Werner Haselmayr

The internet of bio-nano things (IoBNT) extends the concept of the internet of things (IoT) into biological domains. It enables advanced applications, such as real-time health monitoring and targeted drug delivery. Two pivotal challenges are communications as well as distributed computing. However, due to the different conditions in the IoBNT, completely different solutions are required. In this paper, we propose a novel relaying-addition/subtraction algorithm for molecular-diffusive systems, which enables additions/subtractions in a distributed manner. It combines molecular computing with relaying. In particular, two transceivers transmit two-bit sequences to be added/subtracted using different molecule types. In between the two transceivers, multiple relay nodes are positioned, forwarding the transmitted bit sequences based on diffusive wave relaying. When two waves representing two bits collide at a certain relay, the addition/subtraction of the two bits is performed and the resulting bit is emitted using different types of molecules. This scheme can be considered as a distributed alternative to existing molecular computing paradigms, which further allows simultaneous computation and transmission of information. Finally, we confirm the proposed approach with computer simulations.

Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the foundation for innovative healthcare applications that envision a network of remotely coordinated nanodevices within the human body to monitor and actuate over potential diseases. However, interconnecting such nanodevices requires communication strategies that can cope with molecular communication (MC) channels, whose complex, stochastic, and dynamic behavior often makes accurate physical modeling infeasible. To explore the limits of nanodevice interconnectivity under these conditions, this survey focuses on data-driven communication strategies for MC systems, with particular emphasis on machine learning (ML) methods and neural network (NN) architectures for a robust and adaptive communication scheme at the nanoscale. Research on NN-enabled MC spans several aspects covered in this survey, including NNs for communication in IoBNT networks, the feasibility of biocompatible NN realization, explainable approaches, and the generation of training datasets. We also include open-source code examples to support reproducible research across key MC scenarios. Finally, we identify emerging challenges, including the need for robust NN architectures, biologically integrated NN modules, and scalable training strategies.

Chemotaxis describes a number of mechanisms by which living organismsnavigate a concentration field, allowing them for exampleto migrate towards a molecule source. In amoeboid chemotaxis, theexternal concentration signal must first be converted to an intracellularone, which modulates cell crawling. Typically, the leadingedge of the cell forms protrusions and adheres to their adjacentsurfaces, while the trailing edge contracts and detaches from surfaces.Therefore, first a polarity with respect to the extracellularsignal has to be established to define what leading and trailing edgeactually is. In this paper we develop a chemical reaction networkthat is capable of generating the respective polarity signal over thewide range of concentration levels typically observed in biologicalsignaling. Based on the system’s equilibrium, we derive designguidelines for the network and verify them by dynamic simulationsof the overall system.

This paper proposes a distributed scheduling algorithm for time-division multiple access in diffusion-based molecular communication systems. We consider multiple transmitters sending messages to a single receiver. The proposed scheduling algorithm is executed by each transmitter and prevents interuser interference in its final operating state. Moreover, it requires no synchronization among the transmitters and allows non-ideal time measurement and varying package lengths. The efficiency of the proposed algorithm is compared with an ideal and centralized scheduling algorithm as an upper benchmark and is validated with a particle-based simulation.

In this paper, we propose a novel distributed scheduling algorithm for time-division multiple access (TDMA) in diffusion-based molecular communication systems. We consider a receiver nano device (ND) surrounded by randomly distributed transmitter NDs. The goal of the proposed scheduling algorithm is to arrange transmissions of the different transmitter NDs in order to mitigate inter-user interferences (IUI). Each ND follows the scheduling algorithm, which only requires listening to the channel and measuring time, but no synchronization. We provide a theoretical foundation and verify the functionality of the algorithm with a particle-based simulation (PBS). Furthermore, we compare the performance of the algorithm in terms of temporal channel utilization with an ideal (centralized) scheduling algorithm, which shows similar results for large packet lengths.

Implementations of matrix multiplication via diffusion and reactions, thus eliminating the need for electronics, have been proposed as a stepping stone to realize molecular nano-neural networks (M3N). This work examines whether such "matrix multiplication units" can function spontaneously, i.e., without continuous external energy input. We employ the theory of local non-equilibrium thermodynamics in the linear regime, modeling the system through coupled reaction-diffusion equations and deriving the resulting entropy production. Numerical simulations on a 2D computational mesh confirm that correct matrix multiplication and strictly increasing entropy can be attained under two key conditions: negligible cross-diffusion among distinct species and sufficiently sharp membranes to prevent back diffusion. When these constraints are met, the system concentrations naturally converge to the desired results, suggesting that autonomous chemical computing can be realized if the design parameters align with thermodynamic requirements.

Implementations of matrix multiplication via diffusion and reactions, thus eliminating the need for electronics, have been proposed as a stepping stone to realize Molecular Nano-Neural Networks (M3N). This work examines whether such “matrix multiplication units” can function spontaneously, i.e., without continuous external en- ergy input. We employ the theory of Local Non-Equilibrium Thermodynamics in the linear regime, modeling the system through coupled reaction–diffusion equations and deriving the resulting entropy production. Numerical simulations on a 2D computa- tional mesh confirm that correct matrix multiplication and strictly increasing entropy can be attained under two key conditions: negligible cross-diffusion among distinct species and sufficiently sharp membranes to prevent back-diffusion. When these con- straints are met, the system concentrations naturally converge to the desired results, suggesting that autonomous chemical computing can be realized if the design parameters align with thermodynamic requirements.