Modeling Cells Adds Up

Computational Cell Biology Edited by C.P. Fall, E.S. Marland, J.M. Wagner, and J.J. Tyson New York: Springer (2002). 468 pp. $59.95Lately, the words “computation” and “cell biology” have begun to appear noticeably together in public, after a decade or more of discreet liaison. The 30th Annual Stetten Memorial Symposium at the National Institute of General Medical Sciences in October 2002 proudly featured the topic “Computational Cell Biology” to a capacity audience at NIH. At the December 2002 American Society for Cell Biology meeting, a session entitled “Computational Approaches in Cell Biology” attracted throngs of attendees frustrated by an inability to even enter a packed lecture hall. More recently still, the 2nd Annual Conference on Computational Cell Biology in March 2003 is oversubscribed in registration. In the emerging post-genomic era, the recognition that computational methodologies will play a critical role in systems biology is widespread. Cell biologists have realized the impracticality of trying to successfully predict cell behavior arising from complex molecular mechanisms using only pure intuition. Accordingly, the cell biology community is now seeking a suitable avenue for enabling them to add computational tools to their cell biology research kits. One route may be undertaking formal academic courses, which remain sparse, while another is finding helpful texts whether in formal curricula or not.The late Joel Keizer of the University of California at Davis began preparing such a text, but sadly its completion was interrupted by his untimely death in 1999. Fortunately, a cadre of Joel's friends in the computational biology field have finished the task, with the product being this excellent volume of 13 Chapters (plus 3 tutorial Appendices) by a total of 17 authors, all of whom are recognized authorities in their respective areas. Each chapter is essentially self-contained, although care has been taken to ensure a relatively even and admirable degree of depth, detail, and style. Most chapters are devoted to explicating construction and analysis of specific biological topics, including cell membrane ion channels, transporters and pumps, intracellular calcium kinetics, metabolic enzyme networks, molecular motors, and cell cycle control. Dispersed among these are a smaller number of chapters aimed at explaining fundamental modeling methods based on differential equations: ordinary differential equations (only one independent variable, usually time) and partial differential equations (multiple independent variables, typically time and one or more spatial dimension) in both deterministic and stochastic forms. Deterministic models yield the average behavior of a number of individual cells, whereas stochastic models yield diverse realizations of many possible individual cells. Instruction in the computational software XPPAUT, not too dissimilar from the more familiar MATLAB software, is also explicitly provided in a helpful manner. Interested potential readers should be warned, however, that the mathematical treatment is nontrivial, and the writing by-and-large is quite dense, so it will not be generally smooth treading without some previous background in matrix algebra and differential equations. There is no question that the content will be most easily accessible to students from an engineering, physics, or applied mathematics background. At the same time, the biological content is given less emphasis, so that such students will want some supplementary material to further describe the fascinating complexity of cellular processes that motivates the need for computational analysis.This brings us to some discussion of the nature of the biological content itself. The scientific topics described in these chapters represent very interesting problems that have been most broadly and intensely subject to productive computational modeling study over the past decade or so, and are thematically connected primarily by a common modeling perspective. In particular, all the topics are considered in terms of direct physico-chemical mechanisms, are modeled in terms of differential equations, and deal with dynamics of molecular processes (appropriately and importantly including both biochemistry and biophysics/biomechanics facets of dynamics). Some other areas at the forefront of cell biology that could fall into this combination of categories are neglected; among these I might suggest protein and nucleic-acid regulatory processes underlying gene expression, ligand/receptor binding and trafficking processes, and protein signal transduction networks. These are all exciting areas that should benefit tremendously from increased devotion of computational modeling efforts. From a wider perspective, many dynamic molecular-level cell processes can be analyzed productively via computational techniques other than differential equations characterizing physico-chemical mechanisms. That is, while differential equations derived from underlying mechanistic hypotheses are wonderfully capable of simulating dynamic processes to generate predictions based on those hypotheses, at least two other outstanding challenges in cell biology that could benefit from computational analysis methods can be posed.One challenge is the elucidation of hypotheses, in the first place, for operation of a cellular process for experimental systems in which the key molecular components and the topology of their interactions are not well established. An enticing promise of systems biology, in its genomics and proteomics and metabolomics (etc.) mode, is efficient production of experimental data that can be “mined” in an unbiased, “data-driven” manner to glean potential relationships among variables of interest. The types of computational techniques employed for data-mining tasks are typically based on statistical or vector space methods rather than on differential equations, and are not treated in this particular text. The resulting potential relationships, of course, suggest physico-chemical hypotheses for operation of the molecular components represented by the “omics” variables, which can then be transformed into differential equation-based computational models for testing predictions thereof. This hypothesis-formation step is by now the “traditional” domain of bioinformatics, but it is nonetheless a significant area for computational cell biology in facilitating a swift growth in the number of experimental problems that can be addressed by the kind of physico-chemical models articulated in this text at hand.An additional challenge is how to recover intuitive insights about operation of a cell process of interest, even when successful simulation of the process by a physico-chemical differential equation model has been achieved. Computational prediction of the outcome of the next experiment is essential for building confidence in the validity of the model hypotheses, but does not necessarily simultaneously bring intuitive understanding about why that outcome was obtained. Here again the type of mathematics that can be brought to bear on this question may go beyond what is presented in this text. For instance, Boolean logic, Bayesian network, or Markov chain methods may be gainfully employed to discern how certain variables influence others or how multiple variables combine to influence an outcome.Thus, as the field of computational cell biology continues to grow, I anticipate further benefit will arise from additional texts that comprise a broader set of cell biology topics and a more comprehensive battery of computational approaches, and address a greater scope of cell biology problems across hypothesis generation, hypothesis testing, and intuition development. But achieving that comfortable marriage will likely require a decade of continuing work by many researchers tackling a variety of particular problems. In the meantime, this current book is certainly the best available for the present early courtship stage, and I will recommend it enthusiastically to all my own students and colleagues.