BE Seminar Series: Heterogeneous, Multi-Scale and Patient-Speci C Pharmacodynamic Systems Models for Cancer with Clinical Applications

Abstract: Systems models of key signaling pathways in cancer have been extensively used to understand and explore the mechanisms of action of drugs and growth factors on cancer cell signaling. In general, such models predict the effect of mechanical or chemical stimuli (for e.g. drug dosage) in terms of activity of one or more key downstream proteins such as ERK or AKT which are important regulators of cell fate decisions. Although such models are greatly useful and have helped uncover important emergent properties of signaling networks such as ultrasensitivity, bistability and oscillations, they miss many key features that would make them useful in a clinical setting.

1) The predictions of activity of proteins such as ERK or AKT cannot be directly translated into a clinically useful cell fate parameter such as cell kill rate.

2) They don’t work as well when there are multiple biological processes operating under different time and length scales such as receptor based signaling (4-6 hours) and cell cycle (24-48 hours).

3) They cannot incorporate important cellular physics like mechanics of the cell membrane, ECM and the cytoskeleton.

4) The parameter space of such models often exhibits sloppy/stiff character which affect the accuracy of predictions and the robustness of these models.  Such analysis are often not done which casts doubt on validity of the predictions.

Here we have developed a multiscale and multiparadigm framework for systems and pharmacodynamic models that helps us address some of the above shortcomings. This framework was used to successfully integrate a single-cell systems model of ErbB receptor mediated Ras-MAPK and PI3K/AKT pathway with tumor suppressor p53 mediated DNA damage response and cell cycle pathway. The integrated model was used in a clinical setting using gene/protein expression data and drug dosage/schedule information from actual patients of prostate adenocarcinoma. Special mathematical techniques were used to develop algorithms that can integrate models of disparate time scales and time resolutions (continuous vs. discrete time).