Cell signaling pathways as control modules: complexity for simplicity?

Abstract

As biology begins to move into the “postgenomic” era, a key emerging question is how to approach the understanding of how complex biomolecular networks function as dynamical systems. Prominent examples include multimolecular protein “machines,” intracellular signal transduction cascades, and cell–cell communication mechanisms. As the proportion of identified components involved in any of these networks continues to increase, in certain instances already asymptotically, the daunting challenge of developing useful models—mathematical as well as conceptual—for how they work is drawing interest. At one extreme is the hope that fundamental relationships will emerge from essentially statistical analyses of large genomic and proteomic databases enumerating correlations among gene expression, protein level/state/location, and cell behavior. At another extreme is a view that sheer computational power can be harnessed to create comprehensive simulations of the full set of fundamental physicochemical molecular interactions. Recently, an intermediate concept suggests a “modular” framework, treating subsystems of complex molecular networks as functional units that perform identifiable tasks—perhaps even able to be characterized in familiar engineering terms (1). The idea of functional modules as an effective approach to modeling biomolecular systems is quite appealing, because, even in nonbiological applications, engineering design is generally carried out in hierarchical or “nested” fashion. That is, the behavior of a system at the highest (i.e., largest space scale and/or longest time scale) level is typically analyzed and predicted with a model involving properties of the next-lower space/time scales; these properties are then analyzed and predicted with another set of models involving further subdivided space and/or time scales and so forth to a most detailed level as limited by current data.