Average Reviews:
(More customer reviews)I'm torn between giving this book 3 or 4 stars. On one hand, it is enjoyable to read and a great value for Amazon's discounted price. On the other hand, the book tries to tackle many modeling techniques at once; I often found myself wishing for more complete descriptions than were provided.
The first section introduces basic concepts of mathematical modeling and considers structures and behaviors characteristic of biological models: The text opens with a discussion of the compromise between model scope and informativeness. Trade-offs of biological robustness and complexity are discussed. Modularity is explored as a unifying property of biological systems.
The next section discusses a range of mathematical modeling frameworks. Bayesian logic is introduced as a means to discriminate among competing models (hypotheses) of biological systems. Quasi-steady state stoichiometric methods, non-linear ODEs, PDEs, and stochastic methods are each given a chapter. Biological network topology is also discussed. While the topics are presented well (some better than others), many (especially the topology and PDE chapters) would benefit from more extensive coverage and mathematical background. The toy model examples are also very simplistic; I would have liked to see discussion of special considerations for higher-dimensional systems.
The third section was the most useful for me and at the same time the most frustrating. It discusses practical issues: experimental data collection, model identification, parameter estimation, and control theory. There is a chapter on gene regulatory systems (think BioBricks or Uri Alon's work) and a brief discussion of multi-scale (cellular/tissue/organ) models. These practical issues - the 'nuts and bolts' of the title - were exactly what I hoped to learn about. However, the coverage is only superficial. I often found myself digging up references to clarify questions which (I felt) should have been addressed in the text.
The final section addresses computing. Algorithm complexity and machine representation of models are informally described. I would have liked to see model identification and parameter estimation covered much more thoroughly - these can be computationally intensive for large models.Runge-Kutta ODE algorithms and stochastic algorithms (Gillespie, tau-leaping, and Langevin) are discussed and computational challenges (e.g., stiffness) are detailed. The book ends with a description of system biology markup language (SBML) and a list of current (as of 2006) open source modeling tools.
I would recommend this books to biological modelers who wish to get a taste of other modeling approaches outside their own specialty. Math students coming into biological projects might also benefit from the introduction to the field. However, the lack of a mathematical review section might leave pure-biology students confused unless they consult dedicated math or modeling texts.
A complete table of contents may be found at The MIT Press website.
Click Here to see more reviews about: System Modeling in Cellular Biology: From Concepts to Nuts and Bolts (Bradford Books)
Research in systems biology requires the collaboration of researchersfrom diverse backgrounds, including biology, computer science, mathematics,statistics, physics, and biochemistry. These collaborations, necessary because ofthe enormous breadth of background needed for research in this field, can behindered by differing understandings of the limitations and applicability oftechniques and concerns from different disciplines. This comprehensive introductionand overview of system modeling in biology makes the relevant background materialfrom all pertinent fields accessible to researchers with different backgrounds.Theemerging area of systems level modeling in cellular biology has lacked a criticaland thorough overview. This book fills that gap. It is the first to provide thenecessary critical comparison of concepts and approaches, with an emphasis on theirpossible applications. It presents key concepts and their theoretical background,including the concepts of robustness and modularity and their exploitation to studybiological systems; the best-known modeling approaches, and their advantages anddisadvantages; lessons from the application of mathematical models to the study ofcellular biology; and available modeling tools and datasets, along with theircomputational limitations.
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