Biomedical Engineering Reference
In-Depth Information
Systems Biology
Just as genomics research, which focuses on sequencing of human and other genomes, is being
supplanted by proteomic research as the work of sequencing has become commonplace, proteomic
research has a limited lifespan as well. Eventually, the dozens of computer-based methods of protein
structure modeling and the numerous tools that support these methods will be replaced by one or
two accepted methods, and the focus of the bioinformatics community will move up another level
toward functional proteomics. Following this progression to its natural conclusion, the focus of
bioinformatics will eventually converge with clinical medicine at the cellular and organ-system
level—so-called systems biology.
A major challenge in modeling and simulating systems biology is integrating high- and low-level
models so that a more accurate picture of the entire biological process can be obtained. Integrating
models of protein structure and function with those of biochemical pathways promises to provide
insight into disease processes and, by extension, the most efficacious designer drugs.
Although some researchers are working with systems biology today, for the most part they are
limited by both data and computational methods and power. A single cell might contain tens of
thousands of molecules, each interacting with each other in complex ways not yet understood.
Furthermore, not only must researchers understand the function of normal cells, but they must be
able to model and simulate cells involved in cancer or HIV, for example.
Today, the focus is on what can be practically accomplished with current technology and data, such
as creating physiologically complete models and simulations of the heart, pancreas, and liver.
Although very broad clinical simulations of these and other organs have been developed for teaching
purposes, the kinds of models applicable to drug research are at a much greater level of detail and
complexity and require Linux clusters or a mainframe to run them in real time. With time, these
requirements will be more easily met, as affordable desktop computing power continues to increase
in performance. What remains is for researchers to discover how to best apply this hardware toward
solving the next generation of bioinformatics challenges.
