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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.
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Subjective Information in Life Processes: A Computational Case Study
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.
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- PAR ID:
- 10297251
- Date Published:
- Journal Name:
- NANOCOM '21: Proceedings of the Eight Annual ACM International Conference on Nanoscale Computing and Communication
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3477206.3477454
