Mechanistic Hypotheses Exploration of Cellular Processes using Probabilistic Model Selection and Multi-omics Integration

Carlos F. Lopez, PhD
Lead, Multiscale Modeling Group (CIH)
Principal Scientist, SI3 Group
Altos Laboratories
Friday, December 13, 2024
11:00am - 12:00pm
James H. Clark Center, Room S360, 3rd floor next to the Coffee Shop
Zoom link
Abstract: Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a need for both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypotheses underlying a system’s behavior. To overcome these limitations, we introduce a mechanistic hypotheses exploration workflow that leverages probabilistic inference, AI, and multi-omics integration, to quantify how a given mechanistic hypotheses can explain an experimental dataset, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe models of cellular responses to perturbations in cancer and non-disease settings. We find that our approach greatly accelerates identification of causal relationships through model selection and model averaging to eliminate hypotheses that are either not supported or poorly supported by available data. Taken together, our predictions provide a methodology to develop testable hypothesis mechanistic interpretation in complex cellular processes.
Biography: Carlos F. Lopez, received his BSc and BLA degrees from the University of Miami and his PhD in Physical Chemistry from the University of Pennsylvania. He pursued a postdoctoral position at the University of Tex-as at Austin where he studied theoretical biophysics of protein solvation. He pursued further training at Harvard Medical school where he attained the prestigious HW Pierce/King Trust Research Fellowship and developed novel methods to bridge scales in the study of cellular processes. He subsequently moved to Vanderbilt University at the end of 2012 as an Assistant Professor of Cancer Biology, Biomedical Informatics, and faculty member of the Vanderbilt-Ingram Cancer Center, Center for Quantitative Sciences, Center for Structural Biology, Institute for Chemical Biology, and Quantitative Systems Biology Center. In 2017, he moved to the Department of Biochemistry as part of an administrative reorganization within Vanderbilt University. He was promoted to Associate Professor (with tenure) in May of 2019. In 2022, he moved to California to become the Multiscale Modeling Group Lead and Principal Scientist at Altos Labs. He has been the recipient of multiple awards, honors, and fellowships including the NIH K22 Transition Career Development Award (2011), American Association for Cancer Research – Minority Scholar in Cancer Research Award (2012), Vanderbilt-Ingram Cancer Center Young Ambassadors Award (2013), Vanderbilt Provost Office Out-standing URM Accomplishments Award (2014, 2015, 2016), Leadership Alliance SR-EIP Faculty Mentor Commendation (2015), and the National Science Foundation CAREER Award – the highest honor conveyed by this organization to junior faculty. He was appointed as the Vanderbilt University Liaison to Oak Ridge National Laboratory in 2017-2019, where he served in the ORNL steering committee to guide the interactions between the national lab and Vanderbilt University. He is also an active advocate for underrepresented individuals in science through his membership of the Maximizing Access Committee in ASBMB and more recently the Changing the Face of Science committee at Altos Labs. His work employs novel computational modeling tools in combination with experimental data to explain and predict how intracellular molecular interactions give rise to cell-decision processes and cell-community behaviors. The overall goal of his work is to attain a mechanistic and predictive understanding of dynamic cellular systems, how they are regulated in healthy cells, dysregulated in aging or diseases, and leverage this knowledge to guide experiments toward novel therapies. To this end he develops novel theories and numerical methods to explain how systems-level bio-chemical interaction networks process biochemical signals and lead to a phenotypic outcome.