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}
Molecular communication (MC) employs chemical molecules for information transfer in environments where electromagnetic signals are ineffective. However, the diffusion mechanism introduces signal-dependent noise (SDN), complicating accurate signal recovery. Traditional model-based methods struggle to handle SDN’s complex dynamics and depend heavily on optimal parameter tuning, limiting their adaptability to temporal variations. To tackle these challenges, this paper introduces a hybrid recurrent neural network (RNN) model that effectively captures both short- and long-term dependencies within MC signals, surpassing the performance of single RNN models and traditional approaches. This model offers a promising data-driven solution for noise mitigation in MC, with its effectiveness validated through numerical simulation results.
{"title":"Hybrid Recurrent Neural Network for Signal-Dependent Noise Suppression in Molecular Communication","authors":"Cheng Xiang;Yaqing Zhang;Yu Huang;Weiqiang Tan;Xuan Chen;Miaowen Wen","doi":"10.1109/TMBMC.2025.3546208","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3546208","url":null,"abstract":"Molecular communication (MC) employs chemical molecules for information transfer in environments where electromagnetic signals are ineffective. However, the diffusion mechanism introduces signal-dependent noise (SDN), complicating accurate signal recovery. Traditional model-based methods struggle to handle SDN’s complex dynamics and depend heavily on optimal parameter tuning, limiting their adaptability to temporal variations. To tackle these challenges, this paper introduces a hybrid recurrent neural network (RNN) model that effectively captures both short- and long-term dependencies within MC signals, surpassing the performance of single RNN models and traditional approaches. This model offers a promising data-driven solution for noise mitigation in MC, with its effectiveness validated through numerical simulation results.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"283-291"},"PeriodicalIF":2.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272878","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 : 2025-02-26DOI: 10.1109/TMBMC.2025.3546207
Luyao Zhang;Yue Sun;Dong Du;Yifan Chen
This study proposes a novel light-driven nanorobots swarm (NS) aggregation method to enhance tumor targeting efficiency. To replicate the structured and directional flow of density blood vessels near tumors, we employed a Manhattan-geometry vasculature (MGV) model, which mimics the complex, density-connected vasculature near the tumor site. This model significantly influences NS navigation and aggregation behavior, providing more realistic movement dynamics insights. We analyzed NS dynamics under light illumination, focusing on drag and thermophoretic forces. Comparisons with magnetic field-driven and non-external force strategies across three objective functions show that light-driven targeting increases efficiency by 4% to 46% and reduces targeting time by up to 27.9%. The MGV model enables precise predictions of NS movement, optimizing aggregation toward tumor tissues. These findings demonstrate the potential of light-driven NS aggregation to enhance tumor-targeting therapies, offering advantages over magnetic control in complex biological environments, with implications for photothermal therapy and precision drug delivery.
{"title":"Dynamics and Kinetics of Light-Driven Nanorobots Swarm Aggregation for Tumor Targeting","authors":"Luyao Zhang;Yue Sun;Dong Du;Yifan Chen","doi":"10.1109/TMBMC.2025.3546207","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3546207","url":null,"abstract":"This study proposes a novel light-driven nanorobots swarm (NS) aggregation method to enhance tumor targeting efficiency. To replicate the structured and directional flow of density blood vessels near tumors, we employed a Manhattan-geometry vasculature (MGV) model, which mimics the complex, density-connected vasculature near the tumor site. This model significantly influences NS navigation and aggregation behavior, providing more realistic movement dynamics insights. We analyzed NS dynamics under light illumination, focusing on drag and thermophoretic forces. Comparisons with magnetic field-driven and non-external force strategies across three objective functions show that light-driven targeting increases efficiency by 4% to 46% and reduces targeting time by up to 27.9%. The MGV model enables precise predictions of NS movement, optimizing aggregation toward tumor tissues. These findings demonstrate the potential of light-driven NS aggregation to enhance tumor-targeting therapies, offering advantages over magnetic control in complex biological environments, with implications for photothermal therapy and precision drug delivery.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"269-282"},"PeriodicalIF":2.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272912","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 : 2025-02-20DOI: 10.1109/TMBMC.2025.3544111
Dongliang Jing;Linjuan Li;Zhen Cheng;Lin Lin;Andrew W. Eckford
Information molecules play a crucial role in molecular communication (MC), acting as carriers for information transfer. A common approach to get information molecules in MC involves harvesting them from the environment; however, the harvested molecules are often a mixture of various environmental molecules, and the initial concentration ratios in the reservoirs are identical, which hampers high-fidelity transmission techniques such as molecular shift keying (MoSK). This paper presents a transmitter design that harvests molecules from the surrounding environment and stores them in two reservoirs. To separate the mixed molecules, energy is consumed to transfer them between reservoirs. Given limited energy resources, this work explores energy-efficient strategies to optimize transmitter performance. Through theoretical analysis and simulations, we investigate different methods for moving molecules between reservoirs. The results demonstrate that transferring higher initial concentration molecules enhances transmitter performance, while using fewer molecules per transfer further improves efficiency. These findings provide valuable insights for optimizing MC systems through energy-efficient molecule transfer techniques.
{"title":"Energy Efficient Transmitter Creation by Consuming Free Energy in Molecular Communication","authors":"Dongliang Jing;Linjuan Li;Zhen Cheng;Lin Lin;Andrew W. Eckford","doi":"10.1109/TMBMC.2025.3544111","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3544111","url":null,"abstract":"Information molecules play a crucial role in molecular communication (MC), acting as carriers for information transfer. A common approach to get information molecules in MC involves harvesting them from the environment; however, the harvested molecules are often a mixture of various environmental molecules, and the initial concentration ratios in the reservoirs are identical, which hampers high-fidelity transmission techniques such as molecular shift keying (MoSK). This paper presents a transmitter design that harvests molecules from the surrounding environment and stores them in two reservoirs. To separate the mixed molecules, energy is consumed to transfer them between reservoirs. Given limited energy resources, this work explores energy-efficient strategies to optimize transmitter performance. Through theoretical analysis and simulations, we investigate different methods for moving molecules between reservoirs. The results demonstrate that transferring higher initial concentration molecules enhances transmitter performance, while using fewer molecules per transfer further improves efficiency. These findings provide valuable insights for optimizing MC systems through energy-efficient molecule transfer techniques.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"292-303"},"PeriodicalIF":2.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272886","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}
This paper presents the design, implementation, and evaluation of a general-purpose simulation platform for multicellular molecular communication systems. Built on an agent-based model, the platform offers flexibility to simulate diverse multicellular systems, such as cancer spheroids and vascular-like networks. It incorporates efficient algorithms, including Cell-List and Barnes-Hut, for calculating cell-cell interaction forces and supports dynamic behaviors such as cell division, growth, and death. The platform’s capabilities are demonstrated through use cases, highlighting its versatility and coding efficiency. The simulation platform serves as a valuable tool for advancing research in molecular communication and understanding the collective behavior of complex multicellular systems.
{"title":"A General-Purpose Simulation Platform for Multicellular Molecular Communication Systems","authors":"Takanori Saiki;Shohei Imanaka;Shouhei Kobayashi;Tadashi Nakano","doi":"10.1109/TMBMC.2025.3544141","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3544141","url":null,"abstract":"This paper presents the design, implementation, and evaluation of a general-purpose simulation platform for multicellular molecular communication systems. Built on an agent-based model, the platform offers flexibility to simulate diverse multicellular systems, such as cancer spheroids and vascular-like networks. It incorporates efficient algorithms, including Cell-List and Barnes-Hut, for calculating cell-cell interaction forces and supports dynamic behaviors such as cell division, growth, and death. The platform’s capabilities are demonstrated through use cases, highlighting its versatility and coding efficiency. The simulation platform serves as a valuable tool for advancing research in molecular communication and understanding the collective behavior of complex multicellular systems.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"152-165"},"PeriodicalIF":2.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272881","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}
Intersymbol Interference (ISI) has a detrimental impact on any Molecular Communication via Diffusion (MCvD) system. Also, the receiver noise can severely degrade the MCvD channel performance. However, the channel codes proposed in the literature for the MCvD system have only addressed one of these two challenges independently. In this paper, we have designed single Error Correcting Codes in an MCvD system with channel memory and noise. We have also provided encoding and decoding algorithms for the proposed codes, which are simple to follow despite having a non-linear code construction. Finally, through simulation results, we show that the proposed single ECCs, for given code parameters, perform better than the existing codes in the literature in combating the effect of ISI in the channel and improving the average Bit Error Rate (BER) performance in a noisy channel.
{"title":"On Designing Novel ISI-Reducing Single Error Correcting Codes in an MCvD System","authors":"Tamoghno Nath;Krishna Gopal Benerjee;Adrish Banerjee","doi":"10.1109/TMBMC.2025.3544137","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3544137","url":null,"abstract":"Intersymbol Interference (ISI) has a detrimental impact on any Molecular Communication via Diffusion (MCvD) system. Also, the receiver noise can severely degrade the MCvD channel performance. However, the channel codes proposed in the literature for the MCvD system have only addressed one of these two challenges independently. In this paper, we have designed single Error Correcting Codes in an MCvD system with channel memory and noise. We have also provided encoding and decoding algorithms for the proposed codes, which are simple to follow despite having a non-linear code construction. Finally, through simulation results, we show that the proposed single ECCs, for given code parameters, perform better than the existing codes in the literature in combating the effect of ISI in the channel and improving the average Bit Error Rate (BER) performance in a noisy channel.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"228-233"},"PeriodicalIF":2.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272914","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 : 2025-01-01DOI: 10.1109/TMBMC.2024.3523930
{"title":"2024 Index IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Vol. 10","authors":"","doi":"10.1109/TMBMC.2024.3523930","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3523930","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 4","pages":"642-654"},"PeriodicalIF":2.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912560","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}
Nanoscale devices with Terahertz (THz) communication capabilities are envisioned to be deployed within human bloodstreams. Such devices will enable fine-grained sensing-based applications for detecting early indications (i.e., biomarkers) of various health conditions, as well as actuation-based ones such as targeted drug delivery. Associating the locations of such events with the events themselves would provide an additional utility for precision diagnostics and treatment. This vision yielded a new class of in-body localization coined under the term “flow-guided nanoscale localization”. Such localization can be piggybacked on THz communication for detecting body regions in which biological events were localized with the traveling time reported by nanodevices flowing with the bloodstream. From decades of research on objective benchmarking of “traditional” indoor localization and its eventual standardization (e.g., ISO/IEC18305:2016), we know that in early stages, the reported performance results were often incomplete (e.g., targeting a subset of relevant performance metrics). Reported results in the literature carried out benchmarking experiments in different evaluation environments and scenarios and utilized inconsistent performance indicators. To avoid such a “lock-in” in flowguided localization, we propose a workflow for standardized performance evaluation of such approaches. The workflow is implemented in the form of an open-source simulation framework that is able to jointly account for the mobility of the nanodevices, in-body THz communication with on-body anchors, and energy-related and other technological constraints (e.g., pulsebased modulation) at the nanodevice level. Accounting for these constraints, the framework can generate raw data to streamline into different flow-guided localization solutions for generating standardized performance benchmarks.
{"title":"Toward Standardized Performance Evaluation of Flow-Guided Nanoscale Localization","authors":"Arnau Brosa López;Filip Lemic;Gerard Calvo Bartra;Aina Pérez;Jakob Struye;Jorge Torres Gómez;Esteban Municio;Carmen Delgado;Falko Dressler;Eduard Alarcón;Jeroen Famaey;Sergi Abadal;Xavier Costa Pérez","doi":"10.1109/TMBMC.2024.3523428","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3523428","url":null,"abstract":"Nanoscale devices with Terahertz (THz) communication capabilities are envisioned to be deployed within human bloodstreams. Such devices will enable fine-grained sensing-based applications for detecting early indications (i.e., biomarkers) of various health conditions, as well as actuation-based ones such as targeted drug delivery. Associating the locations of such events with the events themselves would provide an additional utility for precision diagnostics and treatment. This vision yielded a new class of in-body localization coined under the term “flow-guided nanoscale localization”. Such localization can be piggybacked on THz communication for detecting body regions in which biological events were localized with the traveling time reported by nanodevices flowing with the bloodstream. From decades of research on objective benchmarking of “traditional” indoor localization and its eventual standardization (e.g., ISO/IEC18305:2016), we know that in early stages, the reported performance results were often incomplete (e.g., targeting a subset of relevant performance metrics). Reported results in the literature carried out benchmarking experiments in different evaluation environments and scenarios and utilized inconsistent performance indicators. To avoid such a “lock-in” in flowguided localization, we propose a workflow for standardized performance evaluation of such approaches. The workflow is implemented in the form of an open-source simulation framework that is able to jointly account for the mobility of the nanodevices, in-body THz communication with on-body anchors, and energy-related and other technological constraints (e.g., pulsebased modulation) at the nanodevice level. Accounting for these constraints, the framework can generate raw data to streamline into different flow-guided localization solutions for generating standardized performance benchmarks.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"116-127"},"PeriodicalIF":2.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645239","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 : 2024-12-23DOI: 10.1109/TMBMC.2024.3521984
Ibrahim Isik;Mitra Rezaei;Adam Noel
Spheroids are aggregates of cells that can mimic the cellular organization often found in tissues. Spheroids can be created from various cell types, including cancer cells, stem cells, and primary cells, and they serve as valuable tools in biological research. Although there are initial results on how a molecular signal can propagate between a pair of spheroids, practical experiments typically use clusters of spheroids and there isn’t a good understanding of how neighboring spheroids impact the spatiotemporal dynamics of local molecule propagation. This paper simulates a series of scenarios to gain intuition about propagation in such multi-spheroid systems for applications such as transport and drug delivery. The spheroids are modeled as porous media with a corresponding effective diffusion coefficient. System variations are considered with a higher spheroid porosity (i.e., with a higher effective diffusion coefficient) and molecule uptake by the spheroid cells (approximated as a first-order degradation reaction while molecules diffuse within the spheroid). Results show that a local crowd of spheroids will eventually slow overall propagation, such that molecules stay in the vicinity of the transmitter for longer. The results demonstrate that a single-spheroid receiver model is insufficient to accurately model propagation under these conditions.
{"title":"Single Input Multi Output Model of Molecular Communication via Diffusion With Spheroidal Receivers","authors":"Ibrahim Isik;Mitra Rezaei;Adam Noel","doi":"10.1109/TMBMC.2024.3521984","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3521984","url":null,"abstract":"Spheroids are aggregates of cells that can mimic the cellular organization often found in tissues. Spheroids can be created from various cell types, including cancer cells, stem cells, and primary cells, and they serve as valuable tools in biological research. Although there are initial results on how a molecular signal can propagate between a pair of spheroids, practical experiments typically use clusters of spheroids and there isn’t a good understanding of how neighboring spheroids impact the spatiotemporal dynamics of local molecule propagation. This paper simulates a series of scenarios to gain intuition about propagation in such multi-spheroid systems for applications such as transport and drug delivery. The spheroids are modeled as porous media with a corresponding effective diffusion coefficient. System variations are considered with a higher spheroid porosity (i.e., with a higher effective diffusion coefficient) and molecule uptake by the spheroid cells (approximated as a first-order degradation reaction while molecules diffuse within the spheroid). Results show that a local crowd of spheroids will eventually slow overall propagation, such that molecules stay in the vicinity of the transmitter for longer. The results demonstrate that a single-spheroid receiver model is insufficient to accurately model propagation under these conditions.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"101-106"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637887","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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