Predicting the Effects of Parameters Changes in Stochastic Models through Parallel Synthetic Experiments and Multivariate Analysis

Abstract

Usually researchers require many experiments to verify how biological systems respond to stimuli. However, the high cost of reagents and facilities as well as the time required to carry out experiments are sometimes the main cause of failure. In this regards, Information Technology offers a valuable help: modeling and simulation are mathematical tools to execute virtual experiments on computing devices. Through synthetic experimentation, researchers can sample the parameters space of a biological system and obtain hundreds of potential results, ready to be reused to design and conduct more targeted wet-lab experiments. A non negligible achievement of this is the enormous saving of resources and time. In this paper, we present a plug-in-based software prototype that combines high performance computing and statistics. Our framework relies on parallel computing to run large numbers of synthetic experiments. Multivariate analysis is then used to interpret and validate results. The software is tested on two well-known oscillatory models: Predator-Prey (Lotka-Volterra) and Repressilator.