The Birtwistle Lab combines computational and experimental methods to understand how cancer cells make decisions, focusing on single cell methods to elucidate how phenotypic variability arises from noisy signaling. We give particular focus to brain tumors, although the approaches we take are quite flexible and can be applied to a multitude of cancer types. We use chemical kinetics theory to represent the various signaling mechanisms controlling proliferation and death as systems of equations which can be simulated to predict how cancer cells stochastically respond to drugs or other perturbations. We constrain and validate these models with a variety of single cell data from flow and mass cytometry that measures many protein and protein modification levels across a cell population, and live-cell microscopy that allows optogenetic manipulation of cell signaling and the ability to simultaneously measure cell fate and signaling processes in the same single cells with high temporal frequency. Our goal is to use the predictive capability of these models to understand better how the panel of genetic alterations and mutations in particular patient’s brain tumor dictates indications for available chemotherapeutic options, providing a potential avenue towards improved therapies.
In this context, we are generally interested in (1) developing new and improving existing methodologies for how to construct, simulate, discriminate, and validate mathematical models of mammalian signaling and cell-fate decision processes such as those involved in transformation, progression and development of drug resistance, (2) understanding the sources and controllability of biological noise, and the consequences of this noise for practical manipulation of signaling and phenotypes, and (3) generating predictive mathematical models of cellular signaling processes to enable rational design and control of cell-fate decisions in biological systems (natural and engineered).