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"Interweaving physical models and machine learning: microscopes, Markov models, and molecular design\n\nAbstract\n\nRealizing the full potential of modern biotechnology requires integration of advanced experimental and computational tools. Rapid improvements in efficiency and precision of data acquisition provide an opportunity to leverage computational models to solve biological problems and control biological systems. This talk will describe advances in data-driven and systems modeling approaches that empower biological discovery and design. The first part of the presentation will detail real-time control of gene expression in individual cells by use of advanced microscopy, machine learning, and stochastic modeling. In the second part of the seminar, I will describe the use of machine learning to bridge the gap between drug-like molecular structures and the constraints of human physiology. Molecular properties are computed using a language model, which is pretrained on extensive databases of molecular sequences and evaluated with human pharmacokinetic models. Together, these research efforts highlight the potential impact that tight coupling of machine learning and traditional systems modeling can have on biotechnological and pharmaceutical research.\n\nBio\n\nZach Fox is a Research Scientist in the Advanced Computing for Health group at Oak Ridge National Laboratory. There, he develops data-driven and systems modeling approaches to solve biophysical problems in systems relevant to human health. Prior to joining ORNL in the fall of 2022, he was a postdoctoral researcher in the Center for Nonlinear Studies at Los Alamos National Lab and at Institut Pasteur. Fox holds a PhD in computational and systems biology from Colorado State University, where he worked with Brian Munsky."
<https://calendar.utk.edu/calendar/day/2023/12/5/2>