Introduction to Biological Networks: From Graphs to Systems Biology (Chapman & Hall/CRC Mathematical and Computational Biology)

The new research area of genomics-inspired network biology lacks an introductory book that enables both physical/computational scientists and biologists to obtain a general yet sufficiently rigorous perspective of current thinking. Filling this gap, **Introduction to Biological Networks** provides a thorough introduction to genomics-inspired network biology for physical scientists and biologists involved in interdisciplinary research. The book focuses on the concept of molecular and genetic interaction networks as a paradigm for interpreting the complexity of molecular biology at a genomic scale. The authors describe the experimental methods used to discover and test networks of interaction among biological molecules. They also present computational methods for predicting the interaction networks, discuss general mechanisms of network formation and evolution, and explore the application of network approaches to important problems in biology and medicine. With many examples throughout and clear explanations of key concepts, this book is the first to offer a broad treatment of genomics-inspired network biology with sufficient mathematical and biological rigor. It gives readers a conceptual understanding of this burgeoning scientific field. Preface In The 1940s And 1950s, Biology Was Transformed By Physicists And Physical Chemists, Who Employed Simple Yet Powerful Concepts And Engaged The Powers Of Genetics To Infer Mechanisms Of Biological Processes. The Biological Sciences Borrowed From The Physical Sciences The Notion Of Building Intuitive, Testable, And Physically Realistic Models By Reducing The Complexity Of Biological Systems To The Components Essential For Studying The Problem At Hand. Molecular Biology Was Born. A Similar Migration Of Physical Scientists And Of Methods Of Physical Sciences Into Biology Has Been Occurring In The Decade Following The Complete Sequencing Of The Human Genome, Whose Discrete Character And Similarity To Natural Language Has Additionally Facilitated The Application Of The Techniques Of Modern Computer Science. Furthermore, The Vast Amount Of Genomic Data Spawned By The Sequencing Projects Has Led To The Development And Application Of Statistical Methods For Making Sense Of This Data. The Sheer Amount Of Data At The Genome Scale That Is Available To Us Today Begs For Descriptions That Go Beyond Simple Models Of The Function Of A Single Gene To Embrace A Systemlevel Understanding Of Large Sets Of Genes Functioning In Unison. It Is No Longer Sufficient To Understand How A Single Gene Mutation Causes A Change In Its Product's Biochemical Function, Although This Is In Many Cases Still An Important Problem. It Is Now Possible To Address How The Consequences Of A Mutation Might Reverberate Through The Interconnected System Of Genes And Their Products Within The Cell-- The Living Interactome -- Experimental Inference Of Interactions -- Prediction Of Physical Interactions -- Metabolic Networks And Genetic Interactions -- Testing Inferred Networks -- Small Model Networks -- Tractable Models Of Large Networks -- Network Modularity And Robustness -- Networks And Disease. Animesh Ray, Alpan Raval. Includes Bibliographical References And Index. "Preface In the 1940s and 1950s, biology was transformed by physicists and physical chemists, who employed simple yet powerful concepts and engaged the powers of genetics to infer mechanisms of biological processes. The biological sciences borrowed from the physical sciences the notion of building intuitive, testable, and physically realistic models by reducing the complexity of biological systems to the components essential for studying the problem at hand. Molecular biology was born. A similar migration of physical scientists and of methods of physical sciences into biology has been occurring in the decade following the complete sequencing of the human genome, whose discrete character and similarity to natural language has additionally facilitated the application of the techniques of modern computer science. Furthermore, the vast amount of genomic data spawned by the sequencing projects has led to the development and application of statistical methods for making sense of this data. The sheer amount of data at the genome scale that is available to us today begs for descriptions that go beyond simple models of the function of a single gene to embrace a systemlevel understanding of large sets of genes functioning in unison. It is no longer sufficient to understand how a single gene mutation causes a change in its product's biochemical function, although this is in many cases still an important problem. It is now possible to address how the consequences of a mutation might reverberate through the interconnected system of genes and their products within the cell"-- Provided by publisher