How are signalling networks rewired in cancer? Are there vulnerabilities in that process that could be exploited?
While first line therapies for many cancer types exist, these same therapies impose selective pressures that lead to cancer evolution and drug resistance. Can we understand, predict and exploit these resistance trajectories? Could we design therapies that are less prone to the most common therapeutic resistance mechanisms?
“What I cannot create, I cannot understand” (Richard Feynman, 1988).
Do we understand proteins, their interactions and molecular recognition patterns enough to design them ab initio?
The combination of computational and experimental technologies (we are effectively a hybrid/“soggy” lab) as well as our collaborative nature both within our group and with other labs is part of our Team values.
The increasing ability to generate large datasets in biology has highlighted even further the need to use and develop computational approaches, such as AI/ML, to learn patterns in the data and generate testable hypotheses.
Equally indispensable is the ability to experimentally test these hypotheses and generate insightful experimental data to feed any computational model and theoretical framework.
Whereas protein biochemistry has traditionally been low-to-middle throughput, we are using and developing new technology that expands our ability to generate biochemical experimental data.
*Some icons created by Javier Cabezas, Re Gara, Bakunetsu Kaito, dDara, Alberto Villar, Weltenraser Alexander Skowalsky, Panda Icons for the Noun Project