Evaluating mixed HTC/cloud approaches for parameter sweep applications in systems biology

In biology, dynamical models are used to understand and predict the behavior of biochemical systems composed of numerous species and reactions. Model parameters represent aspects of the studied system and, therefore, modifications of model parameters correspond to perturbations of the real system. Several methods of model analysis are based on model perturbation by means of its parameter values. Thus, the execution of parameter sweep applications (PSAs) over large parameter spaces becomes very CPU intensive and time consuming. In this paper, we describe how to perform PSA employing a stochastic simulator of chemical kinetics to explore the behavior of a biological model. We created a distributed computing model that enables the user to create mixed cloud/HTC (High Throughput Computing) workflows by combining the powerful distributed systems and the flexibility of the cloud. As a case study, we investigated the impact of the parameters of a bacterial chemotaxis model by considering four different PSAs in which we reproduced the behavior of the system under various conditions by using our stochastic simulator. We accomplished this study by evaluating some existing cloud/HTC approaches used to address biology problems and reliable to deal with PSAs of systems biology models and to handle the simulations required in sensitivity analysis and parameter estimation.