Disease 70 References 72 3.2 Disease PERCEPTION: PERsonalized Comorbidity ExPloraTION 73 3.2.1 Summary 73 3.2.2 Introduction 73 3.2.3 Approach and Application Example 74 3.2.4 Discussion and Perspectives 78 Useful Resources 79 References 79 3.3 Deconvolution of Heterogeneous Cancer Omics Data 81 3.3.1 Summary 81 3.3.2 Introduction 81 3.3.3 Approach and Application Example 82 3.3.4 Discussion and Perspectives 86 Useful Resources 87 References 87 Chapter 4: Mathematical Modelling of Signalling Networks in Cancer 90 4.1 Qualitative Dynamical Modelling of T-helper Cell Differentiation and Reprogramming 90 4.1.1 Summary 90 4.1.2 Introduction 91 4.1.3 Approach and Application Example 92 CoLoMoTo Virtual Machine and Jupyter Notebook 92 Application to Th Cell Differentiation 92 Computation of Th Cell Asymptotic Behaviours 93 Treg to Th17 Cellular Reprogramming 95 Analysis at the Population Level 96 4.1.4 Discussion and Perspectives 96 Useful Resources 97 References 97 4.2 Mathematical Models of Signalling Pathways and Gene Regulation Involved in Cancer 98 4.2.1 Summary 98 4.2.2 Introduction 98 4.2.3 Approach and Application Example 99 4.2.4 Discussion and Perspectives 102 References 103 4.3 Dynamic Logic Models Complement Machine Learning to Improve Cancer Treatment 106 4.3.1 Summary 106 4.3.2 Introduction 106 4.3.3 Approach and Application Example 107 4.3.4 Discussion and Perspectives 110 Useful Resources 111 References 111 4.4 Framework for High-Throughput Personalization of Logical Models Using Multi-Omics Data 113 4.4.1 Summary 113 4.4.2 Introduction 113 4.4.3 Approach and Application Example 114 4.4.4 Discussion and Perspectives 118 Useful Resources 119 References 119 Chapter 5: Single-Cell Analysis in Cancer 122 5.1 Tracing Stem Cell Differentiation with Single-cell Resolution 122 5.1.1 Summary 122 5.1.2 Introduction 123 5.1.3 Approach and Application Example 124 5.1.4 Discussion and Perspectives 126 Useful Resources 127 References 127 5.2 Phylogeny-Guided Single-cell Mutation Calling 129 5.2.1 Summary 129 5.2.2 Introduction 129 5.2.3 Approach and Application Example 130 5.2.4 Discussion and Perspectives 133 Useful Resources 134 References 134 Chapter 6: Patient Stratification and Treatment Response Prediction 136 6.1 Integrative Network-Based Analysis for Subtyping and Cancer Driver Identification 136 6.1.1 Summary 136 6.1.2 Introduction 137 6.1.3 Approach and Application Example 138 6.1.4 Discussion and Perspectives 141 Useful Resources 146 References 146 6.2 Patient Stratification from Somatic Mutations 149 6.2.1 Summary 149 6.2.2 Introduction 149 6.2.3 Approach and Application Example 150 6.2.4 Discussion and Perspectives 153 Useful Resources 154 References 154 6.3 Evaluating Growth and Risk of Relapse of Intracranial Tumours 156 6.3.1 Summary 156 6.3.2 Introduction 156 6.3.3 Approach and Application Example 157 6.3.4 Discussion and Perspectives 160 6.4 Machine Learning for Systems Microscopy 162 6.4.1 Summary 162 6.4.2 Introduction 162 6.4.3 Approach and Application Example 163 6.4.4 Discussion and Perspectives 167 Conclusions and Future Perspectives 170 List of Acronyms 172 Glossary 176 Index 182 "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 Illustrated Access to code/package/web-application"-- Provided by publisher 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 Illustrated Access To Code/package/web-application Ficial Intelligence Approaches. Features Up To Date Using A Wide Range Of Approaches Illustrated Access To Code/package/web-application "This book outlines successful transformation strategies and efforts that have been developed to assist South African higher education system in moving beyond its post-apartheid state of being. Through case studies authored by South African higher education scholars and scholars affiliated with South African institutions, this book aims to highlight the status of transformation in the South African higher education system; demonstrate the variety of transformation initiatives used in academic institutions across South Africa; and offer recommendations to further advance this transformation. Written for scholars and advanced students of higher education in international settings, this volume aims to support quality research that benefits the demographic composition of South African academics and students, and to offer lessons that can inform higher education transformation in similarly multicultural societies"-- Provided by publisher This book outlines successful transformation strategies and efforts that have been developed to assist the South African higher education system in moving beyond its post-apartheid state of being. Through case studies authored by South African higher education scholars and scholars affiliated with South African institutions, this book aims to highlight the status of transformation in the South African higher education system; demonstrate the variety of transformation initiatives used in academic institutions across South Africa; and offer recommendations to further advance this transformation. Written for scholars and advanced students of higher education in international settings, this volume aims to support quality research that benefits the demographic composition of South African academics and students, and offers lessons that can inform higher education transformation in similarly multicultural societies.