Semantic Information in MC: Chemotaxis Beyond Shannon

TL;DR

This paper introduces a semantic-information framework for molecular communication by applying Kolchinsky–Wolpert theory to a computational model of bacterial chemotaxis. Using an agent-based model, it quantifies how environmental information flows (via transfer entropy) to a bacterium that senses nutrient gradients, and how this information utilization relates to survival (viability) under interventions that limit perception or mobility. The work demonstrates that higher environmental adaptability, reflected in larger transfer entropy and a higher observed semantic information, correlates with improved survival in challenging environments, while in easier environments the relationship saturates. The framework thus provides a goal-oriented, information-theoretic lens for designing and evaluating MC systems in dynamic biological settings, potentially guiding nanoscale applications like targeted drug delivery and early disease detection.

Abstract

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.

Figures (5)

• Figure 1: Schematic illustration of a CB (cyan ellipsoid) at three different time instances performing gradient sensing to locate a nutrient source (purple area) that is constantly releasing nutrients (purple dots). The CB senses its environment and switches from tumble mode to run mode to follow a positive nutrient gradient, while switching back to tumble mode when the sensed number of nutrients decreases.
• Figure 2: Considered computational bacterial chemotaxis model. Bacterium (cyan) seeks to follow nutrient source (pink) by switching between run and tumble modes according to the nutrient (red) concentration it observes in its local environment.
• Figure 3: Viability as a function of the simulation time steps for different interventions, which start after 25 steps.
• Figure 4: The mutual information as a function of the simulation time steps for different interventions, which start after 25 steps. Nutrient source movement gradually (dotted) (jumping (solid)) indicates a nutrient source moving a maximum distance of $d_{\mathrm{max}} = 3$ ($d_{\mathrm{max}} = 8$) every $m = 5$ ($m = 25$) steps. The results for intervention $\iota=\mathrm{d}$ (brown) are shown for different numbers of simulation realizations $E$.
• Figure 5: Transfer entropy vs. viability at time step $k=200$ for three different nutrient production rates and different interventions. The shaded areas highlight transfer entropies of interventions resulting in (nearly) constant viability values characterized by $\epsilon$, cf. \ref{['eq:it:sem_inf']}; the cyan arrows indicate the corresponding viability limits.