McDermott, J., Samudrala, R., Bumgarner, R., Montgomery, K., Ireton, R. (Eds.)
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Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and management
Provides a number of clearly described, detailed ‘recipes’ for analysis of systems data
Supplies a number of alternative approaches to modeling similar types of data
The recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In Computational Systems Biology, expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism. The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.
Comprehensive and up-to-date, Computational Systems Biology serves to motivate and inspire all those who wish to develop a complete description of a biological system.