An overview of the current systems biology-based knowledge and the experimental approaches for deciphering the biological basis of cancer.

Genetic alterations in cancer, in addition to being the fundamental drivers of tumorigenesis, can give rise to a variety of metabolic adaptations that allow cancer cells to survive and proliferate in diverse tumor microenvironments. This metabolic flexibility is different from normal cellular metabolic processes and leads to heterogeneity in cancer metabolism within the same cancer type or even within the same tumor. In this book, we delve into the complexity and diversity of cancer metabolism, and highlight how understanding the heterogeneity of cancer metabolism is fundamental to the development of effective metabolism-based therapeutic strategies. Deciphering how cancer cells utilize various nutrient resources will enable clinicians and researchers to pair specific chemotherapeutic agents with patients who are most likely to respond with positive outcomes, allowing for more cost-effective and personalized cancer therapeutic strategies.

Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward.

Many potential applications of synthetic and systems biology are relevant to the challenges associated with the detection, surveillance, and responses to emerging and re-emerging infectious diseases. On March 14 and 15, 2011, the Institute of Medicine's (IOM's) Forum on Microbial Threats convened a public workshop in Washington, DC, to explore the current state of the science of synthetic biology, including its dependency on systems biology; discussed the different approaches that scientists are taking to engineer, or reengineer, biological systems; and discussed how the tools and approaches of synthetic and systems biology were being applied to mitigate the risks associated with emerging infectious diseases. The Science and Applications of Synthetic and Systems Biology is organized into sections as a topic-by-topic distillation of the presentations and discussions that took place at the workshop. Its purpose is to present information from relevant experience, to delineate a range of pivotal issues and their respective challenges, and to offer differing perspectives on the topic as discussed and described by the workshop participants. This report also includes a collection of individually authored papers and commentary.

Systems Biology in Cancer Research and Drug Discovery provides a unique collection of chapters, by world-class researchers, describing the use of integrated systems biology and network modeling in the cancer field where traditional tools have failed to deliver expected promise. This book touches four applications/aspects of systems biology (i) in understanding aberrant signaling in cancer (ii) in identifying biomarkers and prognostic markers especially focused on angiogenesis pathways (iii) in unwinding microRNAs complexity and (iv) in anticancer drug discovery and in clinical trial design. This book reviews the state-of-the-art knowledge and touches upon cutting edge newer and improved applications especially in the area of network modeling. It is aimed at an audience ranging from students, academics, basic researcher and clinicians in cancer research. This book is expected to benefit the field of translational cancer medicine by bridging the gap between basic researchers, computational biologists and clinicians who have one ultimate goal and that is to defeat cancer.

This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.