Scientists develop realistic model to explain cellular signal processing

Mar 13 2012

Scientists at the Charité - Universitätsmedizin Berlin and the US National Institutes of Health (NIH) have developed a realistic model for explaining cellular signal processing. This new method will be applied to the systems biological analysis of how heart muscle cells function and to model particular data from tumor tissue from lung cancer patients. The results of the work are published in the current issue of the journal Nature Methods.

Metabolic and regulatory cell paths are characterized by a large number of interacting components. The formulation and use of detailed mathematical descriptions of cellular processes may help understand these complex systems better and allow for predications about the complex behavior of biological systems. In their work, the scientists introduce a new type of method to model and simulate cellular signal processing. With the method described, it is possible for the first time to realistically connect dynamic biochemical and morphological changes to each other. This allows knowledge about biology derived from experiments, for example the interplay between individual molecules, to be realistically translated into computer models. "An additional advantage of our method is the user-friendliness of the software. It allows physicians and biologists with no knowledge of mathematical physics to develop or modify complex biological models," explains Dr. Frederick Klauschen from the Charité Institute of Pathology.

With these models, experiments can be conducted on the computer enabling scientists to test conceptual models and hypotheses about the function of physiological and pathological processes. For example, one can simulate what happens to a cell's modeled signal network when one changes (or mutates) a particular molecule.

Thus, the method can make a valuable contribution in the future to "personalized medicine" by integrating the results of molecular-pathological diagnostics into a systems biology approach enabling a better understanding of pathological changes.