Similarity measures for automated comparison of in silico and in vitro experimental results

The overwhelming complexity of biological systems prevents exhaustive description of the processes and mechanisms that cause system functionality. There are large numbers of processes to be considered with options for manifold hypotheses describing each. The long-term goal of this project, for a particular biological system, is to put the computer to work weeding out the weaker hypotheses and, even, weeding out posited processes that do not pertain directly to specific functionality. An objective towards this goal is to build a computational framework to host an ongoing competition for the most effective structural description of what goes on inside an organ, in this case the liver. In order to do that, one needs robust algorithms for comparing the data taken from biological experiments with the data taken from the simulation. In this paper, we begin to delineate and survey algorithms by which to compare the output of any given simulation with data taken from experiments.