ubjective Information and Survival in a Simulated Biological System

Authors

Tyler S. Barker, University of Nebraska-LincolnFollow
Massimiliano Pierobon, University of Nebraska-LincolnFollow
Peter J. Thomas, Case Western Reserve UniversityFollow

ORCID IDs

Massimiliano Pierobon https://orcid.org/0000-0003-1074-6925

Peter J. Thomas https://orcid.org/0000-0001-7533-6770

Date of this Version

4-2-2022

Citation

Barker, T.S.; Pierobon, M.; Thomas, P.J. Subjective Information and Survival in a Simulated Biological System. Entropy 2022, 24, 639. https://doi.org/10.3390/ e24050639

Comments

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

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 behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this 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 emergence of a “subjective information”, i.e., 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.