Development of a data-integrated ODE and agent-based simulation framework that captures and predicts responsiveness to targeted cancer therapeutics

As a logical follow-up to PN 2-1 A/B, we will develop a novel data-integrated ODE - and agent-based simulation framework that captures and predicts responsiveness to targeted cancer therapeutics. This relates to the class of BH3 mimetics and TRAIL -based death receptor agonists, both of which have entered clinical trials and are studied by us using combined experimental and computational approaches. Our prior work demonstrated that data-integrated simulation of death decisions in cancer cells, as affected by these treatments, can successfully classify qualitative response categories in cancer cell lines and patient tumor tissues. We now require deeper insight into the signaling dynamics and system behaviors that orchestrate death decision making. We will approach this by a novel hybrid modelling approach combining ODE- and agent-based methods, which allow us to better understand key inter-compartmental signaling, and to identify optimal and individualized therapeutic interventions. Eventually, we will strive for a transition from qualitative to quantitative predictions of treatment outcomes, based on which simulation-based  patient stratification and smarter clinical trial designs can be realized.