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



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

Author: Zoltan Szallasi

Publisher: The MIT Press

Genres:

Publish Date: March 24, 2006

ISBN-10: 0262195488

Pages: 464

File Type: PDF

Language: English

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Book Preface

Biology is the study of self-replicating chemical processes. Biology is the study of systems accurately transmitting a genetic blueprint. Biology is the study of complex adaptive reproducing systems.

What is systems biology if all definitions of biology implicitly or explicitly refer to the study of a whole object, whether it is a virus, a cell, a bacterium, a protozoan or a metazoan? We treat systems biology as the quantitative study of biological systems, aided (or hindered) by technological advances that both permit molecular observations on far more inclusive scales than possible even 15 years ago, and permit computational analysis of such observations. Thus, for the purposes of this book, systems biology is the promise of biology on a larger and quantitatively rigorous scale, a marriage of molecular biology and physiology. Concretely, this defines the focus of the book: data-centric quantitative modeling of biological processes and systems.

Biology is an experimentally driven science simply because evolutionary processes are not understood well enough to allow theoretical advances to rest on terra firma. Systems biology is experimentally driven, computationally driven, and knowledge driven. It is experimentally driven because the complexity of biological systems is difficult to penetrate without large-scale coverage of the molecular underpinnings; it is computationally driven because the data obtained from experimental investigations of complex systems need extensive quantitative analysis to be informative; and it is knowledge driven because it is not computationally feasible to analyze the data without incorporating all that is already known about the biology in question. Furthermore, the use of data, computation and knowledge must be concurrent. Available knowledge guides experiment design, novel knowledge is generated by the computational analysis of new data in light of available knowledge, and the cycle repeats.

The difference between knowledge and data is central to understanding the underpinnings of systems biology. The sequencing of whole genomes is a good example. Any given genome is data. Without extensive analysis, it is just as uninformative about biological processes as a photograph of the night sky. First steps in transforming a genome into knowledge include identifying genes, identifying transcription factor binding sites, finding the transcription factor complexes that control the expression of the genes, and finding the chromatin structure in the cell being studied, to determine which genes are accessible for transcription. While this is wildly optimistic in terms of the knowledge that can be extracted from the genome data, it is still nowhere close to the level of understanding required to make predictions about the response of an organism to a specific stimulus. A reductionist approach to biology is bootless because complex adaptive systems are inherently nonlinear, so their behavior is well summarized by the statement: the whole is more than the sum of the components


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