The recently emerging molecular communication (MC) paradigm intents to leverage communication engineering tools for the design of synthetic chemical communication systems. These systems are envisioned to operate on nanoscale and in biological environments, such as the human body, and catalyze the emergence of revolutionary applications in the context of early disease monitoring and drug targeting. However, while a plethora of theoretical (and more recently also more and more practical) MC system designs have been proposed over the past years, some fundamental questions remain open, hindering the breakthrough of MC in real-world applications. One of these questions is: What is a useful measure of information in the context of MC-based applications? While most existing works in MC build upon the concept of syntactic information as introduced by Shannon, in this paper, we explore the framework of semantic information as introduced by Kolchinsky and Wolpert for the information theoretical analysis of a natural MC system, namely bacterial chemotaxis. Exploiting the computational modeling tool of agent-based modeling (ABM), we are able to demonstrate that the semantic information framework can provide a useful information theoretical framework for quantifying the information exchange of chemotactic bacteria with their environment. In particular, we show that the measured semantic information provides a useful measure of the ability of the bacteria to adapt to and survive in a changing environment. Encouraged by our results, we envision that the semantic information framework can open new avenues for developing theoretical and practical MC system designs and in this way help to unleash the full potential of MC for complex adaptive systems-based nanoscale applications.

翻译：新兴的分子通信（MC）范式旨在利用通信工程工具设计合成化学通信系统。这些系统被设想在纳米尺度和诸如人体等生物环境中运行，并有望在早期疾病监测和药物靶向等领域催生革命性应用。然而，尽管过去几年已提出大量理论（以及近来越来越多的实践）MC系统设计方案，但一些基本问题仍悬而未决，阻碍了MC在实际应用中的突破。其中一个问题是：在基于MC的应用中，什么是有用的信息度量？尽管现有多数MC工作建立在香农提出的句法信息概念基础上，但本文探索了由科尔钦斯基和沃尔珀特提出的语义信息框架，用于对自然MC系统（即细菌趋化性）进行信息理论分析。利用基于智能体建模（ABM）的计算建模工具，我们能够证明语义信息框架可为量化趋化细菌与环境的信息交换提供有用的信息理论框架。特别是，我们表明测量的语义信息为衡量细菌适应和生存于变化环境的能力提供了有用度量。受此结果鼓舞，我们预见语义信息框架可为发展理论和实践MC系统设计开辟新途径，从而帮助释放MC在基于复杂自适应系统的纳米级应用中的全部潜力。