Hot Topics in Research
Hot Topics in Research is an educational lunch-and-learn series designed to inform the UTHSC community of opportunities and resources available to them. Attendees will learn about a variety of topics, from institutional support and tools to new policies, regulations and trending topics in the areas of health science education and research.
2023 Lineup
April 25, 2023
12:00pm - 1:00pm
Freeman Auditorium, 930 Madison Avenue, 3rd Floor
"The Future of Biology: Artificial Intelligence, Machine Learning, Robot Scientists,
and Self-Driving Laboratories?"
John P. Wikswo, PhD
University Distinguished Professor of Biomedical Engineering, Molecular Physiology
& Biophysics, and Physics
A.B. Learned Professor of Living State Physics
Founding Director, Vanderbilt Institute for Integrative Biosystems Research and Education
A.B. Learned Professor of Living State Physics
Founding Director, Vanderbilt Institute for Integrative Biosystems Research and Education
Adjunct Professor, Department of Graduate Education, College of Graduate Health Sciences,
The University of Tennessee Health Science Center
The complexity of biology is legendary, and the classic approach to research has been
for an individual scientist to drill down to a manageable level of detail, for example
one’s favorite protein or cell. Systems biology takes a broader view by embracing
complexity that spans multiple scales in space and time, but faces the challenge of
identifying an appropriate subset of biological variables and interactions that can
be managed within the structure of contemporary research groups using state-of-the-art
multi-omic technologies and computational models. As the breadth and depth of systems
biology models continue to grow, so does the difficulty in generating the many testable
hypotheses needed to validate and expand the models, designing and conducting the
requisite experiments, interpreting the data so as to test each hypothesis, and iterating
the process. The resulting geometric or possibly exponential increase in complexity
of both models and experiments suggests that there will be a proportional decrease
in the rate of scientific progress, unless scientific discovery can be radically accelerated.
Artificial intelligence, machine learning, robot scientists, and self-driving laboratories
could revolutionize an individual’s ability to address ever-harder biological problems.