Subjective Information in Life Processes: A Computational Case Study

Abstract

Information processing has increasingly gained traction as a unifying and holistic concept to characterize biological systems. Current research has obtained important but limited results in applying information to understanding life, mainly because of inherent syntactic constraints embedded in a universally accepted theory, formulated for communication system engineering, rather than a universal characterization of nature. In this paper, we further the notion of "subjective information", which takes into account the relative importance of different information sources for distinct life functions. To this end, we develop a computational model of a microorganism that requires two metabolic substrates to survive and grow. The substrates have different spatial distributions, and the organism acquires information on their environmental concentrations and gradients through a noisy receptor-binding process, ultimately guiding its chemotaxis in the environment to increase the chances of growth and survival. Our simulation results reveal 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, suggesting that a form of "subjective information" promotes growth and survival in life processes, rather than the classical, purely syntactic Shannon information.