Systems Biology: Principles, Methods, and Concepts

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With extraordinary clarity,the Systems Biology: Principles, Methods, and Concepts focuses on the technical practical aspects of modeling complex or organic general systems. It also provides in-depth coverage of modeling biochemical, thermodynamic, engineering, and ecological systems. Among other methods and concepts based in logic, computer science, and dynamical systems, it explores pragmatic techniques of General Systems Theory. This text presents biology as an autonomous science from the perspective of fundamental modeling techniques. A complete resource for anyone interested in biology as an exact science, it includes a comprehensive survey, review, and critique of concepts and methods in Systems Biology.

Systems Biology: Principles, Methods, and Concepts Review

Building on precursors like general systems theory, cybernetics, chaos theory, cellular automata theory, catastrophe theory, and the work of Robert Rosen, systems biology has emerged during the past decade as an increasingly popular approach to try to deal with the complexity of biological systems.

This particular book aims to present relevant "principles, methods, and concepts" of systems biology, and consists of an edited collection of nine chapters written by various authors. Unfortunately, I'm not as enthused as other reviewers about this book, mainly for two reasons: (a) while some of the chapters are good and useful, others are rather esoteric and seem out of place, such as two chapters which cover thermodynamics in a way that makes very little explicit connection with biology, and (b) the book overemphasizes the computer science perspective at the expense of other perspectives.

Nevertheless, I did get something out of the book. First, here are some key points related to structure and behavior of biological systems:

(1) Biological systems are complex because of their large numbers of components and large numbers of nonlinear interactions between the components. These interactions can involve both competition and cooperation among components, and tend to create network structures, often organized hierarchically such that networks are nested within other networks in a modular way.

(2) Biological systems display emergence, anticipation, and closed-loop endogenous interaction involving both upward and downward causation. They also display internal redundancy and functional robustness, thus giving them ability to adapt to perturbations from changing environments. Part of the price paid for this biological robustness is some inefficiency ("overhead") in the system, which can reduce relative fitness in some circumstances.

(3) Due to nonlinearities, biological systems are often poised "at the edge of chaos," exhibiting a combination of order and chaos (extreme sensitivity to initial conditions, thus giving the appearance of randomness). This sensitivity enables quick regulatory responses, thus contributing to robustness; conversely, loss of sufficient chaos can potentially lead to pathology (however counterintuitive that may seem).

(4) Beyond chaos, biological systems often have genuinely stochastic aspects, which thus diminish our intuitive understanding of them, as well as our ability to predict and control their behavior. The combination of deterministic and stochastic aspects can be said to give biological systems "propensities". These propensities may involve contingencies which can be complex, unique, and irreversible, leading to the possibility of non-repeatable behavior which is path-dependant and historical.

(5) Considering the behavioral characteristics of biological systems as a whole, process may be as important or more important than structure and components.

A second set of key points relates to modeling of biological systems:

(1) Models are only incomplete and approximate representations of reality. Therefore, there can be multiple complementary and overlapping models in biology, and the need for such may itself be considered a definition of complexity.

(2) Though not logically rigorous, abduction (inference to the best explanation from available evidence) is often necessary in development of models.

(3) The process of developing models can itself be a source of valuable insight, even if particular models themselves ultimately prove inadequate.

(4) While reductionism can be useful to gather data (eg, using high-throughput techniques), "reconstructionism" is necessary to build mathematical and computational models which turn this data into knowledge.

(5) One reason reductionism isn't adequate in biology is that the behavior of systems is affected by their environments, so this full context needs to be considered.

(6) Closed-form mathematical models have often used ordinary differential equations, but partial differential equations are typically needed to account for spatial effects. Nonlinearity makes use of both types of models more difficult (though power laws can sometimes provide useful approximations of nonlinearity). This often leads to the need to revert to computational simulations, although such simulations often provide only limited insight.

(7) Biological systems often require models which are too complex to be easily communicated, and the trend has been for models of biological systems to become larger and more complex. Ultimately, these models will need to be underpinned by theoretical foundations in order to develop a genuine theory of biology suitable for applications such as prevention and treatment of disease.

Overall, because of the reasons noted above (and because better books are available), I can't strongly recommend this book, but it may still be worth your time and money if you have a strong interest in this topic.

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