Data-driven mechanistic models of morphology-controlled Ras oncogenic signaling

Project summary – Data-driven mechanistic models of morphology-controlled Ras oncogenic signaling. Cells adapt their shape during differentiation and transformation processes, or when exposed to environmental changes or drug challenges. Accordingly, cell geometry is often considered as a marker for cancer diagnosis and prognosis. However, whether cell geometry itself modulates the oncogenic signaling programs underlying these behaviors is understudied. This proposal provides a training plan for my long-term career to study how cell geometry controls oncogenic signaling that is implicated in cell behaviors such as survival and proliferation. Recent observations in our lab revealed that cell morphology drives key oncogenic processes. For example, bleb formation – hemispherical membrane protrusion – promotes survival signaling in suspended melanoma cells; or the formation of lamellipodia and ruffles promotes a proliferation signaling pathway in Ras-transformed cancers that is independent of the canonical MAP kinase signaling. In both cases, we have some evidence that the narrow spaces of blebs and lamellipodia upregulate signaling events because of scaffolding and diffusion restriction of molecules, which in turn elicits feedback that would not be activated in wider volumes. However, the detailed interplay between constrained morphology and nonlinear signaling effects remains unclear. To address this, experimental approaches must be complemented by computational approaches. My broad research goal is to develop integrated computational platforms that allow us to quantitatively unravel the mechanisms by which cell morphology drives cell signaling. I propose an imaging-guided modeling pipeline that will test the capacity of different formulations of reaction-diffusion systems in realistic geometries to recapitulate the experimentally measured signaling distributions across different cell morphologies. I will drive the calibration of free model parameters and the assessment of the model quality by direct matching of simulated and observed signaling distributions, as developed in my recent paper (PMID: 38177778). Given a calibrated model, I will test the mechanisms by which membrane protrusions – blebs and lamellipodia/ruflles – amplify cell survival and proliferation signals in Ras-mutated cells. In my R00 phase, I aim to develop a novel computational framework to implement 3D live-cell imaging data and study reaction-diffusion systems in dynamic boundaries, where cell morphodynamics govern signaling dynamic. To enhance my background in biophysics, and mathematics, I will pursue additional training in computational modeling, cell biology and systems biology during the K99 phase. The Danuser lab at UT Southwestern is ideal for this training. Dr Gaudenz Danuser is a pioneer in using computation to solve biological questions in cancer biology. Under his supervision, I’ll train to become a computational cancer biophysicist, initiating (K99) and leading (R00) the systems biological tools for studies of morphology-controlled signaling pathways.