Thursday, September 22, 2022
3:30 p.m., Avery 115
4:30 p.m., Avery 115
Peter Thomas, Ph.D.Professor, Case Western Reserve University
Information transmission and storage have gained popularity as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information characterizes systems on the basis of their maximal information efficiency. In established literature one can find the claim that optimal information efficiency is necessarily a condition of optimal biological fitness. The work to be discussed challenges this view. Indeed, purely syntactic information measures seem not entirely suitable for biological systems, where transmission and storage of different types of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, our recent paper explores the aforementioned disconnect between classical information theory and biology. In this paper we present a model, specified as a computational state machine, which is utilized in a simulation framework constructed specifically to study the emergence of ``subjective information,” i.e., a trade-off between a living system’s capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a greater semantic relevance for survival.
Peter Thomas is a professor in the Department of Mathematics, Applied Mathematics and Statistics at Case Western Reserve University. He received his Ph.D. in mathematics from the University of Chicago, did postdoctoral work in computational neurobiology at the Salk Institute for Biological Studies in La Jolla, Calif, and taught at Oberlin College, before joining CWRU in 2006. He has held visiting positions at Ohio State University and Humboldt University (Berlin, Germany). His work has been supported by the National Institutes of Health, the National Science Foundation, the Simons Foundation, and the Council for the International Exchange of Scholars (Fulbright Program). His research interests include mathematical neuroscience, computational cell biology, and the application of information theory and control theory to theoretical biology. Using a combination of mathematical analysis and computational modeling, he works closely with biological collaborators to understand principles of communication and control in a variety of biological systems. Current projects include (1) studying the effects of random ion channel gating on spontaneously firing nerve cells, (2) combining bottom-up and top-down models for the interaction of central pattern generator circuits with biomechanical motor control systems, and (3) analyzing the effects of molecular fluctuations on communication in signal transduction pathways.