Introduction to Systems Biology

Introduction to Systems Biology

Language: English

Pages: 542

ISBN: 1588297063

Format: PDF / Kindle (mobi) / ePub


This book provides an introductory text for undergraduate and graduate students who are interested in comprehensive biological systems. The authors offer a broad overview of the field using key examples and typical approaches to experimental design. The volume begins with an introduction to systems biology and then details experimental omics tools. Other sections introduce the reader to challenging computational approaches. The final sections provide ideas for theoretical and modeling optimization in systemic biological researches. The book is an indispensable resource, providing a first glimpse into the state-of-the-art in systems biology.

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biology is the central dogma of molecular biology that describes the progression from DNA to proteins that will ultimately play a role in the manifested phenotype. Understanding this progression, how the genotype relates to phenotype, is one of the largest current challenges in biology and has become a focal point of research because genomic data have become readily available for hundreds of organisms over the past several years, starting with the complete genome sequencing of Haemophilus

in the metabolic network, such that the fluxes through reactions of the target metabolite are optimally used, while reactions leading to other byproducts from common precursors are deleted from the network. The premise underlying this bilevel optimization algorithm Bringing Genomes to Life: The Use of Genome-Scale In Silico Models is that overproduction of target metabolites can be achieved by altering the structure of the metabolic network through gene deletions. With this direct

constraints. Each case lists the results of the experimental data (exp), metabolic model (met) and regulatory metabolic model (reg). “+”: predicted or observed growth, “−”: no growth, and ‘n’: for cases involving a regulatory gene knockout not predictable by the metabolic model. Bottom Panel: Metabolic and regulatory networks may be expanded by using high-throughput phenotyping and gene expression data coupled with the predictions of a computational model. The accuracy refers to the percentage of

constraints. Each case lists the results of the experimental data (exp), metabolic model (met) and regulatory metabolic model (reg). “+”: predicted or observed growth, “−”: no growth, and ‘n’: for cases involving a regulatory gene knockout not predictable by the metabolic model. Bottom Panel: Metabolic and regulatory networks may be expanded by using high-throughput phenotyping and gene expression data coupled with the predictions of a computational model. The accuracy refers to the percentage of

corresponds to the “independent nucleotide approximation,” where different positions i contribute additively to the binding energy. More generally, the sequence specific interaction E(S) can be parametrized by L 4 L E (S ) = ∑ ∑ ε iα i Siα + ∑ i =1 α =1 4 ∑J ij ,αβ Siα S jβ + . . . , (4) i ,j =1 α ,β =1 where Sai characterizes the sequence Sai = 1, if the i-th base is a and Sai = 0 otherwise. eia is the interaction energy with the nucleotide a at position i = 1, . . . , L of the DNA

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