Computational framework of parameter estimation of mechanistic models in cancer.
Much of mechanistic cancer modeling revolves around studying the long-term success of co-evolving, complex interaction-driven tumor growth combined with statistical outcomes of treatment. Often, the data available to parameter these mechanistic models is underpowered and it is difficult to accurate the patient-to-patient variability in small, cohort studies. To improve our confidence in parameterization of mechanistic models, I’ve developed a computational pipeline for parameterizing of both in vitro and clinical cohort ordinary differential equation (ODE)-based model systems.