Abstract:
Many biological decision-making processes can be viewed as performing a classification task over a set of inputs, using various chemical and physical processes as “biological hardware.” In this context, it is important to understand the inherent limitations on the computational expressivity of classification functions instantiated in biophysical media. Here, we model biochemical networks as Markov jump processes and train them to perform classification tasks, allowing us to investigate their computational expressivity. We reveal several unanticipated limitations on the input-output functions of these systems, which we further show can be lifted using biochemical mechanisms like promiscuous binding. We analyze the flexibility and sharpness of decision boundaries as well as the classification capacity of these networks. Additionally, we identify distinctive signatures of networks trained for classification, including the emergence of correlated subsets of spanning trees and a creased “energy landscape” with multiple basins. Our findings have implications for understanding and designing physical computing systems in both biological and synthetic chemical settings.
Bio:
Dr. Suri Vaikuntanathan is a Professor in the Department of Chemistry at the University of Chicago. His research focuses on developing and using tools of equilibrium and non-equilibrium statistical mechanics to understand the behavior of complex systems in physical chemistry, soft condensed matter physics, and biophysics.
Prior to joining the University of Chicago, Dr. Vaikuntanathan completed his postdoctoral research at the University of California, Berkeley (2014). He received his Ph.D. in Chemical Physics from the University of Maryland, College Park (2011), where he worked with Dr. Christopher Jarzynski. He earned his B.Tech in Biotechnology from the Indian Institute of Technology - Madras, India (2006).
His contributions to the field have been recognized with several honors, including the Early Career Award in Theoretical Chemistry (2023), Camille Dreyfus Teacher-Scholar Award (2020), NSF CAREER Award (2018), and the Alfred P. Sloan Fellowship (2017).
The Physics Frontiers Centers (PFC) program supports university-based centers and institutes where the collective efforts of a larger group of individuals can enable transformational advances in the most promising research areas. The program is designed to foster major breakthroughs at the intellectual frontiers of physics by providing needed resources such as combinations of talents, skills, disciplines, and/or specialized infrastructure, not usually available to individual investigators or small groups, in an environment in which the collective efforts of the larger group can be shown to be seminal to promoting significant progress in the science and the education of students. PFCs also include creative, substantive activities aimed at enhancing education, broadening participation of traditionally underrepresented groups, and outreach to the scientific community and general public.