The research, from Drexel University in Philadelphia and Children's Hospital Boston, will appear in the June issue of PLOS Computational Biology.
Lead author Andres Kriete, Associate Professor at Drexel's School of Biomedical Engineering, Science and Health Systems, said: "One important goal of computational approaches in aging is to develop integrated models of a unifying aging theory in order to better understand the progression of aging phenotypes grounded on molecular mechanisms."
The study relates progressive damage and dysfunction in aging, dubbed a vicious cycle, to inflammatory and metabolic stress response pathways.
Interestingly, the activation of these pathways remodels the inner functioning of the cell in a protective and adaptive manner and thus extends lifespan.
This is the first time that scientists have applied fuzzy logic modelling to the field of aging.
Co-author Dr. William Bosl said: "Since cellular biodynamics in aging may be considered a complex control system, a fuzzy logic approach seems to be particularly suitable."
Dr. Bosl, a staff scientist in the Informatics Program at Children's Hospital Boston, developed a fuzzy logic modelling platform called Bionet together with a cell biologist, Dr. Rong Li of the Stowers Institute for Medical Research in Kansas City, to study the complex interactions that occur in a cell's machinery using the kind of qualitative information gained from laboratory experiments.
Fuzzy logic can handle imprecise input, but makes precise decisions and has wide industrial applications from air conditioning to anti-lock break systems in cars, using predefined rules.
In a similar fashion, the aging model relies on sets of rules drawn from experimental data to describe molecular interactions.
Co-author Glenn Booker, who is Faculty at the College of Information Science and Technology at Drexel, said: "Integration of such data is the declared goal of systems biology, which enables simulation of the response of cells to signalling cues, cell cycling and cell death."pplications in aging are currently geared towards deciphering the underlying connections and networks.
Dr. Kriete said: "We have to realize that the real strength of computational systems biology in aging is to be able to predict and develop strategies to control cellular networks better as they may be related to age related diseases and our approach is just a first step in this direction."