Anisotropic Diffusion Model of Communication in 2D Biofilm

A biofilm is a microbial city. It consists of bacteria embedded in an extracellular polymeric substance (EPS) that functions as a protective barrier. Quorum sensing (QS) is a method of bacterial communication, where autoinducers (AIs) propagate via diffusion through the EPS and water channels within the biofilm. This diffusion process is anisotropic due to varying densities between the EPS and water channels. This study introduces a 2D anisotropic diffusion model for molecular communication (MC) within biofilms, analyzing information propagation between a point-to-point transmitter (TX) and receiver (RX) in bounded space. The channel impulse response is derived using Green's function for concentration (GFC) and is validated with particle-based simulation (PBS). The outcomes reveal similar results for both isotropic and anisotropic diffusion when the TX is centrally located due to symmetry. However, anisotropic conditions lead to greater diffusion peaks when the TX is positioned off-center. Additionally, the propagation of AIs is inversely proportional to both overall biofilm size and and diffusion coefficient values. It is hypothesized that anisotropic diffusion supports faster responses to hostile environmental changes because signals can propagate faster from the edge of the biofilm to the center.

Biophysical Model for Signal-Embedded Droplet Soaking into 2D Cell Culture

Using agar plates hosting a 2D cell population stimulated with signaling molecules is crucial for experiments such as gene regulation and drug discovery in a wide range of biological studies. In this paper, a biophysical model is proposed that incorporates droplet soaking, diffusion of molecules within agar, cell growth over an agar surface, and absorption of signaling molecules by cells. The proposed model describes the channel response and provides valuable insights for designing experiments more efficiently and accurately. The molecule release rate due to droplet soaking into agar, which is characterized and modeled as the source term for the diffusion model, is derived. Furthermore, cell growth is considered over the surface, which dictates the dynamics of signaling molecule reactions and leads to a variable boundary condition. As a case study, genetically-modified $E.\,coli$ bacteria are spread over the surface of agar and Isopropyl-beta-D thiogalactopyranoside (IPTG) is considered as a signaling molecule. IPTG droplets are dropped onto the bacteria-covered agar surface. The parameters for the IPTG molecule release rate as a diffusion source into the agar are estimated from this experiment. Then, a particle-based simulator is used to obtain the spatio-temporal profile of the signaling molecules received by the surface bacteria. The results indicate that the number of molecules reacting with or absorbed by bacteria at different locations on the surface could be widely different, which highlights the importance of taking this variation into account for biological inferences.

Abnormality Detection and Localization Schemes using Molecular Communication Systems: A Survey

Abnormality, defined as any abnormal feature in the system, may occur in different areas such as healthcare, medicine, cyber security, industry, etc. The detection and localization of the abnormality have been studied widely in wireless sensor networks literature where the sensors use electromagnetic waves for communication. Due to their invasiveness, bio-incompatibility, and high energy consumption for some applications, molecular communication (MC) has been introduced as an alternative approach, which enables promising systems for abnormality detection and localization. In this paper, we overview the MC-based abnormality detection and localization schemes. To do this, we propose a general MC system for abnormality detection and localization to encompass the most related works. The general MC-based abnormality detection and localization system consists of multiple tiers for sensing the abnormality and communication between different agents in the system. We describe different abnormality recognition methods, which can be used by the sensors to obtain information about the abnormality. Further, we describe the functional units of the sensors and different sensor features. We explain different interfaces for connecting the internal and external communication networks and generally model the sensing and communication channels. We formulate the abnormality detection and localization problem using MC systems and present a general framework for the externally-controllable localization systems. We categorize the MC-based abnormality detection schemes based on the sensor mobility, cooperative detection, and cooperative sensing/activation. We classify the localization approaches based on the sensor mobility and propulsion mechanisms. Finally, we provide the ongoing challenges and future research directions to realize and develop MC-based systems for detection and localization of the abnormality.