Subjective Information and Survival in a Simulated Biological System
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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/5/639 |
| _version_ | 1797500012160090112 |
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| author | Tyler S. Barker Massimiliano Pierobon Peter J. Thomas |
| author_facet | Tyler S. Barker Massimiliano Pierobon Peter J. Thomas |
| author_sort | Tyler S. Barker |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:55:43Z |
| format | Article |
| id | doaj.art-07dac57f6f0d40f7b0227acfb317aadd |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T03:55:43Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-07dac57f6f0d40f7b0227acfb317aadd2023-11-23T10:55:00ZengMDPI AGEntropy1099-43002022-05-0124563910.3390/e24050639Subjective Information and Survival in a Simulated Biological SystemTyler S. Barker0Massimiliano Pierobon1Peter J. Thomas2School of Computing, College of Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USASchool of Computing, College of Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USADepartment of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH 44106, USAInformation 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.https://www.mdpi.com/1099-4300/24/5/639mutual informationbiologyforagingchemotaxisgrowth ratesubjective information |
| spellingShingle | Tyler S. Barker Massimiliano Pierobon Peter J. Thomas Subjective Information and Survival in a Simulated Biological System Entropy mutual information biology foraging chemotaxis growth rate subjective information |
| title | Subjective Information and Survival in a Simulated Biological System |
| title_full | Subjective Information and Survival in a Simulated Biological System |
| title_fullStr | Subjective Information and Survival in a Simulated Biological System |
| title_full_unstemmed | Subjective Information and Survival in a Simulated Biological System |
| title_short | Subjective Information and Survival in a Simulated Biological System |
| title_sort | subjective information and survival in a simulated biological system |
| topic | mutual information biology foraging chemotaxis growth rate subjective information |
| url | https://www.mdpi.com/1099-4300/24/5/639 |
| work_keys_str_mv | AT tylersbarker subjectiveinformationandsurvivalinasimulatedbiologicalsystem AT massimilianopierobon subjectiveinformationandsurvivalinasimulatedbiologicalsystem AT peterjthomas subjectiveinformationandsurvivalinasimulatedbiologicalsystem |