Molecular Communication Networks Research Articles

Modeling intercellular calcium dynamics in an epithelial organ using Dynamic Mode Decomposition

Calcium ions (Ca2+) are versatile, yet critical components of molecular communication networks. Cells have developed complex mechanisms to maintain homeostasis of Ca2+ concentration levels within cytoplasm. Both at the cellular and tissue levels, information is transferred through complex dynamical patterns of Ca2+ signals. Since Ca2+ is involved in the regulation of various biological processes, understanding its dynamics is a valuable source of information with potential applications in systems and synthetic biology. However, modeling such complicated dynamics is a challenge in systems biology. In this work, we present a data driven analysis of Ca2+ dynamics based on Dynamic Mode Decomposition (DMD) to study the dynamics of Ca2+ signaling in Drosophila melanogaster wing disc in late larval stages. This system is amenable to studying Ca2+ signaling in a developing epithelium. Experimentally, it is observed that Ca2+ dynamics show different dynamical patterns depending on genetic perturbations. Therefore, from a systems biology perspective, devising a reasonable model to capture various dynamical patterns can shed light into interplay between dynamics and various biological processes. The dynamics of Ca2+ is analyzed using a first-order state space model approximated by DMD algorithm. DMD modes and eigenvalues of the derived model was investigated to identify coherent spatial and temporal patterns that reflect the known morphogenetic boundaries of the organ. This implies that Ca2+ signaling dynamics encode the differentiation state of cells in the system.

Optimal Positioning of Relay Node in Cooperative Molecular Communication Networks

In this paper, we consider a cooperative diffusion-based molecular communication (MC) network consisting of single source, single decode-and-forward relay, and single destination. The source and the relay nodes use different types of molecules for information transmission. Considering both the relay and the direct links at the destination node, we have two different received signals that can be used by the destination node to retrieve the data transmitted by the source node. Therefore, diversity arises at the destination node. For detection, we adopt the energy detector applying the diversity combining technique. Since the optimum relay position in relay-aided networks plays a crucial role to enhance their performance, we study the relay location optimization problem. We first derive a closed-form expression for the bit error probability of the proposed detection scheme in which the received energy from the source and the relay nodes are linearly combined at the destination node. Then, in order to find the optimum relay position as well as the optimal destination decision threshold for a given number of molecules released by the source and the relay nodes, we formulate a joint optimization problem whose objective is to minimize the bit error probability of the network. To solve this optimization problem, we propose an iterative algorithm based on the block coordinate descent algorithm and study its convergence behavior. Numerical results show that the error performance can be improved by optimizing the position of the relay node. Our analyses help us in designing a reliable cooperative diffusion-based MC network.

A Mathematical Model of Non-Diffusion-Based Mobile Molecular Communication Networks

This letter presents a mathematical model of molecular communication networks where mobile bio-nanomachines coordinate their motion by using non-diffusive surface-bound molecules for detecting and localizing spatially distributed targets in the environment. The mathematical model assumes that bio-nanomachines release two types of molecule: repellents to distribute bio-nanomachines in search of targets and attractants to attract distributed bio- nanomachines toward target locations. The two types of molecule assumed in this letter are non-diffusive, meaning that molecules bind to a surface in the environment, creating concentration gradients on the surface in order to distribute bio-nanomachines according to the target distribution. In this letter, we first develop dimensionless equations for the non-diffusion-based mobile molecular communication networks. We then perform mathematical analysis to show that, at steady-state, bio- nanomachines distribute according to a given target distribution. Finally, we demonstrate through numerical experiments that the bio-nanomachine distribution converges to the steady-state solution.

Microbiome, probiotics and neurodegenerative diseases: deciphering the gut brain axis.

The gut microbiota is essential to health and has recently become a target for live bacterial cell biotherapies for various chronic diseases including metabolic syndrome, diabetes, obesity and neurodegenerative disease. Probiotic biotherapies are known to create a healthy gut environment by balancing bacterial populations and promoting their favorable metabolic action. The microbiota and its respective metabolites communicate to the host through a series of biochemical and functional links thereby affecting host homeostasis and health. In particular, the gastrointestinal tract communicates with the central nervous system through the gut-brain axis to support neuronal development and maintenance while gut dysbiosis manifests in neurological disease. There are three basic mechanisms that mediate the communication between the gut and the brain: direct neuronal communication, endocrine signaling mediators and the immune system. Together, these systems create a highly integrated molecular communication network that link systemic imbalances with the development of neurodegeneration including insulin regulation, fat metabolism, oxidative markers and immune signaling. Age is a common factor in the development of neurodegenerative disease and probiotics prevent many harmful effects of aging such as decreased neurotransmitter levels, chronic inflammation, oxidative stress and apoptosis-all factors that are proven aggravators of neurodegenerative disease. Indeed patients with Parkinson's and Alzheimer's diseases have a high rate of gastrointestinal comorbidities and it has be proposed by some the management of the gut microbiota may prevent or alleviate the symptoms of these chronic diseases.

Inter-Neuron Interference Analysis in Neuro-Synaptic Communications

The neuro-synaptic system is a molecular communication network which is important in the human body. In this system, the neurons and the junctions between them, i.e., synapses, are used for the transmission. The axonal transmission and the synaptic diffusion cause interference to the adjacent neurons. We model this effect with the interference channel studied in the communication theory. Then, we propose detection and equalization methods to compensate the effects of the interference and improve the signal detection probability at the destination neurons. Finally, we evaluate the performance of the proposed schemes with simulations. The simulations show that, in an interference neuronal channel, the proposed equalization methods outperform the non-equalized system.

NanoNS3: A network simulator for bacterial nanonetworks based on molecular communication

We present nanoNS3, a network simulator for modeling Bacterial Molecular Communication (BMC) networks. nanoNS3 is built atop the Network Simulator 3 (ns-3). nanoNS3 is designed to achieve the following goals: 1) accurately and realistically model the real world BMC, 2) maintain high computational efficiency, 3) allow newly designed protocols to be implemented easily. nanoNS3 incorporates the channel, physical (PHY) and medium access control (MAC) layers of the network stack. The simulator has models that accurately represent receiver response, microfluidic channel loss, transfer rate and error analysis, modulation, and amplitude addressing designed specifically for BMC networks. We outline the design and architecture of nanoNS3, and then validate the aforementioned features through simulation and experimental results.

Performance Evaluation of Leader–Follower-Based Mobile Molecular Communication Networks for Target Detection Applications

This paper proposes a leader–follower-based model of mobile molecular communication networks for target detection applications. The proposed model divides the application functionalities of molecular communication networks into two types of mobile bio-nanomachine: leader and follower bio-nanomachines. Leader bio-nanomachines distribute in the environment to detect a target and create an attractant gradient around the target. Follower bio-nanomachines move according to the attractant gradient established by leader bio-nanomachines; they approach the target and perform necessary functionalities, such as releasing drug molecules. This paper develops mathematical expressions for the proposed model, describes wet laboratory experiments designed to estimate model parameters, and performs biologically realistic computer simulation experiments to evaluate the performance of the proposed model. The main contributions of this paper are to demonstrate the functional division of molecular communication networks, which will facilitate the design and development of molecular communication networks. Furthermore, insight into the application-level performance of molecular communication networks will be provided based on the proposed model.

Molecular Communication and Nanonetwork for Targeted Drug Delivery: A Survey

Molecular communication (MC) and molecular network (MN) are communication paradigms that use biochemical signaling to achieve information exchange among naturally and artificially synthesized nanosystems. Among the envisaged application areas of MC and MN is the field of nanomedicine, where the subject of targeted drug delivery (TDD) is at the forefront. Typically, when someone gets sick, therapeutic drugs are administered to the person for healing purposes. Since no therapeutic drug can be effective until it is delivered to the target site in the body, different modalities to improve the delivery of drugs to the targeted sites are being explored in contemporary research. The most promising of these modalities is TDD. TDD modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. Research in TDD has been going on for many years in the field of medical science; however, the translation of expectations and promises to clinical reality has not been satisfactorily achieved because of several challenges. The exploration of TDD ideas under the MC and MN paradigms is considered as an option to addressing these challenges and to facilitate the translation of TDD from the bench to the patients’ bedsides. Over the past decade, there have been some research efforts made in exploring the ideas of TDD on the MC and MN platforms. While the number of research output in terms of scientific articles is few at the moment, the desire in the scientific community to participate in realizing the goal of TDD is quite high as is evidence from the rise in research output over the last few years. To increase awareness and provide the multidisciplinary research community with the necessary background information on TDD, this paper presents a visionary survey of this subject within the domain of MC and MN. We start by introducing in an elaborate manner, the motivation behind the application of MC and MN paradigms to the study and implementation of TDD. Specifically, an explanation on how MC-based TDD concepts differ from traditional TDD being explored under the field of medical science is provided. We also summarize the taxonomy of the different perspectives through which MC-based TDD research can be viewed. System models and design challenges/requirements for developing MC-based TDD are discussed. Various metrics that can be used to evaluate the performance of MC-based TDD systems are highlighted. We also provide a discussion on the envisaged path from contemporary research activities to clinical implementation of the MC-based TDD. Finally, we discuss issues such as informatics and software tools, as well as issues that border on the requirement for standards and regulatory policies in MC-based TDD research and practice.

Generalized Solution for the Demodulation of Reaction Shift Keying Signals in Molecular Communication Networks

This paper considers a diffusion-based molecular communication system where the transmitter uses Reaction Shift Keying (RSK) as the modulation scheme. We focus on the demodulation of RSK signal at the receiver. The receiver consists of a front-end molecular circuit and a back-end demodulator. The front-end molecular circuit is a set of chemical reactions consisting of multiple chemical species. The optimal demodulator computes the posteriori probability of the transmitted symbols given the history of the observation. The derivation of the optimal demodulator requires the solution to a specific Bayesian filtering problem. The solution to this Bayesian filtering problem had been derived for a few specific molecular circuits and specific choice(s) of observed chemical species. The derivation of such solution is also lengthy. The key contribution of this paper is to present a general solution to this Bayesian filtering problem which can be applied to any molecular circuit and any choice of observed species.

Maximum <italic>A-Posteriori</italic> Decoding for Diffusion-Based Molecular Communication Using Analog Filters

Molecular communication is a promising approach to realize the communication between nanoscale devices. In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules. The transmitter uses different time-varying functions of concentration of signalling molecules (called emission patterns) to represent different transmission symbols. The signalling molecules diffuse freely in the medium. The receiver is assumed to consist of a number of receptors, which can be in ON or OFF state. When the signalling molecules arrive at the receiver, they react with the receptors and switch them from OFF to ON state probabilistically. The receptors remain ON for a random amount of time before reverting to the OFF state. This paper assumes that the receiver uses the continuous history of receptor state to infer the transmitted symbol. Furthermore, it assumes that the transmitter uses two transmission symbols and approaches the decoding problem from the maximum a posteriori (MAP) framework. Specifically, the decoding is realized by calculating the logarithm of the ratio of the posteriori probabilities of the two transmission symbols, or log-MAP ratio. A contribution of this paper is to show that the computation of log-MAP ratio can be performed by an analog filter. The receiver can therefore use the output of this filter to decide which symbol has been sent. This analog filter provides insight on what information is important for decoding. In particular, the timing at which the receptors switch from OFF to ON state, the number of OFF receptors and the mean number of signalling molecules at the receiver are important. Numerical examples are used to illustrate the property of this decoding method.

A Markovian Approach to the Optimal Demodulation of Diffusion-Based Molecular Communication Networks

In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules (or ligands) in a fluid medium. This paper assumes that the transmitter uses different chemical reactions to generate different emission patterns of signalling molecules to represent different transmission symbols, and the receiver consists of receptors. When the signalling molecules arrive at the receiver, they may react with the receptors to form ligand-receptor complexes. Our goal is to study the demodulation in this setup assuming that the transmitter and receiver are synchronised. We derive an optimal demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model which includes the chemical reactions in the transmitter, diffusion in the transmission medium and the ligand-receptor process in the receiver. This model, which takes the form of a continuous-time Markov process, captures the noise in the receiver signal due to the stochastic nature of chemical reactions and diffusion. We then adopt a maximum a posterior framework and use Bayesian filtering to derive the optimal demodulator. We use numerical examples to illustrate the properties of this optimal demodulator.

An intra-body molecular communication networks framework for continuous health monitoring and diagnosis.

Intra-body communication networks are designed to interconnect nano- or micro-sized sensors located inside the body for health monitoring and drug delivery. The most promising solutions are made of implanted nanosensors to timely monitor the body for the presence of specific diseases and pronounce a diagnosis without the intervention of a physician. In this manner, several deadly health conditions such as heart attacks are avoided through the early in vivo detection of their biomarkers. In reality, nanosensors are challenged by the individual specificities, molecular noise, limited durability, and low energy resources. In this paper, a framework is proposed for estimating and detecting diseases and localizing the nanosensors. This framework is based on molecular communication, a novel communication paradigm where information is conveyed through molecules. Through the case study of the shedding of endothelial cells as an early biomarker for heart attack, the intra-body molecular communication networks framework is shown to resolve major issues with in vivo nanosensors and lay the foundations of low-complexity biomedical signal processing algorithms for continuous disease monitoring and diagnosis.

Capacity of Diffusion-Based Molecular Communication Networks Over LTI-Poisson Channels

In this paper, the capacity of a diffusion-based molecular communication network under the model of a Linear Time Invariant-Poisson (LTI-Poisson) channel is studied. Introduced in the context of molecular communication, the LTI-Poisson model is a natural extension of the conventional memoryless Poisson channel to include memory. Exploiting prior art on linear intersymbol interference (ISI) channels, a computable finite-letter characterization of the capacity of single-hop LTI-Poisson networks is provided. Then, the problem of finding more explicit bounds on the capacity is examined, where lower and upper bounds for the point to point case are provided. Furthermore, an approach for bounding mutual information in the low SNR regime using the symmetrized KL divergence is introduced and its applicability to Poisson channels is shown. To the best of our knowledge, the first upper bound on the capacity of Poisson channel with a maximum transmission constraint in the low SNR regime is found. Numerical results show that the proposed upper bound is of the same order as the capacity in the low SNR regime.

Analysis and Design of Multi-Hop Diffusion-Based Molecular Communication Networks

In this paper, we consider a multi-hop molecular communication network consisting of one nanotransmitter, one nanoreceiver, and multiple nanotransceivers acting as relays. We consider three different relaying schemes to improve the range of diffusion-based molecular communication. In the first scheme, different types of messenger molecules are utilized in each hop of the multi-hop network. In the second and third schemes, we assume that two types of molecules and one type of molecule are utilized in the network, respectively. We identify self-interference, backward intersymbol interference (backward-ISI), and forward-ISI as the performance-limiting effects for the second and third relaying schemes. Furthermore, we consider two relaying modes analogous to those used in wireless communication systems, namely full-duplex and half-duplex relaying. We propose the adaptation of the decision threshold as an effective mechanism to mitigate self-interference and backward-ISI at the relay for full-duplex and half-duplex transmission. We derive closed-form expressions for the expected end-to-end error probability of the network for the three considered relaying schemes. Furthermore, we derive closed-form expressions for the optimal number of molecules released by the nanotransmitter and the optimal detection threshold of the nanoreceiver for minimization of the expected error probability of each hop.

Impact of Receiver Reaction Mechanisms on the Performance of Molecular Communication Networks

In a molecular communication network, transmitters and receivers communicate by using signalling molecules. At the receivers, the signalling molecules react, via a chain of chemical reactions, to produce output molecules. The counts of output molecules over time is considered to be the output signal of the receiver. This output signal is used to detect the presence of signalling molecules at the receiver. The output signal is noisy due to the stochastic nature of diffusion and chemical reactions. The aim of this paper is to characterise the properties of the output signals for two types of receivers, which are based on two different types of reaction mechanisms. We derive analytical expressions for the mean, variance and frequency properties of these two types of receivers. These expressions allow us to study the properties of these two types of receivers. In addition, our model allows us to study the effect of the diffusibility of the receiver membrane on the performance of the receivers.

Nanoscale molecular communication networks: a game-theoretic perspective

Currently, communication between nanomachines is an important topic for the development of novel devices. To implement a nanocommunication system, diffusion-based molecular communication is considered as a promising bio-inspired approach. Various technical issues about molecular communications, including channel capacity, noise and interference, and modulation and coding, have been studied in the literature, while the resource allocation problem among multiple nanomachines has not been well investigated, which is a very important issue since all the nanomachines share the same propagation medium. Considering the limited computation capability of nanomachines and the expensive information exchange cost among them, in this paper, we propose a game-theoretic framework for distributed resource allocation in nanoscale molecular communication systems. We first analyze the inter-symbol and inter-user interference, as well as bit error rate performance, in the molecular communication system. Based on the interference analysis, we formulate the resource allocation problem as a non-cooperative molecule emission control game, where the Nash equilibrium is found and proved to be unique. In order to improve the system efficiency while guaranteeing fairness, we further model the resource allocation problem using a cooperative game based on the Nash bargaining solution, which is proved to be proportionally fair. Simulation results show that the Nash bargaining solution can effectively ensure fairness among multiple nanomachines while achieving comparable social welfare performance with the centralized scheme.

Fluorescent molecules as transceiver nanoantennas: the first practical and high-rate information transfer over a nanoscale communication channel based on FRET.

Nanocommunications via Förster Resonance Energy Transfer (FRET) is a promising means of realising collaboration between photoactive nanomachines to implement advanced nanotechnology applications. The method is based on exchange of energy levels between fluorescent molecules by the FRET phenomenon which intrinsically provides a virtual nanocommunication link. In this work, further to the extensive theoretical studies, we demonstrate the first information transfer through a FRET-based nanocommunication channel. We implement a digital communication system combining macroscale transceiver instruments and a bulk solution of fluorophore nanoantennas. The performance of the FRET-based Multiple-Input and Multiple-Output (MIMO) nanocommunication channel between closely located mobile nanoantennas in the sample solution is evaluated in terms of Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER) obtained for the transmission rates of 50 kbps, 150 kbps and 250 kbps. The results of the performance evaluation are very promising for the development of high-rate and reliable molecular communication networks at nanoscale.

Proteomics and integrative genomics for unraveling the mysteries of spermatogenesis: the strategies of a team.

The strikingly complex structural organization of the mammalian testis in vivo creates particular difficulties for studies of its organization, function and regulation. These difficulties are particularly pronounced for investigations of the molecular communication networks within the seminiferous tubules that govern spermatogenesis. The use of classical molecular and cell biology approaches to unravel this complexity has proved problematic, due to difficulties in maintaining differentiated germ cells in vitro, in particular. The lack of a suitable testing ground has led to a greater reliance on high-quality proteomic and genomic analyses as a prelude to the in vitro antx1d in vivo testing of hypotheses. In this study, we highlight the options currently available for research, as used in our laboratory, in which proteomic and integrative genomic strategies are applied to the study of spermatogenesis in mammals. We will comment on results providing insight into the molecular mechanisms underlying normal and pathological spermatogenesis and new perspectives for the treatment of male infertility in humans. Finally, we will discuss the relevance of our strategies and the unexpected potential and perspectives they offer to teams involved in the study of male reproduction, within the framework of the Human Proteome Project. Integrative genomics is becoming a powerful strategy for discovering the biological significance hidden in proteomic datasets. This work introduces some of the integrative genomic concepts and works used by our team to gain new insight into mammalian spermatogenesis, a remarkably sophisticated process. We demonstrate the relevance of these integrative approaches to understand the cellular cross talks established between the somatic Sertoli cells and the germ cell lineage, within the seminiferous epithelium. Our work also contributes to new knowledge on the pathophysiology of testicular function, with promising clinical applications. This article is part of a Special Issue entitled: 20years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

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.

Noise properties of linear molecular communication networks

Molecular communication networks consist of transmitters and receivers distributed in a fluid medium. The communication in these networks is realised by the transmitters emitting signalling molecules, which are diffused in the medium to reach the receivers. This paper investigates the properties of noise, or the variance of the receiver output, in molecular communication networks. The noise in these networks come from multiple sources: stochastic emission of signalling molecules by the transmitters, diffusion in the fluid medium and stochastic reaction kinetics at the receivers. We model these stochastic fluctuations by using an extension of the master equation. We show that, under certain conditions, the receiver outputs of linear molecular communication networks are Poisson distributed. The derivation also shows that noise in these networks is a nonlinear function of the network parameters and is non-additive. Numerical examples are provided to illustrate the properties of this type of Poisson channels.