University of the Andes
Carlos F. Lopez lab
The advent of quantitative techniques to probe biomolecular-signaling processes have led to increased use of mathematical models to extract mechanistic insight from complex datasets. These complex mathematical models can yield useful insights about intracellular signal execution but the task to identify key molecular drivers in signal execution, within a complex network, remains a central challenge in quantitative biology. Hence, my research project consists in developing new approaches using physical chemistry, statistical clustering, and tropical algebra formalisms to identify signaling drivers and characterize dynamic signal processes within a network.