A genetic programming based methodology for variable interaction determination in multivariate dynamical systems

In many systems, the determination of variable interaction structures using data is of central importance. For example, biological systems mainly comprise of a cascade of various interrelated metabolites/reactions such that the occurrence of one event depends on the occurrence of a prior event or a set of events of the same or different nature. This time dependent inter-correlation between the variables, once deciphered, can help in gaining a better understanding the mechanisms governing the systems and paves way for their manipulation. In the present paper, genetic programming and standard parameter estimation techniques are utilized to determine such relationships from noise corrupted datasets.