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Networks of Networks in Biology
Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.
Uncertainty in Biology
Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
High Confidence Network Predictions from Big Biological Data
Biology functions in a most intriguing fashion, with human cells being regulated by multiplex networks of proteins and their dependent systems that control everything from proliferation to cell death. Notably, there are cases when these networks fail to function properly. In some diseases there are multiple small perturbations that push the otherwise healthy cells into a state of malfunction. These maladies are referred to as complex diseases, and include common disorders such as allergy, diabetes type II, and multiple sclerosis, and due to their complexity there is no universally defined approach to fully understand their pathogenesis or pathophysiology. While these perturbations can be mea...
Omic Network Modules in Complex diseases
Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model m...
Between the Lines of Genetic Code
Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current study designs and the possibilities for integrating designs. It then provides recent applications data with case studies in rheumatoid arthritis, multiple sclerosis, myositis and other complex human diseases. Last, it examines current studies and directions for future applications in patient care. Recent multivariate studies show that gene-gene and gene-environme...
