Artificial Intelligence (AI) tools such as ChatGPT have transformed our daily life. Usually, these AI tools are based on Neural Networks (NNs) implemented in digital hardware, i.e., not biological or physical NNs like those found in human brains. Instead, NNs are typically implemented as software on top of a Graphics Processing Unit (GPU) or specialized AI hardware. In contrast, this work-in-progress utilizes a biological NN, demonstrating the use of Synthetic Biological Intelligence (SBI) by implementing a toy example - controlling an inverted pendulum. Biological NNs can be beneficial compared to GPUs or specialized AI hardware, especially in terms of energy efficiency and capability on noisy or dynamic tasks. Following the encoding, decoding, reward, and punishment nature of the biological NN, this work presents initial results toward controlling the physical system in future research.
Despite advances in medical research and therapy, cancer remains a major global health issue due to its complexity in treatment. Depending on the type and extent of the disease, tumor removal with cancer treatment drugs without harming surrounding healthy tissue is a critical challenge. The targeted drug delivery (TDD) approach has emerged to increase the effectiveness of treatment while reducing side effects. This paper envisions a hybrid navigation mechanism that utilizes chemotaxis and entropy to direct nanoscale medical agents (NMAs) toward cancerous cells within a tumor microenvironment (TME) to release medication and treat them. The NMAs sense the concentration gradient of the biomarkers in their vicinity to localize the tumor(s). For this study, we used hypoxia as our biomarker, given the higher oxygen consumption by cancerous cells. The numerical results show that the effectiveness and reliability of the hybrid navigation strategy are significantly higher than those of the random and chemotaxis navigation mechanisms. Furthermore, analysis of cancerous cell statistics over time demonstrates that the proposed method eliminates tumors faster than the other two strategies.
Molecular communication (MC) enables information exchange via chemical signals, offering distinct advantages in environments where electromagnetic communication is ineffective. In this work, we present the first experimental demonstration of an MC system with a function of integrated sensing and communication (ISAC). Specifically, alcohol molecules are modulated using on-off keying and detected by a chemical sensor that enables both binary decoding and real-time odor source localization. A mobile robotic platform interprets molecular concentration variations to navigate and recover messages. The results validate the feasibility of this dual function system, paving the way for future MC with embedded sensing capabilities.
In environments where airborne molecular communication (AMC) is used, the choice of surface materials can significantly affect system performance by altering the concentration and persistence of molecular signals. To gain first insights into these material-dependent effects, we performed experiments in a small closed chamber with a high surface-to-volume ratio and compared the observed AMC signals when varying the inner surface between borosilicate glass and polyethylene (PE)-based plastic. In contrast to plastic, the glass environment produced a signal with a more distinct pattern and shorter duration, showing the impact of material in the environment on molecular retention. This behavior can be attributed to differences in surface interactions, in particular the adsorption (and potentially partial absorption) and subsequent desorption of molecules on plastic surfaces. Our results underline the need to explicitly consider the properties of surface materials in the development and modeling of AMC systems.
The Cellular Potts Model (CPM) is a lattice-based computational framework for simulating tissue dynamics such as cell sorting, collective cell migration, and morphogenesis. While the model effectively captures essential biological properties, its high computational cost poses a significant challenge when simulating large-scale multicellular systems over extended simulation times. In our previous work, we proposed a boundary-based algorithm that reduces computation time by limiting copy attempts to cell boundaries and performing local energy calculations. In this paper, we further accelerate CPM simulations with a CPU-GPU hybrid boundary-based algorithm: local energy calculations are offloaded to the GPU to leverage its parallel processing capabilities, while the CPU continues to manage cell boundaries and connectivity checks. Benchmark results demonstrate a substantial reduction in computation time compared to the CPU-based boundary-based algorithm, enabling more efficient and scalable simulations of complex multicellular systems.
Concentration-shift keying (CSK) is the classical modulation for diffusion-based molecular communication, but implementations remain almost binary: on-off keying (OOK, two symbols) is standard and only a few reports reach more symbols. Higher-order CSK would raise efficiency, yet two hurdles persiststrong inter-symbol interference (ISI) and the limited molecule payload of a nanomachine. We present a feasible linear high-order concentration modulation (LHOCM) with Gaussian-Intersection threshold and limited molecule payload that overcomes both obstacles in a purely statistical manner. First, closed-form expressions for ISI mean and variance in a 3-D diffusion channel are derived. Using these, we upper-bound the BER with Gaussian Intersection thresholds that statistically compensates ISI, and obtain a simple sizing rule that links channel quality, molecule payload and constellation order. Numerical results show that, under a realistic budget of molecules per symbol, LHOCM attains a obvious gain over OOK using the same payload. The work thus supplies (i) the first reproducible high-order CSK baseline, and (ii) a one-line design tool for power-constellation sizing in nanonetworks. The proposed LHOCM is algorithm-agnostic and can be directly embedded into existing CSK-related modulation architectures.
Molecular communication (MC) transmits information through the release, diffusion, and reception of molecules, holding great potential in the field of drug delivery. In an MC system, the prediction of the distance between the transmitter and the receiver is crucial for the receiver's resource consumption. Traditional distance detection strategies mainly focus on known channel state information (CSI). To address this limitation, this paper proposes a method for estimating the distance between the transmitters and the receiver in MC system with unknown CSI using a deep neural network (DNN) model. We employ Monte Carlo simulation to capture the positions of molecules in a three-dimensional environment. The dataset is generated based on the molecular coordinates at each position. Numerical results indicate that the DNN model can accurately estimate the distance between the transmitters and the receiver, demonstrating good detection capabilities and generalization ability. Additionally, the minimum distance between the transmitters and the receiver's boundary also affects the accuracy of the distance estimation.
Galvanic Coupling (GC) Intra-Body Communication offers a promising solution for reliable, low-power data transmission in wearable medical devices. However, signal performance is highly sensitive to electrode material and skin-electrode interface properties. This study uses finite element modeling (FEM) to quantify the effects of electrode design, including material choice, conductive gel, and foam layers, on the GC channel frequency response (CFR), focusing on attenuation, phase delay, and group delay. Results show that incorporating gel and foam significantly enhances signal transmission by reducing attenuation, minimizing phase distortion, and stabilizing group delay across a broad frequency range. These improvements help mitigate performance disparities between Ag/AgCl and copper (Cu) electrodes, supporting the development of high-performance, energy-efficient intra-body networks for wearable healthcare applications.
Molecular communication's diffusion characteristics limit data transmission rates. While Multiple-Input Multiple-Output (MIMO) technology increases rates, it also intensifies inter-symbol interference (ISI) and inter-link interference (ILI), complicating channel modeling. Traditional neural network detectors are computationally intensive, failing to meet low complexity and energy requirements. This paper proposes a Mamba neural network-based detector, which reduces complexity and improves performance, making it more suitable for molecular communication systems compared to Transformer.
This paper investigates the localization of an unintended fully absorbing receiver in an unbounded three-dimensional molecular communication via diffusion (MCvD) system. The system consists of a point source transmitter, a known fully absorbing target receiver, and an unintended fully absorbing receiver. Unlike conventional single-receiver scenarios, the presence of two fully absorbing receivers induces molecular absorption competition, altering the expected received signal distribution and increasing the complexity of localization. To address this challenge, we employ maximum likelihood estimation (MLE) and the Newton-Raphson iterative method to estimate the distances between the transmitter and the unintended receiver, as well as between the target receiver and the unintended receiver. The likelihood function is formulated based on the number of molecules absorbed by the target receiver under the influence of the unintended receiver, and the receiver position is estimated iteratively through optimization. Simulation results validate the effectiveness of the proposed method, demonstrating its capability to accurately localize the unintended fully absorbing receiver despite interference from two fully absorbing receivers.
Personalized medicine increasingly relies on advanced simulations to support treatment planning. MEHLISSA is a simulation tool for in-body communication and disease modeling in the human circulatory system. Its previous implementation, based on the ns-3 framework, was computationally intensive and not suitable for large-scale biological simulations. In this work, we present MEHLISSA 2.0, a redesigned version with a streamlined simulation core. We demonstrate its applicability by simulating a typical large-scale molecular communication environment: CAR-T cell leukemia therapy based on established biological models. Benchmarking shows substantial performance improvements, particularly in long simulations with over 2x runtime reduction, which marks a significant step toward simulating realistic treatment scenarios.
This paper introduces an experimental platform for airborne molecular communication (AMC) and investigates the feasibility of using metal oxide semiconductor (MOS) sensors as receivers. The platform enables chemical communication with volatile organic compounds (VOCs), specifically ethanol and acetone, under repeated dosing patterns with controlled airflow. Preliminary experiments show that ethanol and acetone yield different sensor responses, indicating the potential of MOS sensors to facilitate the decoding of molecular signals. Distinguishable temporal response patterns are also shown, highlighting the possibilities and challenges of selective chemical detection in AMC. Future work will extend the study to molecular mixture signals.
Molecular communication is a core pillar of the Internet of Bio-Nano Things. Exhaled breath, rich in water vapor, offers a viable medium for air-based molecular communication. This paper presents a low-cost, non-invasive approach using a DHT22 sensor to classify breath patterns, namely Eupnea, Bradypnea, and Tachypnea. Humidity and temperature signals from the mouth and nose are processed using machine learning (ML). The model achieves strong classification performance, showing that ML can effectively distinguish breath patterns despite sensor constraints.
Superparamagnetic iron oxide nanoparticles (SPIONs) often form chain-like agglomerates due to their magnetic dipole interactions. Various simulation methods have been employed to elucidate the underlying physical mechanisms; however, experimental validation using realistic ferrofluid parameters remains limited. In this study, we perform molecular dynamics simulations based on the Lennard-Jones potential to investigate the agglomerate size distribution using experimentally derived physical parameters.
Molecular matrix computing devices play a major role in translating concepts from classical engineering into the bio-nano-domain and form the basis for artificial biological Neural Networks (NNs). Different concepts for the realization of such devices have been proposed in the literature. There exists a trade-off between computing speed and complexity of the realized matrix/NN, which dictates the appropriate choice for different application scenarios. In this work, we focus on computing speed for comparatively simple computations, i.e., matrix multiplications. We show that a previously proposed reaction-diffusion based computing structure, called M3N, will likely be faster than originally anticipated, given that the involved chemicals are chosen appropriately. The reason is that in previous theoretical studies, an idealized reaction model was utilized. Incorporating a thermodynamically more accurate model, we find that the computational speed in a real system can be higher due to previously neglected non-idealities. This finding is explained theoretically and supported by computer simulations.
Neuron-microglia communication is essential for brain function but remains poorly quantified. We present a combined experimental and computational framework using the NAOM TetraCulture system and a molecular communication model with information-theoretic analysis. Our model captures ATP and cytokine signaling dynamics and quantifies information flow between neurons and microglia under normal and inflammatory conditions. This approach advances understanding of neuroimmune interactions and aids the identification of therapeutic strategies.
For testbed experiments in molecular communication, the injection method can significantly impact the results of the received signals. In this work, the impact of the injection flow rate on the uniform distribution of particles was studied by numerical simulation of a syringe injection in the direction of background flow for three different flow rates in 3D. A low injection rate seems to negatively impact the overall propagation of the particle bolus.
Cancer remains a leading global health challenge, with early detection being crucial to improving patient outcomes. The tumor microenvironment (TME), characterized by abnormal oxygen and pH distributions, plays a vital role in tumor progression and treatment response. Recent advances in nanotechnology have enabled the development of micro/nanorobots capable of real-time sensing and targeted delivery within the TME. However, the effectiveness of such systems depends on the accurate modeling of biological gradient fields (BGFs). In this study, we propose a simulation framework grounded in Computational Nanobiosensing (CONA) to integrate vascular morphology with oxygen gradient field analysis for a more comprehensive understanding of the TME. Using COMSOL Multiphysics, we constructed a two-dimensional model that couples laminar flow and mass transport to investigate oxygen distribution under conditions in the presence and absence of tumors. The simulation results reveal that tumors significantly alter local oxygen profiles, leading to steep concentration gradients and hypoxic regions. These findings provide both visual and quantitative insight into the TME and establish a theoretical foundation for designing oxygen gradient-guided nanoparticle delivery strategies. This work contributes to the advancement of intelligent navigation and precision therapy in complex tumor environments.
Molecular communication (MC) represents a novel paradigm that utilizes biochemical signals for information transfer, presenting a viable alternative for scenarios where conventional electromagnetic communication is not practical. Despite significant advancements in theoretical models, experimental realizations remain limited due to the fundamental complexity of biological systems. Although synthetic biology provides a tool to realize in vivo MC systems, its integration into the communication field is impeded by complex biological factors and the absence of readily available design methodologies. In this work, we present a structured workflow for developing in vivo MC systems using synthetic biology. Our approach guides researchers through genetic circuit design, enzyme-based DNA assembly, and validation techniques using quantifiable gene expression outputs. We demonstrate the feasibility of this workflow through a case study, where engineered Escherichia Coli ( E-coli) bacteria detect acyl-homoserine lactones (AHL) signals and produce measurable fluorescence in response to varying concentrations. Our results confirm the reliability and responsiveness of the engineered receiver, validating our proposed design strategy. Importantly, this work facilitates broader adoption of in vivo MC systems and bridges the gap between theoretical modeling and practical implementation in biological environments.
Traditional single-input-single-output (SISO) models fall short in multi-receiver molecular communication due to mutual interference and complex spatial distribution. This study proposes a single-input-multiple-output (SIMO) 3D unbounded diffusion model with a point transmitter and two spherical receivers, incorporating finite surface receptors and reversible ligand-receptor reactions. A probabilistic equation for binding and the expected received signal (ERS) are derived. To handle spatial interference, a probabilistic equivalent modeling (PEM) approach and a multiple receiver iteration (MRI) algorithm are proposed. The MRI algorithm decomposes interference into direct and indirect components to iteratively compute each receiver's ERS. Simulations confirm the model's accuracy, and further analysis shows that optimizing receiver layout effectively reduces interference and improves system performance.
