Barker Tyler S, Pierobon Massimiliano, Thomas Peter J
School of Computing, College of Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA.
Entropy (Basel). 2022 May 2;24(5):639. doi: 10.3390/e24050639.
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.
信息传输和存储作为统一概念已受到关注，用于描述生物系统及其在多个尺度上的生存和进化机会。尽管基于信息的数学框架有潜力为生命过程以及与生命过程交互和控制的方式提供新见解，但主要遗产仍来自香农，在香农的理论中，信息对系统的评分是基于其最大信息效率进行的纯粹句法特征描述。后一种度量标准似乎并不完全适用于生物系统，在生物系统中，不同信息片段（携带不同语义）的传输和存储会导致不同的生存机会。基于一个抽象数学模型，该模型能够捕捉一群单细胞生物的参数和行为，其生存与从环境中检索信息相关，本文探讨了经典信息论与生物学之间的上述脱节。在本文中，我们提出了一个被指定为计算状态机的模型，然后将其用于专门构建的模拟框架中，以揭示“主观信息”的出现，即生命系统从环境中最大化获取信息的能力与其随时间最大化生长和生存之间的权衡。模拟清楚地表明，与获取较少信息但对生存具有更高意义的策略相比，最大化信息效率的策略会导致较低的生长速率。
