Computational biology

Computational biology has emerged as an analogue to the development of molecular biology as a discipline in its own right. The application of mathematical and computational tools to all areas of biology is producing many exciting results, providing insights into biological problems too complex for traditional analysis, and has become a new discipline within biological and computer sciences. There is a consensus among all observers that biology, regardless of the subspecialty, is overwhelmed with a large amount of very complex data. However, what sets biology apart from other data-rich fields is the complexity rather than the sheer volume of the data produced. In contrast to other datarich fields, biology remains a scientific “cottage industry,” with data generation done in a highly distributed mode, without standard format or syntax. Thus all areas of the biological sciences have urgent needs for the development of organized and accessible storage of biological data. Unfortunately, traditional relational database technology is inadequate to serve many areas of the biological sciences due to the complexity of biological data and the absence of a standardized data structure. It is clear that collaboration between computer scientists and biologists will be necessary to design information platforms that accommodate the needs for variation in the representation of biological data. The distributed nature of the data acquisition system, the variable demands placed on different data sets, and the absence of adequate algorithms for data comparison, which forms the basis of biological science, add further complexity. During the last decade there were dramatic advances in instrumentation and related methodologies for both light and electron microscopy. The advances lie not merely in higher resolution, but rather in a broader size range of structures that can be analyzed, more powerful methods for putting together the pieces of three-dimensional puzzles of cell form, and the addition of dynamic details of biological form and function, ranging from the subcellular to the physiological level. These new techniques are computationally demanding, and efforts to develop new computational approaches are under way in laboratories around the world. For example, a recurrent problem in all imaging techniques is the existence of artifacts due to incomplete data collection. These artifacts may seriously interfere with the interpretation of the reconstruction, and may even lead to incorrect conclusions. This problem is most serious in electron microscopy, where a full range of viewing angles is not usually accessible, and data corresponding to a cone or wedge in Fourier space cannot be collected. In confocal microscopy, the resolution in the z direction (parallel to the optical axis) is much lower than in the x-y directions, as reflected by a nonisotropic instrumental point-spread function. Although several restoration algorithms have existed for some time, only the recent increases in computational speed have made their practical implementation possible. Software solutions for problems such as these are necessary in many areas; however, new soft-