Molecular Communication Model Research Articles

Optimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation

The analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p'' and ``N'' as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.

COTiR: Molecular Communication Model for Synthetic Exosome-Based Tissue Regeneration.

Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.

Impact of Anomalous Diffusion Phenomenon on Molecular Information Delivery in Bounded Environment.

Through this paper, a three-dimensional molecular communication (MC) inside a cuboid container is considered. Instead of normal diffusion phenomenon, the anomalous diffusion phenomenon is incorporated which enhances the practicability of the model. The Fick's law is re-defined for the considering rectangular coordinate system in which information carrying molecules (ICMs) diffuse anomalously in the environment. The impact of flow of the fluid along the +x direction in the environment is also considered. Moreover, considering free propagator phenomenon, the expressions of spatio-temporal probability density function (PDF) of the ICMs is derived for the considered model. Further, the novel closed-form expressions for first arrival time density (FATD) of the ICM, survival probability (SP) at any time, and its corresponding first arrival probability (FAP) are also derived. Furthermore, the considered MC model is also analyzed in terms of minimum bit-error-rate (BER) using log-likelihood ratio test (LLRT) optimal detector. The derived expressions are verified using MATLAB based particle-based and Monte-Carlo simulations.

The End-to-End Molecular Communication Model of Extracellular Vesicle-based Drug Delivery.

A closer look at nature has recently brought more interest in exploring and utilizing intra-body communication networks composed of cells as intrinsic, perfectly biocompatible infrastructures to deliver therapeutics. Naturally occurring cell-to-cell communication systems are being manipulated to release, navigate, and take-up soluble cell-derived messengers that are either therapeutic by nature or carry therapeutic molecular cargo. One example of such structures is extracellular vesicles (EVs) which have been recently proven to have pharmacokinetic properties, opening new avenues for developing the next generation biotherapeutics. In this paper, we study theoretical aspects of the EV transfer within heart tissue as a case study by utilizing an information and communication technology-like approach in analyzing molecular communication systems. Our modeling implies the abstraction of the EV releasing cells as transmitters, the extracellular matrix as the channel, and the EV receiving cells as receivers. Our results, derived from the developed analytical models, indicate that the release can be modulated using external forces such as electrical signals, and the transfer and reception can be affected by the extracellular matrix and plasma membrane properties, respectively. The presented modeling provides initial results for the EV biodistributions and contribute to avoiding unplanned administration, often resulting in side- and adverse effects.

Analyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Models

Alzheimer’s disease will affect more people with increases in the elderly population, as the elderly population of countries everywhere generally rises significantly. However, other factors such as regional climates, environmental conditions and even eating and drinking habits may trigger Alzheimer’s disease or affect the life quality of individuals already suffering from this disease. Today, the subject of biomedical engineering is being studied intensively by many researchers considering that it has the potential to produce solutions to various diseases such as Alzheimer’s caused by problems in molecule or cell communication. In this study, firstly, a molecular communication model with the potential to be used in the treatment and/or diagnosis of Alzheimer’s disease was proposed, and its results were analyzed with an artificial neural network model. Secondly, the ratio of people suffering from Alzheimer’s disease to the total population, along with data of educational status, income inequality, poverty threshold, and the number of the poor in Turkey were subjected to detailed distribution analysis by using the random forest model statistically. As a result of the study, it was determined that a higher income level was causally associated with a lower risk of Alzheimer’s disease.

How mobility of transmitter and receiver affects the communication quality

Nano-networks focused on communication of nano-sized devices (nanomachines) are a new communication concept, which is known as Molecular Communication (MC) in the literature. In this study, on the contrary to the literature, a mobile MC model is proposed in a diffusion environment by using 5 bits because it is known that besides the molecules, which transport information between the transmitter and receiver, the transmitter and receiver parts of the biological cells are mobile in the blood or any other fluid media. In this study, both the transmitter and the receiver can be chosen as mobile and/or fixed for some specific duties, such as drug delivery systems. Their mobility values can also be regulated separately for the proposed mobile MC model. The proposed model is analyzed for the different situations of the transmitter and receiver (fixed and/or mobile) by considering the fraction of the received molecules. Finally, the number of bits, the time step, and the bit duration are analyzed to find the best MC model. It is concluded that when the receiver and the transmitter are mobile, the distance between them changes, and finally, this affects the probability of the received molecules at the receiver.

A Molecular Communication Detection Method for the Deformability of Erythrocyte Membrane in Blood Vessels.

Molecular communication (MC) inspired drug delivery holds considerable promise as a new design for targeted therapy with high efficiency and minimal toxicity. The process of drug delivery can be modelled in a blood flow-based MC system, where nanoparticles (NPs) carry therapeutic agents through the blood vessel channels to the targeted diseased tissue. Most previous studies in the flow-based MC consider a Newtonian fluid with a laminar flow, which ignores the influence of red blood cells (RBCs). However, the nature of blood flow is a complex and non-Newtonian fluid composed of proteins, platelets, plasma and deformable cells, especially RBCs. The ability to change their shapes is essential to the proper functioning of RBCs in the microvasculature. Different shapes of RBCs have a great impact on the performance of blood flow. Changes in the properties and shapes of RBCs are often associated with different diseases, such as sickle cell anemia, diabetes, and malaria. Thus, it is highly important to establish a more realistic blood flow MC model considering the deformable cells. According to our previous study, the motion and adhesion of individual NPs are modelled through the Brownian adhesion dynamics. Subsequently, this paper establishes a particle-cell hybrid model in the flow-based MC, which focuses on the RBC deformation, aggregation, and dispersion in the blood suspension. Based on the state of the RBC deformation and aggregation in the vessels with different flow rates, this paper proposes a novel methodology for detecting the deformability of the cells. The blood state in terms of RBC deformability is determined by the difference in NPs' concentration at the receiving end., this paper sheds some light on the influence of RBCs on the motion of NPs, which provides new insights on the design of targeted drug delivery and the detection of vascular diseases.

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

In quorum sensing (QS), bacteria exchange molecular signals to work together. An analytically-tractable model is presented for characterizing QS signal propagation within a population of bacteria and the number of responsive cooperative bacteria (i.e., cooperators) in a two-dimensional (2D) environment. Unlike prior works with a deterministic topology and a simplified molecular propagation channel, this work considers continuous emission, diffusion, degradation, and reception among randomly-distributed bacteria. Using stochastic geometry, the 2D channel response and the corresponding probability of cooperation at a bacterium are derived. Based on this probability, new expressions are derived for the moment generating function and different orders of moments of the number of cooperators. The analytical results agree with the simulation results obtained by a particle-based method. In addition, the Poisson and Gaussian distributions are compared to approximate the distribution of the number of cooperators and the Poisson distribution provides the best overall approximation. The derived channel response can be generally applied to any molecular communication model where single or multiple transmitters continuously release molecules into a 2D environment. The derived statistics of the number of cooperators can be used to predict and control the QS process, e.g., predicting and decreasing the likelihood of biofilm formation.

Performance Analysis of D-MoSK Modulation in Mobile Diffusive-Drift Molecular Communications

Molecular communication (MC), in which molecules serve as the carrier for data transmission, plays an essential role in nanonetworks. In this article, a mobile diffusive-drift MC model is investigated, which consists of a mobile transmit nanomachine (TN) and a mobile receive nanomachine (RN). The depleted molecule shift keying (D-MoSK) modulation is utilized in this model to perform end-to-end communication. To explore the performance of D-MoSK, we derive the closed-form expressions of symbol error rate (SER) as well as the channel capacity, and then we give out the numerical results. It is observed from the numerical results that, if compared with the molecule shift keying modulation, the D-MoSK modulation can exhibit better performances in terms of SER, channel capacity, and complexity under the employed model. Also, the impacts of several crucial parameters on the performance are evaluated and discussed comprehensively. The obtained results are expected to provide guidance significance for the design of a practical mobile diffusive-drift MC system.

Model-Based: End-to-End Molecular Communication System Through Deep Reinforcement Learning Auto Encoder

Molecular communication (MC) system is an emerging technology for nanoscale networks. Therefore, there is a requirement to develop a new end-to-end MC model, which may deliver new perceptions into the aspect of these nanoscale networks. This paper aims to implement the MC framework as an end-to-end deep reinforcement learning (DRL) auto encoder (AE). The technique enables training of the MC system without any information about the actual channel (medium) model. For training the receiver and transmitter, the proposed techniques are supervised learning and DRL, respectively. The results show that the performance of the DRL autoencoder (AE) based system achieves nearly the same performance as the traditional modulation and demodulation methods in term of bit-error-rate (BER) under the Gaussian noise channel but with less complexity. The proposed technique can also be joint with the other coding methods to improve their performance.

An Information Theoretical Analysis of Human Insulin-Glucose System Toward the Internet of Bio-Nano Things.

Molecular communication is an important tool to understand biological communications with many promising applications in Internet of Bio-Nano Things (IoBNT). The insulin-glucose system is of key significance among the major intra-body nanonetworks, since it fulfills metabolic requirements of the body. The study of biological networks from information and communication theoretical (ICT) perspective is necessary for their introduction in the IoBNT framework. Therefore, the objective of this paper is to provide and analyze for the first time in the literature, a simple molecular communication model of the human insulin-glucose system from ICT perspective. The data rate, channel capacity, and the group propagation delay are analyzed for a two-cell network between a pancreatic beta cell and a muscle cell that are connected through a capillary. The results point out a correlation between an increase in insulin resistance and a decrease in the data rate and channel capacity, an increase in the insulin transmission rate, and an increase in the propagation delay. We also propose applications for the introduction of the system in the IoBNT framework. Multi-cell insulin glucose system models may be based on this simple model to help in the investigation, diagnosis, and treatment of insulin resistance by means of novel IoBNT applications.

A vertical channel model of molecular communication and its test-bed

The study of molecular communication is more and more prevalence, and channel model of molecular communication plays an important role in the molecular communication system. Different propagation environment and modulation techniques lead to different channel model, and most of the previous researches are mainly concentrate on the channel model in horizontal direction. However, in nature the communications between nano-machines are in short range and some of the information propagation are in the vertical direction, such as transpiration of plants, biological pump in ocean, etc. Therefore, in this paper a vertical channel model was proposed in which nano-machines communicate with each other mainly through diffusion at the vertical direction. Firstly, proposing a vertical molecular communication model, we mainly focus on the gravity, though the channel model is also affected by other main factors, such as the flow of the medium, the distance between the transmitter and the receiver, the delay or sensitivity of the transmitter and the receiver, etc. Secondly, we set up a test-bed for the vertical channel model, and verify the differences between the theoretical result and the experimental result. Finally, we get the parameters of the channel model which was proposed for the test-bed by utilizing the experimental data and the non-linear least squares method.

Reliability and delay analysis of multicast in binary molecular communication

Molecular communication is a new and promising communication technology. However, we consider that molecular communication easily suffers from high unreliability and long delay due to the stochastic behavior of the molecules in a biological environment, thus it is essential to investigate the characteristics of reliability and delay in molecular communication. In this paper, we analyze the reliability and delay in multicast topology based on binary molecular communication model. First, we give the mathematical expressions of reliability and delay in different scenarios including a single link, a single path and multicast topology, respectively. In particular, we use retransmission mechanism for transmission failure. Then the numerical results derived from the simulation performances show how the parameters including diffusion coefficient, the distance between transmitter nanomachine and receiver nanomachine, the number of molecules emitted in each time slot, the number of slots and each time slot duration have impacts on reliability and delay. More importantly, under the same reliability, lower delay can be theoretically realized by binary molecular communication model compared to the concentration model of virus-based nanonetworks.

Molecular Communication Model for Targeted Drug Delivery in Multiple Disease Sites With Diversely Expressed Enzymes.

Targeted drug delivery (TDD) for disease therapy using liposomes as nanocarriers has received extensive attention in the literature. The liposome's ability to incorporate capabilities such as long circulation, stimuli responsiveness, and targeting characteristics, makes it a versatile nanocarrier. Timely drug release at the targeted site requires that trigger stimuli such as pH, light, and enzymes be uniquely overexpressed at the targeted site. However, in some cases, the targeted sites may not express trigger stimuli significantly, hence, achieving effective TDD at those sites is challenging. In this paper, we present a molecular communication-based TDD model for the delivery of therapeutic drugs to multiple sites that may or may not express trigger stimuli. The nanotransmitter and nanoreceiver models for the molecular communication system are presented. Here, the nanotransmitter and nanoreceiver are injected into the targeted body system's blood network. The compartmental pharmacokinetics model is employed to model the transportation of these therapeutic nanocarriers to the targeted sites where they are meant to anchor before the delivery process commences. We also provide analytical expressions for the delivered drug concentration. The effectiveness of the proposed model is investigated for drug delivery on tissue surfaces. Results show that the effectiveness of the proposed molecular communication-based TDD depends on parameters such as the total transmitter volume capacity, the receiver radius, the diffusion characteristic of the microenvironment of the targeted sites, and the concentration of the enzymes associated with the nanotransmitter and the nanoreceiver designs.

Molecular Communication Modeling of Antibody-Mediated Drug Delivery Systems.

Antibody-mediated Drug Delivery Systems (ADDS) are emerging as one of the most encouraging therapeutic solutions for treating several diseases such as human cancers. ADDS use small molecules (antibodies) that propagate in the body and bind selectively to their corresponding receptors (antigens) expressed at the surface of the diseased cells. In this paper, the Molecular Communication (MC) paradigm, where information is conveyed through the concentration of molecules, is advocated for the engineering of ADDS and modeling their complex behavior, to provide a realistic model without the over-complication of system biology models, and the limitations of experimental approaches. The peculiarities of antibodies, including their anisotropic transport and complex electrochemical structure, are taken into account to develop an analytical model of the ADDS transport and antigen-binding kinetics. The end-to-end response of ADDS, from the drug injection to the drug absorption, is mathematically derived based on the geometry of the antibody molecule, the electrochemical structure of the antibody-antigen complex, and the physiology of the patient. The accuracy of the MC model is validated by finite-element (COMSOL) simulations. The implications of the complex interplay between the transport and kinetics parameters on the performance of ADDS are effectively captured by the proposed MC model. The MC model of ADDS will enable the discovery and optimization of drugs in a versatile, cost-efficient, and reliable manner.

Antibody-based molecular communication for targeted drug delivery systems.

Antibody-based drug delivery systems (ADDS) are established as the most promising therapeutic methods for the treatment of human cancers and other diseases. ADDS are composed of small molecules (antibodies) that selectively bind to receptors (antigens) expressed by the diseased cells. In this paper, the Molecular Communication (MC) paradigm, where the delivery of molecules is abstracted as the delivery of information, is extended to be applied to the design and engineering of ADDS. The authors have previously developed a straightforward framework for the modeling of Particulate Drug Delivery Systems (PDDS) using nano-sized molecules. Here, the specificities of antibody molecules are taken into account to provide an analytical model of ADDS transport. The inputs of the MC model of PDDS are the geometric properties of the antibodies and the topology of the blood vessels where they are propagated. Numerical results show that the analytical MC model is in good agreement with finite-element simulations, and that the anisotropy is an important factor influencing ADDS.

HLA based architecture for molecular communication simulation

In parallel to the developments in nano-scale machines and engineered cells, communication among them is getting more attention. The use of molecules to transfer information from one nanomachine to another is one of the mechanisms to build networks at the nano-scale. The research activities on communication models and performance evaluation of molecular communication heavily depend on simulations to verify the proposed models. The existing simulation tools for computer networking can not be directly used for molecular communication due to different communication model and channel characteristics of the fluid environment and the carrier wave properties. Simulation of molecular communication requires modeling the new communication paradigm that comprises different options for encoding, transmission, propagation, reception, and decoding. It should consider possible architectural design options and performance evaluation of molecular communication networks. Since molecular communication involves the modeling of large number of nano-scale objects, the scalability of the simulation tool is another important concern. In this paper, we introduce a simulator design that aims at fulfilling important design issues of the molecular communication model, focusing on scalability. High Level Architecture (HLA), which is standardized under IEEE 1516, is used to design and develop a distributed simulation tool for molecular communication. The results show that different scalability options can be used to benefit from additional processing power to shorten the execution time. This also enables modeling large systems, which may not be possible otherwise.

Rate-Delay Tradeoff With Network Coding in Molecular Nanonetworks

Molecular communication is a novel nanoscale communication paradigm, in which information is encoded in messenger molecules for transmission and reception. However, molecular communication is unreliable and has highly varying long propagation delays mainly due to the stochastic behavior of the freely diffusing molecules. Thus, it is essential to analyze its delay characteristics, as well as the tradeoff between the rate and delay, in order to reveal the capabilities and limitations of molecular information transmission in nanonetworks. In this paper, first, a new messenger-based molecular communication model, which includes a nanotransmitter sending information to a nanoreceiver, is introduced. The information is encoded on a polyethylene molecule, CH3 (CHX)n CH2F, where X stands for H and F atoms representing 0 and 1 bits, respectively. The emission of the molecules is modeled by puffing process which is inspired by the alarm pheromone release by animals in dangerous situations. In this work, the rate-delay characteristics of this messenger-based molecular communication model are explored. Then, a Nano-Relay is inserted in the model, which XOR's the incoming messages from two different nanomachines. Performance evaluation shows that indeed, a simple network coding mechanism significantly improves the rate given delay of the system, and vice versa.