| 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. |