Computational biochemistry: systems biology minireview series.

Understanding how biological processes work and are regulated requires that the biochemical mechanisms underlying the processes be described. The study of biochemical mechanisms permeates all of biological and biomedical sciences. A description of biochemical mechanisms requires multiple approaches. Over the past 50 years, many of these approaches have been largely experimental. The isolation, purification, and characterization of proteins and other cellular components, as well as elucidation of how their functions are regulated, have all added to our understanding of the biochemical basis of biological processes. The rapid intertwined growth of biochemistry and molecular biology has resulted in a large “parts list” and “parts specifications.” As the parts list has grown, the assembly of cellular components into functional units that are subject to regulation and control has become a major focus of current research. Systems with few components continue to be successfully analyzed using purely experimental approaches. As the number of components and interactions increases, however, intuitive reasoning coupled with experiments becomes insufficient to provide an understanding of how these systems function. Such systems are best understood in quantitative terms. Even in the absence of formal mathematical theories, numerical simulations (i.e. computation) based on empirical experimental observations are often very useful for drawing mechanistic inferences of how biochemical systems are organized, function, and are regulated. This has led to the resurgence of computation in regulatory biochemistry over the past decade. Mathematical reasoning and numerical simulations together form the field of computational biochemistry. This approach has long been used to understand biochemical mechanisms. Enzyme kinetics, a well developed area in computational biochemistry, has provided us with a mechanistic understanding of how proteins and enzymes work. Key concepts in cooperativity, i.e. the mechanisms of multiple interacting binding reactions such as that for oxygen to hemoglobin (1, 2), enzymatic reaction mechanisms (3, 4), and allosteric regulation (5, 6), have all been developed by mathematical reasoning and computational analyses that were tightly coupled to experiments. The mathematical basis for enzyme kinetics was sufficiently well developed by the mid-seventies to yield textbooks such as that by Segel (7). Understanding how two coupled biochemical reactions lead to regulatory behavior such as ultrasensitivity (8) has been useful in demonstrating the switch-like behavior of the MAPK2 pathway (9). As systems with greater numbers of components have been analyzed, numerical simulations have been useful in identifying those positive feedback loops that have switch-like characteristics (bistable behavior). Theoretical predictions from numerical simulations that a system of enzymes, e.g. MAPK-phospholipase A2-protein kinase C, can behave as a switch to trigger physiological responses (10) have recently been experimentally confirmed in long-term depression of synaptic responses in the cerebellum (11). These advances have led to a growing recognition that computations and simulations are likely to play a complementary role to experiments in regulatory biochemistry. Much in the same way that molecular biology technologies were major drivers for the identification, isolation, and characterization of cellular components, computational biochemistry is likely to be the major driver for systems biology, providing a mechanistic understanding of the functional regulation of biochemical systems at the cellular and subcellular levels. It seems reasonable to hypothesize that computational biochemistry will be the major approach to understanding mechanisms in systems biology. The minireviews in this series describe some areas of success where computational biochemistry has provided mechanistic insight into the regulation and function of complex systems. At the outset, it should be noted that although diverse, these reviews do not cover all areas of computational biochemistry. For instance, computations that have provided insight into protein folding and other structural computations are not covered. These reviews focus on reactions between molecules and the regulation of these reactions. Linderman describes how modeling G protein-coupled signaling pathways has been valuable not only in understanding basic mechanisms of signal transduction such as collision coupling (12) but also in drug discovery and development of disease models. Pollard and Berro consider one of the most successful examples in regulatory biochemistry: actin cytoskeleton-dependent cell movement. In this system, regulation of actin polymerization into microfilaments and depolymerization underlie the origins of force within cells. Mathematical models have helped us to understand how this mechanotransduction process is responsible for the “physical integrity of the cell” and regulated cell movement. In this review, one can get a feel for the full range of ways in which modeling helps us understand the propagation of regulatory behavior from the three-dimensional structure of actin monomers to the organization of actin filaments to how filament structure drives movement at the leading edge of the cell. Kearns and Hoffmann describe studies of NF-κB that have been useful in developing the concept of cell state-dependent rate-limiting steps and the role of different isoforms of the same protein in contributing to complex behavior. Neves and Iyengar discuss how spatially limited reactions within cell signaling networks contribute to the specificity of receptor action. Computational analyses of spatially restricted biochemical reactions have helped identify spatial information as a distinct entity that is transmitted through cell signaling networks. Ma’ayan introduces the utility of graph theory in dealing with large data sets and inferring mechanisms from these data sets. Network analysis has been very useful in understanding “the global regulatory features” of large biochemical systems. Schellenberger and Palsson describe emerging methods for dynamical studies of metabolic networks based on whole genomes. Models of metabolic networks have long been used to obtain a mechanistic understanding of how organization of network leads to metabolic control (13, 14). In this review, new approaches that help convert static network maps into large-scale dynamic systems provide a glimpse of how the future of computational biochemistry is likely to look. These reviews have been written to emphasize the systems level mechanistic insights that computation provides rather than to focus on the methods used for computation. The supplemental data of Pollard and Berro and Neves and Iyengar provide brief descriptions of methods and approaches. As do all fields of research, computational biochemistry and systems biology have some unavoidable jargon often originating in mathematics and engineering. The supplemental data of Pollard and Berro define some of the terms used. With this preview, there is a supplemental glossary that complements the list of Pollard and Berro. These glossaries should be useful for experimental biochemists who read these reviews. Often, the authors also define terms within a review. This planned partial redundancy should allow each review to be read on its own and also as a set. We hope that these reviews will stimulate many authors and readers of the Journal to consider using computational approaches in their own research.