System Modelling in Cellular Biology: From Concepts to Nuts and Bolts

Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This book is a comprehensive introduction and overview of system modeling in biology. It makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds. The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview, and this book fills that gap. This is perhaps the first book to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, which includes: * The concepts of robustness and modularity and their exploitation to study biological systems * The best-known modeling approaches, and their advantages and disadvantages * Lessons from the application of mathematical models to the study of cellular biology, and * Available modeling tools and datasets, along with their computational limitations. Table of Contents I-GENERAL CONCEPTS o 1. The Role of Modeling in Systems Biology. o 2. Complexity and Robustness of Cellular Systems. o 3. On Modules and Modularity. II—MODELING APPROACHES o 4. Bayesian Inference of Biological Systems: The Logic of Biology. o 5. Stoichiometric and Constraint-based Modeling. o 6. Modeling Molecular Interaction Networks with Nonlinear Ordinary Differential Equations. o 7. Qualitative Approaches to the Analysis of Genetic Regulatory Networks. o 8. Stochastic Modeling of Intracellular Kinetics. o 9. Kinetics in Spatially Extended Systems. III—MODELS AND REALITY o 10. Biological Data Acquisition for System Level Modeling—An Exercise in the Art of Compromise. o 11. Methods to Identify Cellular Architecture and Dynamics from Experimental Data. o 12. Using Control Theory to Study Biology. o 13. Synthetic Gene Regulatory Systems. o 14. Multilevel Modeling in Systems Biology: From Cells to Whole Organs. IV—COMPUTATIONAL MODELING o 15. Computational Constraints on Modeling in Systems Biology. o 16. Numerical Simulation for Biochemical Kinetics. o 17. Software Infrastructure for Effective Communication and Reuse of Computational Models. o A—Software Tools for Biological Modeling o References. o Contributors. o Index.