Biological processes are highly stochastic in nature. Furthermore, based on the three-body problem, living systems may exhibit chaotic behavior even though there is a strong push by evolution to put homeostatic mechanisms in place. It may be that in many large groupings of cells the overall phenotype is the result, not only of one state, but the average of two or more distinct states, possibly alternating between each other. To complicate things even further it is often the case that nature, to solve a problem, picks modules “off the shelf” thereby creating true biological noise from those parts of the module turned on, which are not directly involved in the solution of the problem.
This makes modeling a biological system, from its phenotype, extremely difficult. High throughput approaches have helped in understanding how wild-type systems behave in ‘real time’; but, because of lots of experimental noise, and the difficulty of the approach involved, these methods have been little more than hypothesis generators, rather than giving true knowledge about the studied system.
It may be possible, however, to coordinate the results from various high throughput approaches to improve the level of confidence about different aspects of a model, and thus get a better picture, by observing from all angles and comparing these snapshots. A software suite like BioSystAnSe, designed to modularly grow with ease, as methods to investigate a system from new angles are developed, may help in this objective.
- Robert J. Feezor, Henry V. Baker, Michael Mindrinos, Doug Hayden, Cynthia L. Tannahill, Bernard H. Brownstein, Adrian Fay, Sandra MacMillan, Jason Laramie, Wenzhong Xiao, Lyle L. Moldawer, J. Perren Cobb, David Schoenfeld, Ronald W. Davis, Ronald G. Tompkins, M.D., Sc.D., and the Inflammation and Host Response to Injury Large Scale Collaborative Research Program, (2004): Whole Blood and Leukocyte RNA Isolation for Gene Expression Analyses.