Computational Modeling of Genetic and Biochemical Networks

In 1982, at a symposium commemorating the tenth anniversary of the death of the biologist and modeler Aharon Katzir-Katchalsky, George Oster recalled Katchalsky joking that "Biologists can be divided into two classes: experimentalists who observe things that cannot be explained, and theoreticians who explain things that cannot be observed." [1] The joke is still funny because it sums up in one statement the traditionally awkward alliance between theory and experiment in most fields of biology as well as science in general. The statement that experimentalists "observe things that cannot be explained" mirrors the view of many traditional theorists that biological experiments are for the most part simply descriptive ("stamp collecting" as Rutherford may have put it) and that much of the resulting biological data are either of questionable functional relevance or can be safely ignored. Conversely, the statement that theorists "explain things that cannot be observed" can be taken to reflect the view of many experimentalists that theorists are too far removed from biological reality and therefore their theories are not of much immediate usefulness. In fact, of course, when presenting their data, most experimentalists do provide an interpretation and explanation for the results, and many theorists aim to answer questions of biological relevance. Thus, in principle, theorists want to be biologically relevant, and experimentalists want to understand the functional significance of their data. It is the premise of this book that connecting the two requires a new approach to both experiments and theory. We hope that this book...