Expected Density of Cooperative Bacteria in a 2D Quorum Sensing Based Molecular Communication System

Abstract

The exchange of small molecular signals within microbial populations is generally referred to as quorum sensing (QS). QS is ubiquitous in nature and enables microorganisms to respond to fluctuations in living environments by working together. In this study, a QS-based molecular communication system within a microbial population in a two-dimensional (2D) environment is analytically modeled. Microorganisms are randomly distributed on a 2D circle where each one releases molecules at random times. The number of molecules observed at each randomly-distributed bacterium is first derived by characterizing the diffusion and degradation of signaling molecules within the population. Using the derived result and some approximation, the expected density of cooperative bacteria is derived. Our model captures the basic features of QS. The analytical results for noisy signal propagation agree with simulation results where the Brownian motion of molecules is simulated by a particle-based method. Therefore, we anticipate that our model can be used to predict the density of cooperative bacteria in a variety of QS-coordinated activities, e.g., biofilm formation and antibiotic resistance.

Characterization of Cooperators in Quorum Sensing With 2D Molecular Signal Analysis

“…QS enables coordination within large groups of cells, potentially increasing the efficiency of processes that require a large population of cells working together. Microscopic populations utilize QS to This work was presented in part at IEEE Globecom 2019 [1]. complete many collaborative activities, such as virulence, bioluminescene, biofilms, and the production of antibiotics.…”

“…We validate the accuracy of our analytical results via a particle-based simulation method where we track the random walk of each signaling molecule over time. In contrast to our preliminary work in [1], which only derives a portion of the results in 1) and the expected number of cooperators, this paper conducts a more comprehensive analysis of the 2D channel response, derives an exact expression for the expected probability of cooperation, and studies the distribution of the number of cooperators by its MGF and different order statistics.…”

Joint Sensing, Communication and Localization of a Silent Abnormality Using Molecular Diffusion

“…1) Collaborative sensors: The sensors used in micro-scale MC are engineered cells, bacteria or artificial nanomachines. Some engineered bacteria behave socially based on Quorum sensing [29], [30]. It means that they simultaneously respond to an environmental change by cooperating with each other.…”

Characterization of Cooperators in Quorum Sensing with 2D Molecular Signal Analysis

“…We compare the Poisson and Gaussian distributions with the derived statistics to approximate the probability mass function We validate the accuracy of our analytical results via a particle-based simulation method where we track the random walk of each signaling molecule over time. In contrast to our preliminary work in [1], which only derives a portion of the results in 1) and the expected number of cooperators, this paper conducts a more comprehensive analysis of the 2D channel response, derives an exact expression for the expected probability of cooperation, and studies the distribution of the number of cooperators by its MGF and different order statistics.…”