Peter J. Thomas – “On the Relationship between Information Processing and Fitness in Biology”

Abstract

Information transmission and storage have gained traction 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 scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviours 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 then utilized in a simulation framework constructed specifically to reveal the emergence of “subjective information”, i.e., the 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 higher meaning for survival.

Brief Biography

Prof. Peter Thomas is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University, Cleveland, Ohio, USA, with courtesy appointments to the Department of Electrical and Computer Engineering and the Department of Biology. His research concerns information processing and information and control theory in biological systems.