Dr. Daniel Lobo Department of Biological Sciences University of Maryland, Baltimore County
Abstract: Extracting mechanistic knowledge from spatial and temporal phenotypes is a current challenge due to the complexity of biological regulation and their feedback loops. Furthermore, these regulatory interactions can include biophysical forces shaping a developing organism, creating complex interactions responsible for emergent patterns and forms. In this talk I’ll present how an approach combining computational and molecular systems biology can aid in the understanding of biological growth and form from a mechanistic perspective. This methodology integrates the mathematical modeling of gene regulation, metabolic networks, and tissue growth and patterning with dynamical systems, the automatic reverse engineering of parameters or complete equations from phenotypic data with machine learning, and the generation of precise computational predictions that can be tested at the bench. We have successfully applied this approach to obtain mechanistic insights in developmental, cancer, and synthetic biology.
Bio: Dr. Daniel Lobo is an Assistant Professor at the University of Maryland, Baltimore County. He obtained his PhD in Computer Science from the University of Malaga in Spain, and completed a postdoc in Developmental, Regenerative, and Cancer Biology at Tufts University. His research in systems biology aims to understand, control, and design the dynamic regulatory mechanisms governing complex biological processes. To this end, his lab develops new computational methods, mathematical models, and formalized databases together with molecular assays at the bench to reverse-engineer biological mechanisms from experimental data and design new regulatory networks for specific functions. They seek to discover the mechanisms of development and regeneration, find therapies for cancer and other diseases, and streamline the application of systems and synthetic biology. Dr. Lobo received an Outstanding Investigator Award (R35) in 2020 from the National Institutes of Health and his work has been featured in popular media such as PBS, Popular Mechanics, and Wired.
Please use the link below to join the Zoom Meeting:
https://ucr.zoom.us/s/95408873063
Meeting ID: 954 0887 3063
Password: 638902
Organizers: Weitao Chen, Mark Alber