Semantic Information and Channel Capacity: A Unified Perspective for the Molecular Channel

Communication is essential for biological and technological systems alike. Generally, increasing the volume of transmitted information is a priority for communication systems design. However, what if communication in living systems, which are subject to energy and noise constraints, is reliant on efficiently communicating "precise" information as opposed to "more" information? This question motivates the concept of semantic information. This study explores the trade-off between maximizing information transfer versus selecting semantically important information transfer in Biomolecular Communication Channels (BioMC). Specifically, we assume that a biological system needs to receive information to survive. This defines a viability function. We assume that maximizing information transfer optimizes the viability function by providing the maximum information to the receiver. We also assume that reducing information exchange reduces resource consumption. With this formulation, we propose a data-driven algorithm that seeks a transmission strategy that optimizes the viability function while reducing the information exchange. We validate the proposed methodology on in-silico data. Overall, this study provides a new framework for investigating semantic information transfer in BioMC.