Under the Molecular Communications-motivated perspective of abstracting the problem of early-detection of bacterial infections as system with a clearly defined sender (bacterial infection), channel (tissue) and receiver (Bio-Nano machine), the focus of research conducted in 2022 was set on the investigation of an bacteria-based Bio-Nano receiver/sensor.
This sensor’s integral component consists of engineered bacteria which are genetically altered to sense an analyte (e.g., Quorum Sensing signal) and in response produce a measurable light signal through bioluminescence/fluorescence.
The intricate bio-chemical processes, from molecule uptake into the bacterial cells to the expression of bioluminescence (LuxABCDE) genes which underly this process need to be closely investigated to derive reliable quantitative models which describe and predict a bacterial-sensor’s response to a signal.
To that end, mathematical models have been derived which are based on both literature study of relevant scientific publications in microbiology, and empirical data. These models consist of several differential equations following well-studied bio-chemical principles (General-Mass Action, Chemical Langevin, Michaelis-Menten kinetics) that capture the time evolution of chemical substances involved in the reaction-cascade initiated by the target signal, ultimately yielding an expression for the resulting luminescence signal.
Both numerical and analytic approaches have been investigated and relevant system parameters are derived utilizing the developed model and procured empirical data.
The experimental data has been collected in a series of initial Wet Lab in-vitro assay-based experiments which resulted in time series luminescence data for various input concentrations of the target signal.
While the initial project description anticipated a focus on the investigation of bacterial infections by the gram-negative Pseudomonas aeruginosa strain, considerations regarding feasibility, accessibility and based on expertise of the involved biologists, the attention of the project was directed to the analysis of the gram-positive Streptococcus mutans strain which can be found in the oral cavity of mammals and whose infections can lead to the host’s tooth decay.
The focus of future research will lie on further mathematical modeling with the clear goal of deriving comprehensive figures capturing crucial dynamics of a bacteria-based sensor (e.g., noise analysis for signal-to-noise levels). Furthermore, particle-based Monte-Carlo simulations capturing the stochastic nature of the investigated molecule-based system will be conducted. It is anticipated that experimental focus will shift towards micro-fluidic set-up, as these allow for extremely precise observations of cellular dynamics with resolutions down to single-cell level. The integration of living bacteria into an actual physical nano-sensor will be a further strong project focus in 2023.
Our ability to miniaturize sensors and electronics is unprecedented, and our understanding of biological systems enables fine-grained manipulation of cellular behavior down to the molecular level. This project will leverage the PIs’ unique combination of expertise at the crossroads of biology, bio-sensor interface design, and wireless communications, to address the challenges for human health applications such as prediction and prevention of infections.
CLIPEUS (Shield in Latin) aims at creating a communications network among man-made implantable devices and the natural cells inside the body under the novel communication paradigm, called Internet of Bio-NanoThings, where sensing, actuation, and computing processes are tightly coupled to pervasively, perpetually, and precisely sense, process, control, and exchange health information of the body in real time to defend it against infections.
CLIPEUS focuses on the detection of infections inside the body based on the detection of the molecular communication among the infectious bacteria by man-made bio-nanothings, called GLADIO (Sword in Latin), consisting of bionanosensors for detection, low power electronics for processing and antenna for near field communication to transfer the data through the tissues to outside of the body. Due to the power limitations of GLADIO, the inherent noise of biological processes, and the non-deterministic response of patients to infections, the collected data will be sparse and noisy. Novel machine learning techniques will be developed to interpret this data for future personalized medicine applications. The system will be extensively tested and ameliorated by phantom experiments as a first step before in vivo experiments.
The project will recruit and train 2 PhD students and 2 Postdocs and has drawn up a comprehensive, multifaceted plan to disseminate, communicate and exploit the project results.