An Automated Derivation of Differential Equations for Cumulants Defined by Stochastic Cancer Models

Abstract: Stochastic cancer models are becoming a valuable tool for hypothesis evaluation as well as for risk assessment. Cumulants derived from stochastic cancer models are among the most useful quantities that cancer models can provide. But the computation of those cumulants entails a formidably tedious process, i.e., the symbolic derivation of involved ordinary differential equations satisfied by those cumulants. The presentation offers an automation of this symbolic process. Implemented with Mathematica, the automation applies to an abundant array of stochastic cancer models.