Semantic Information in MC: Chemotaxis Beyond Shannon
<|reference_start|>Semantic Information in MC: Chemotaxis Beyond Shannon: The recently emerged molecular communication (MC) paradigm intends to leverage communication engineering tools for the design of synthetic chemical communication systems. These systems are envisioned to operate at 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. Despite the abundance of theoretical (and recently also experimental) MC system designs proposed over the past years, some fundamental questions remain unresolved, hindering the breakthrough of MC in real-world applications. One of these questions is: What can be a useful measure of information in the context of MC applications? While most existing works on 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-theoretic analysis of a natural MC system, namely bacterial chemotaxis. Exploiting computational agent-based modeling (ABM), we are able to quantify, for the first time, the amount of information that the considered chemotactic bacterium (CB) utilizes to adapt to and survive in a dynamic environment. In other words, we show how the flow of information between the environment and the CB is related to the effectiveness of communication. Effectiveness here refers to the adaptation of the CB to the dynamic environment in order to ensure survival. Our analysis reveals that it highly depends on the environmental conditions how much information the CB can effectively utilize for improving their survival chances. Encouraged by our results, we envision that the proposed semantic information framework can open new avenues for the development of theoretical and experimental MC system designs for future nanoscale applications.<|reference_end|>
@article{brand2024semantic,
    title={Semantic Information in MC: Chemotaxis Beyond Shannon},
    author={Lukas Brand, Yan Wang, Maurizio Magarini, Robert Schober, and
  Sebastian Lotter},
    journal={arXiv preprint arXiv:2402.18465},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18465},
    primaryClass={cs.IT math.IT}
}