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

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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.
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