SPEAKER: 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

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.

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