Daniel Lobo is an Associate Professor at the University of Maryland, Baltimore County and member of the Program in Oncology at the University of Maryland Marlene and Stewart Greenebaum Cancer Center.
His research in Systems Biology aims to understand the regulation and mechanisms of biological growth, shape, and pattern formation. To this end, his interdisciplinary lab combines both molecular and computational methods to gain a mechanistic understanding of development and regeneration, find personalized treatments for cancer, and streamline regulatory designs in synthetic biology. They have developed new techniques for the acquisition and formalization of microscopy spatial data, the formulation of mechanistic models of genetic, metabolic, and signaling networks, and high-performance machine learning methods for inferring such models. They combine these computational methods with molecular assays at the bench for obtaining quantitative spatial and dynamic data, such as gene expression and morphological outcomes, and for prediction validation including genetic, surgical, and pharmacological perturbations and the engineering of synthetic circuits.
This ambitious research plan will have a long-term payoff for the streamline of our ability to infer and design dynamic mechanisms, which will be essential for advancing fundamental understanding in biology and developing new medical and industrial applications.