Computational cell biology

All of us who do experiments and think about biology on a daily basis know that biological systems are exceedingly complex. Biologists have long avoided facing up to the interconnected nature of cellular processes by using reductionist approaches. This strategy has been spectacularly successful in identifying key factors for many fundamentally important biological processes. However, the traditional methods of cell and molecular biology are not very well suited to illuminating the big picture of how the manifold cellular events we have discovered are interconnected. As we explore in increasing detail the molecular nature of cells and organisms, it is becoming unavoidably clear that our simple, reductionist models can be incomplete, inaccurate, or entirely incorrect. Pathways are rarely linear, but are usually branched and interconnected ; proteins rarely interact with just a few partners, but generally form extensive interaction networks; and cellular components rarely just reside and act in one place, but dynamically shuttle between cellular locals where they may participate in various functions. The recent wave of genomic, proteomic, and other "-omic " strategies has made us acutely aware of the convoluted nature of biological systems. Clearly, the level of complexity that is emerging in biological systems is beyond what we can comprehend using conventional, often qualitative, cell and molecular– biological methods. To gain a global picture of biological systems, we need help in acquiring, processing, analyzing, and interpreting the multitude of interactions and dynamic events that occur in a cell at any given time. Computational methods are now coming to our rescue. Computational methods have permeated the field of cell biology over the last decades. Most of us now use image analysis software, structural modeling programs, and sequence alignment tools on a daily basis without appreciating that they are computational applications. Up to now, these methods have largely been the supporting cast to the established major experimental tools of cell biologists. In addition, the limited mathematical training of most cell biologists has slowed the wide acceptance of computation analysis in cell biology. Perhaps the time has come, however, to acknowledge the dawn of a new age in cell biology, where computation is an equal to the more conventional molecular tools. This new era in cell biology promises to reveal entirely novel concepts of cellular organization and function that cannot be extracted using traditional cell biological approaches. This issue of the Journal of Cell Biology contains three reviews on computational …