Pub Date : 2025-03-26DOI: 10.1109/TMBMC.2025.3554640
Nikolaos Ntetsikas;Styliana Kyriakoudi;Antonis Kirmizis Kirmizis;Ioannis Krikidis;Ian F. Akyildiz;Marios Lestas
A critical aspect of Molecular Communications (MC) is the implementation of signal detection policies amidst noise. To date, noise characterizations within the MC field have predominantly drawn from methodologies found in wireless communications literature. In this study, we diverge from existing MC research by utilizing a newly developed experimental platform that employs yeast, allowing us to consider more realistic noise characterizations based on the relevant signaling pathways. We propose suitable signal detection mechanisms tailored to this experimental setup, which focuses on yeast cell-to-cell communications. Our analysis identifies gene transcription as the primary source of noise, and we utilize a Markov birth-death process model with Poisson arrivals and departures to characterize it. The noisy expression of the FUS1 gene is best represented using a mixed Gaussian distribution model. This model serves as a foundation for evaluating the performance of Maximum Likelihood Detection mechanisms in terms of Bit Error Rate (BER) for both symbol-by-symbol and sequence transmission schemes. Error analysis indicates that appropriate adjustments to the signal threshold can reduce errors to as low as 10%, which is not negligible. In contrast, the detection of symbol sequences demonstrates enhanced error performance, achieving error rates as low as 0.4%, albeit at the cost of increased computational complexity.
{"title":"Noise Characterization and Robust Signal Detection in Yeast Pheromone Molecular Communication","authors":"Nikolaos Ntetsikas;Styliana Kyriakoudi;Antonis Kirmizis Kirmizis;Ioannis Krikidis;Ian F. Akyildiz;Marios Lestas","doi":"10.1109/TMBMC.2025.3554640","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3554640","url":null,"abstract":"A critical aspect of Molecular Communications (MC) is the implementation of signal detection policies amidst noise. To date, noise characterizations within the MC field have predominantly drawn from methodologies found in wireless communications literature. In this study, we diverge from existing MC research by utilizing a newly developed experimental platform that employs yeast, allowing us to consider more realistic noise characterizations based on the relevant signaling pathways. We propose suitable signal detection mechanisms tailored to this experimental setup, which focuses on yeast cell-to-cell communications. Our analysis identifies gene transcription as the primary source of noise, and we utilize a Markov birth-death process model with Poisson arrivals and departures to characterize it. The noisy expression of the FUS1 gene is best represented using a mixed Gaussian distribution model. This model serves as a foundation for evaluating the performance of Maximum Likelihood Detection mechanisms in terms of Bit Error Rate (BER) for both symbol-by-symbol and sequence transmission schemes. Error analysis indicates that appropriate adjustments to the signal threshold can reduce errors to as low as 10%, which is not negligible. In contrast, the detection of symbol sequences demonstrates enhanced error performance, achieving error rates as low as 0.4%, albeit at the cost of increased computational complexity.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"218-227"},"PeriodicalIF":2.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A biofilm is a microbial city. It consists of bacteria embedded in an extracellular polymeric substance (EPS) that functions as a protective barrier. Quorum sensing (QS) is a method of bacterial communication, where autoinducers (AIs) propagate via diffusion through the EPS and water channels within the biofilm. This diffusion process is anisotropic due to varying densities between the EPS and water channels. This study introduces a 2D anisotropic diffusion model for molecular communication (MC) within biofilms, analyzing information propagation between a point-to-point transmitter (TX) and receiver (RX) in bounded space. The channel impulse response is derived using Green’s function for concentration (GFC) and is validated with particle-based simulation (PBS). The outcomes reveal similar results for both isotropic and anisotropic diffusion when the TX is centrally located due to symmetry. However, anisotropic conditions lead to greater diffusion peaks when the TX is positioned off-center. Additionally, the propagation of AIs is inversely proportional to both overall biofilm size and diffusion coefficient values. It is hypothesized that anisotropic diffusion supports faster responses to hostile environmental changes because signals can propagate faster from the edge of the biofilm to the center.
{"title":"Anisotropic Diffusion Model of Communication in 2D Biofilm","authors":"Yanahan Paramalingam;Hamidreza Arjmandi;Freya Harrison;Tara Schiller;Adam Noel","doi":"10.1109/TMBMC.2025.3552991","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3552991","url":null,"abstract":"A biofilm is a microbial city. It consists of bacteria embedded in an extracellular polymeric substance (EPS) that functions as a protective barrier. Quorum sensing (QS) is a method of bacterial communication, where autoinducers (AIs) propagate via diffusion through the EPS and water channels within the biofilm. This diffusion process is anisotropic due to varying densities between the EPS and water channels. This study introduces a 2D anisotropic diffusion model for molecular communication (MC) within biofilms, analyzing information propagation between a point-to-point transmitter (TX) and receiver (RX) in bounded space. The channel impulse response is derived using Green’s function for concentration (GFC) and is validated with particle-based simulation (PBS). The outcomes reveal similar results for both isotropic and anisotropic diffusion when the TX is centrally located due to symmetry. However, anisotropic conditions lead to greater diffusion peaks when the TX is positioned off-center. Additionally, the propagation of AIs is inversely proportional to both overall biofilm size and diffusion coefficient values. It is hypothesized that anisotropic diffusion supports faster responses to hostile environmental changes because signals can propagate faster from the edge of the biofilm to the center.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"176-185"},"PeriodicalIF":2.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quorum Sensing (QS) is a bacterial cell-to-cell communication mechanism allowing to share information about cell density, to adjust gene expression accordingly. Pathogens leverage QS to coordinate virulence and antimicrobial resistance, leading to distinctive population-level behaviors. To support rational design of synthetic biology strategies counteracting these mechanisms, we first mathematically model and compare two common QS architectures: one based on a single positive feedback loop to auto-induce signal molecule synthesis, the other including an additional positive feedback to increase signal molecule receptors production. Our comprehensive analysis of these QS structures and their equilibria highlights the differences in their bistable and hysteretic behaviors. An extensive sensitivity analysis is then performed, highlighting how parameter variations may lead to phenotype alterations in system behavior. Finally, building on our sensitivity analysis, we mathematically model four distinct QS inhibition strategies - signal molecule degradation, pharmaceutical inhibition, CRISPRi, and RNAi - which lead to the design of Quorum-Quenching (QQ) therapeutic approaches. Despite the underlying complex mechanisms, we demonstrate that the effect of the proposed QQ strategies can be captured by varying specific parameters within the QS models. We numerically analyze how these strategies affect the steady-state behavior of both QS models, identifying critical parameter thresholds for effective QS suppression.
{"title":"Quorum Sensing Model Structures Inspire the Design of Quorum Quenching Strategies","authors":"Chiara Cimolato;Gianluca Selvaggio;Luca Marchetti;Giulia Giordano;Luca Schenato;Massimo Bellato","doi":"10.1109/TMBMC.2025.3554671","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3554671","url":null,"abstract":"Quorum Sensing (QS) is a bacterial cell-to-cell communication mechanism allowing to share information about cell density, to adjust gene expression accordingly. Pathogens leverage QS to coordinate virulence and antimicrobial resistance, leading to distinctive population-level behaviors. To support rational design of synthetic biology strategies counteracting these mechanisms, we first mathematically model and compare two common QS architectures: one based on a single positive feedback loop to auto-induce signal molecule synthesis, the other including an additional positive feedback to increase signal molecule receptors production. Our comprehensive analysis of these QS structures and their equilibria highlights the differences in their bistable and hysteretic behaviors. An extensive sensitivity analysis is then performed, highlighting how parameter variations may lead to phenotype alterations in system behavior. Finally, building on our sensitivity analysis, we mathematically model four distinct QS inhibition strategies - signal molecule degradation, pharmaceutical inhibition, CRISPRi, and RNAi - which lead to the design of Quorum-Quenching (QQ) therapeutic approaches. Despite the underlying complex mechanisms, we demonstrate that the effect of the proposed QQ strategies can be captured by varying specific parameters within the QS models. We numerically analyze how these strategies affect the steady-state behavior of both QS models, identifying critical parameter thresholds for effective QS suppression.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"201-217"},"PeriodicalIF":2.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938711","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-25DOI: 10.1109/TMBMC.2025.3554674
Aiman Khalil;Kurt J. A. Pumares;Anne Skogberg;Pasi Kallio;Deirdre Kilbane;Daniel P. Martins
Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g., collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions-brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures.
{"title":"Molecular Communications Loss Budget for tsRNA Detection in the Brain","authors":"Aiman Khalil;Kurt J. A. Pumares;Anne Skogberg;Pasi Kallio;Deirdre Kilbane;Daniel P. Martins","doi":"10.1109/TMBMC.2025.3554674","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3554674","url":null,"abstract":"Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g., collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions-brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 3","pages":"405-417"},"PeriodicalIF":2.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-19DOI: 10.1109/TMBMC.2025.3552959
Nayereh FallahBagheri;Özgür B. Akan
Molecular communication (MC) within the synaptic cleft is vital for neurotransmitter diffusion, a process critical to cognitive functions. In Alzheimer’s Disease (AD), beta-amyloid oligomers (A$beta $ os) disrupt this communication, leading to synaptic dysfunction. This paper investigates the molecular interactions between glutamate, a key neurotransmitter, and A$beta $ os within the synaptic cleft, aiming to elucidate the underlying mechanisms of this disruption. Through stochastic modeling, we simulate the dynamics of A$beta $ os and their impact on glutamate diffusion. The findings, validated by comparing simulated results with existing experimental data, demonstrate that A$beta $ os serve as physical obstacles, hindering glutamate movement and increasing collision frequency. This impairment of synaptic transmission and long-term potentiation (LTP) by binding to receptors on the postsynaptic membrane is further validated against known molecular interaction behaviors observed in similar neurodegenerative contexts. The study also explores potential therapeutic strategies to mitigate these disruptions. By enhancing our understanding of these molecular interactions, this research contributes to the development of more effective treatments for AD, with the ultimate goal of alleviating synaptic impairments associated with the disease.
{"title":"A Molecular Communication Perspective of Alzheimer’s Disease: Impact of Amyloid Beta Oligomers on Glutamate Diffusion in the Synaptic Cleft","authors":"Nayereh FallahBagheri;Özgür B. Akan","doi":"10.1109/TMBMC.2025.3552959","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3552959","url":null,"abstract":"Molecular communication (MC) within the synaptic cleft is vital for neurotransmitter diffusion, a process critical to cognitive functions. In Alzheimer’s Disease (AD), beta-amyloid oligomers (A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os) disrupt this communication, leading to synaptic dysfunction. This paper investigates the molecular interactions between glutamate, a key neurotransmitter, and A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os within the synaptic cleft, aiming to elucidate the underlying mechanisms of this disruption. Through stochastic modeling, we simulate the dynamics of A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os and their impact on glutamate diffusion. The findings, validated by comparing simulated results with existing experimental data, demonstrate that A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os serve as physical obstacles, hindering glutamate movement and increasing collision frequency. This impairment of synaptic transmission and long-term potentiation (LTP) by binding to receptors on the postsynaptic membrane is further validated against known molecular interaction behaviors observed in similar neurodegenerative contexts. The study also explores potential therapeutic strategies to mitigate these disruptions. By enhancing our understanding of these molecular interactions, this research contributes to the development of more effective treatments for AD, with the ultimate goal of alleviating synaptic impairments associated with the disease.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"186-200"},"PeriodicalIF":2.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TMBMC.2025.3525995
{"title":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","authors":"","doi":"10.1109/TMBMC.2025.3525995","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3525995","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"C2-C2"},"PeriodicalIF":2.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TMBMC.2025.3526017
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TMBMC.2025.3526017","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3526017","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"C3-C3"},"PeriodicalIF":2.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-11DOI: 10.1109/TMBMC.2025.3550323
Mohammad Zoofaghari;Krizia Sagini;Martin Damrath;Azar Zargarnia;Håkon Flaten;Mladen Veletić;Alicia Llorente;Ilangko Balasingham
Extracellular vesicles (EVs) are lipid bilayer enclosed nanovesicles involved in intercellular communication. EVs are emerging as potential cancer biomarkers, providing insights into the condition of parent cancer cells. Their composition and entry into the bloodstream are influenced by factors such as tumor grade, type, and the configuration of the vascular network at the release site. In this work, we propose a computer simulation model to emulate the penetration of EVs into the bloodstream. We take into account convective and diffusive parameters that are influenced by the tumor’s characteristics, and the configuration of the vasculature and lymphatic network. We investigate the penetration rate of EVs into the bloodstream in terms of various parameters such as vessel wall permeability and the configuration of the vasculature and lymphatic networks. Our parametric study using a 2D model demonstrates that increasing the permeability coefficient, as observed in tumor tissue, could lead to a two-fold increase in EV penetration rate into the bloodstream. We believe that this model offers pre-experimental insights concerning liquid biopsy assays and the metastatic progression of the disease.
{"title":"In Silico Study of Bloodstream Penetrating Extracellular Vesicles","authors":"Mohammad Zoofaghari;Krizia Sagini;Martin Damrath;Azar Zargarnia;Håkon Flaten;Mladen Veletić;Alicia Llorente;Ilangko Balasingham","doi":"10.1109/TMBMC.2025.3550323","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3550323","url":null,"abstract":"Extracellular vesicles (EVs) are lipid bilayer enclosed nanovesicles involved in intercellular communication. EVs are emerging as potential cancer biomarkers, providing insights into the condition of parent cancer cells. Their composition and entry into the bloodstream are influenced by factors such as tumor grade, type, and the configuration of the vascular network at the release site. In this work, we propose a computer simulation model to emulate the penetration of EVs into the bloodstream. We take into account convective and diffusive parameters that are influenced by the tumor’s characteristics, and the configuration of the vasculature and lymphatic network. We investigate the penetration rate of EVs into the bloodstream in terms of various parameters such as vessel wall permeability and the configuration of the vasculature and lymphatic networks. Our parametric study using a 2D model demonstrates that increasing the permeability coefficient, as observed in tumor tissue, could lead to a two-fold increase in EV penetration rate into the bloodstream. We believe that this model offers pre-experimental insights concerning liquid biopsy assays and the metastatic progression of the disease.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"166-175"},"PeriodicalIF":2.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1109/TMBMC.2025.3547892
Muralikrishnna G. Sethuraman;Megan A. McSweeney;Mark P. Styczynski;Faramarz Fekri
Monitoring the levels of biomarkers for diagnostic applications has significant potential for impacts on patient care, but the measurement of all relevant biomarkers for a given set of conditions is often too expensive or unwieldy to be feasible at scale. Here, we propose a novel computational method for detecting changes in the levels of multiple target molecules from a complex sample via a small, cost-effective group of biosensors. We use the framework of density evolution (DE), a technique commonly used in the design of linear error-correcting codes for transmission over noisy channels, to develop an approach for localizing changes to a small subset of input signals based on a few simple output signals. As a biologically relevant testbed, we sought to detect the changes in the levels of multiple different microRNAs (miRNAs), which are nucleic acid molecules that are being increasingly studied and used as biomarkers. We accomplished this via the use of a class of molecules called “toehold switches” to create biosensors each capable of detecting multiple different miRNA sequences via a single output, with an overlap in sensitivity patterns between the different biosensors. A small number of these sensors were then used for inference of miRNA profiles. We demonstrate the potential utility of our approach with real data. Experimental results indicate the promising outcomes regarding the effectiveness of our method in detecting changes in miRNA concentrations.
{"title":"Construction of an Array of Biosensors Using Density Evolution for MicroRNA Monitoring","authors":"Muralikrishnna G. Sethuraman;Megan A. McSweeney;Mark P. Styczynski;Faramarz Fekri","doi":"10.1109/TMBMC.2025.3547892","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3547892","url":null,"abstract":"Monitoring the levels of biomarkers for diagnostic applications has significant potential for impacts on patient care, but the measurement of all relevant biomarkers for a given set of conditions is often too expensive or unwieldy to be feasible at scale. Here, we propose a novel computational method for detecting changes in the levels of multiple target molecules from a complex sample via a small, cost-effective group of biosensors. We use the framework of density evolution (DE), a technique commonly used in the design of linear error-correcting codes for transmission over noisy channels, to develop an approach for localizing changes to a small subset of input signals based on a few simple output signals. As a biologically relevant testbed, we sought to detect the changes in the levels of multiple different microRNAs (miRNAs), which are nucleic acid molecules that are being increasingly studied and used as biomarkers. We accomplished this via the use of a class of molecules called “toehold switches” to create biosensors each capable of detecting multiple different miRNA sequences via a single output, with an overlap in sensitivity patterns between the different biosensors. A small number of these sensors were then used for inference of miRNA profiles. We demonstrate the potential utility of our approach with real data. Experimental results indicate the promising outcomes regarding the effectiveness of our method in detecting changes in miRNA concentrations.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 3","pages":"335-343"},"PeriodicalIF":2.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1109/TMBMC.2025.3546503
Zhen Cheng;Heng Liu;Ziyan Xu;Jiaxin Li;Kaikai Chi
Diffusive molecular communication (DMC) utilizes the emission, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The estimation of emission time and arrival time of molecules in DMC system plays important roles in the resource consumption at the receivers. Existing traditional strategies for the derivation of emission time and arrival time mainly focus on known channel state information (CSI). In this paper, we propose a deep learning method for estimating emission time and arrival time of the molecules in DMC system with unknown CSI by using Transformer-based model, respectively. The simulation results show that the emission time and arrival time of molecules can be accurately estimated by the Transformer-based model which exhibits better estimation and generalization abilities than deep neural network (DNN) model.
{"title":"Deep Learning-Based Estimation of Emission Time and Arrival Time in Diffusive Multi-Receiver Molecular Communication","authors":"Zhen Cheng;Heng Liu;Ziyan Xu;Jiaxin Li;Kaikai Chi","doi":"10.1109/TMBMC.2025.3546503","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3546503","url":null,"abstract":"Diffusive molecular communication (DMC) utilizes the emission, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The estimation of emission time and arrival time of molecules in DMC system plays important roles in the resource consumption at the receivers. Existing traditional strategies for the derivation of emission time and arrival time mainly focus on known channel state information (CSI). In this paper, we propose a deep learning method for estimating emission time and arrival time of the molecules in DMC system with unknown CSI by using Transformer-based model, respectively. The simulation results show that the emission time and arrival time of molecules can be accurately estimated by the Transformer-based model which exhibits better estimation and generalization abilities than deep neural network (DNN) model.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"257-268"},"PeriodicalIF":2.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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