Achievement

Sensitivity analysis (SA) is a powerful tool to study properties of complex systems in many disciplines. However, the high computational cost required for repeated Monte Carlo simulations limits SA of stochastic chemical reaction networks. To overcome this, IGERT Trainee Patrick Shephard developed two methods for more efficient SA, adopting concepts from probability and statistics. However, his methods and others can be difficult to implement and remain inaccessible to many researchers in biology. To address this issue, Patrick developed an open-source software package, SPSens, incorporating several efficient SA methods into a common interface. With SPSens users can easily switch between algorithms to efficiently perform SA on their particular problem. SPSens allows researchers--especially those in systems biology--to utilize advanced, state-of-the-art algorithms for efficient SA of complex stochastic reaction networks.