Pub Date : 2024-11-18DOI: 10.1109/TMBMC.2024.3501576
Bat-Ider Tumenbayar;Khanh Pham;John C. Biber;Rhonda Drewes;Yongho Bae
Cardiovascular diseases (CVDs) and pathologies are often driven by changes in molecular signaling and communication, as well as in cellular and tissue components, particularly those involving the extracellular matrix (ECM), cytoskeleton, and immune response. The fine-wire vascular injury model is commonly used to study neointimal hyperplasia and vessel stiffening, but it is not typically considered a model for CVDs. However, applying this model to study CVDs in conjunction with established processes could offer valuable insights. In this paper, we hypothesize that vascular injury induces changes in gene expression, molecular communication, and biological processes similar to those observed in CVDs at both the transcriptome and protein levels. To investigate this, we analyzed gene expression in microarray datasets from injured and uninjured femoral arteries in mice two weeks post-injury, identifying 1,467 significantly and differentially expressed genes involved in several CVDs such as including vaso-occlusion, arrhythmia, and atherosclerosis. We further constructed a protein-protein interaction network with seven functionally distinct clusters, with notable enrichment in ECM, metabolic processes, actin-based process, and immune response. Significant molecular communications were observed between the clusters, most prominently among those involved in ECM and cytoskeleton organizations, inflammation, and cell cycle. Machine Learning Disease pathway analysis revealed that vascular injury-induced crosstalk between ECM remodeling and immune response clusters contributed to aortic aneurysm, neovascularization of choroid, and kidney failure. Additionally, we found that interactions between ECM and actin cytoskeletal reorganization clusters were linked to cardiac damage, carotid artery occlusion, and cardiac lesions. Overall, through multi-scale bioinformatic analyses, we demonstrated the robustness of the vascular injury model in eliciting transcriptomic and molecular network changes associated with CVDs, highlighting its potential for use in cardiovascular research.
{"title":"Transcriptomic and Multi-Scale Network Analyses Reveal Key Drivers of Cardiovascular Disease","authors":"Bat-Ider Tumenbayar;Khanh Pham;John C. Biber;Rhonda Drewes;Yongho Bae","doi":"10.1109/TMBMC.2024.3501576","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3501576","url":null,"abstract":"Cardiovascular diseases (CVDs) and pathologies are often driven by changes in molecular signaling and communication, as well as in cellular and tissue components, particularly those involving the extracellular matrix (ECM), cytoskeleton, and immune response. The fine-wire vascular injury model is commonly used to study neointimal hyperplasia and vessel stiffening, but it is not typically considered a model for CVDs. However, applying this model to study CVDs in conjunction with established processes could offer valuable insights. In this paper, we hypothesize that vascular injury induces changes in gene expression, molecular communication, and biological processes similar to those observed in CVDs at both the transcriptome and protein levels. To investigate this, we analyzed gene expression in microarray datasets from injured and uninjured femoral arteries in mice two weeks post-injury, identifying 1,467 significantly and differentially expressed genes involved in several CVDs such as including vaso-occlusion, arrhythmia, and atherosclerosis. We further constructed a protein-protein interaction network with seven functionally distinct clusters, with notable enrichment in ECM, metabolic processes, actin-based process, and immune response. Significant molecular communications were observed between the clusters, most prominently among those involved in ECM and cytoskeleton organizations, inflammation, and cell cycle. Machine Learning Disease pathway analysis revealed that vascular injury-induced crosstalk between ECM remodeling and immune response clusters contributed to aortic aneurysm, neovascularization of choroid, and kidney failure. Additionally, we found that interactions between ECM and actin cytoskeletal reorganization clusters were linked to cardiac damage, carotid artery occlusion, and cardiac lesions. Overall, through multi-scale bioinformatic analyses, we demonstrated the robustness of the vascular injury model in eliciting transcriptomic and molecular network changes associated with CVDs, highlighting its potential for use in cardiovascular research.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"78-90"},"PeriodicalIF":2.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637817","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 : 2024-11-11DOI: 10.1109/TMBMC.2024.3496000
Nihit Bhatnagar;Sandeep Joshi
In this letter, we study a three-dimensional heterogeneous media-based mobile molecular communication (MC) system, with the communicating devices as point transmitters and passive spherical-shaped receiver nano-machines. For the shorter time range, the diffusion process faces internal diffusivity fluctuations, due to which communicating devices and the information-carrying molecule’s diffusivity exhibit stochastic behavior. We propose a stochastic diffusivity-based mobile MC system model, which considers the non-Gaussian Brownian displacement of molecules and characterize it by the channel impulse response, and derive its mean. We consider the molecule’s constrained time-varying Poisson statistical diffusive channel model at a high inter-symbol interference regime and analyze the channel performance in terms of the bit error rate and channel capacity. Furthermore, the numerical results are verified through particle-based simulations.
{"title":"Characterization and Performance Optimization of Heterogeneous Media-Based Mobile Molecular Communication Systems","authors":"Nihit Bhatnagar;Sandeep Joshi","doi":"10.1109/TMBMC.2024.3496000","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3496000","url":null,"abstract":"In this letter, we study a three-dimensional heterogeneous media-based mobile molecular communication (MC) system, with the communicating devices as point transmitters and passive spherical-shaped receiver nano-machines. For the shorter time range, the diffusion process faces internal diffusivity fluctuations, due to which communicating devices and the information-carrying molecule’s diffusivity exhibit stochastic behavior. We propose a stochastic diffusivity-based mobile MC system model, which considers the non-Gaussian Brownian displacement of molecules and characterize it by the channel impulse response, and derive its mean. We consider the molecule’s constrained time-varying Poisson statistical diffusive channel model at a high inter-symbol interference regime and analyze the channel performance in terms of the bit error rate and channel capacity. Furthermore, the numerical results are verified through particle-based simulations.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"128-133"},"PeriodicalIF":2.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637889","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}
In the context of the Internet of Bio-Nano Things (IoBNT), nano-devices are envisioned to perform complex tasks collaboratively, i.e., by communicating with each other. One candidate for the implementation of such devices are engineered cells due to their inherent biocompatibility. However, because each engineered cell has only little computational capabilities, transmitter and receiver (RX) functionalities can afford only limited complexity. In this paper, we propose a simple, yet modular, architecture for a cellular RX that is capable of processing a stream of observed symbols using chemical reaction networks. Furthermore, we propose two specific detector implementations for the RX. The first detector is based on a machine learning model that is trained offline, i.e., before the cellular RX is deployed. The second detector utilizes pilot symbol-based training and is therefore able to continuously adapt to changing channel conditions online, i.e., after deployment. To coordinate the different chemical processing steps involved in symbol detection, the proposed cellular RX leverages an internal chemical timer. Furthermore, the RX is synchronized with the transmitter via external, i.e., extracellular, signals. Finally, the proposed architecture is validated using theoretical analysis and stochastic simulations. The presented results confirm the feasibility of both proposed implementations and reveal that the proposed online learning-based RX is able to perform reliable detection even in initially unknown or slowly changing channels. By its modular design and exclusively chemical implementation, the proposed RX contributes towards the realization of versatile and biocompatible nano-scale communication networks for IoBNT applications narrowing the existing implementation gap in cellular molecular communication (MC).
{"title":"Closing the Implementation Gap in MC: Fully Chemical Synchronization and Detection for Cellular Receivers","authors":"Bastian Heinlein;Lukas Brand;Malcolm Egan;Maximilian Schäfer;Robert Schober;Sebastian Lotter","doi":"10.1109/TMBMC.2024.3486190","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3486190","url":null,"abstract":"In the context of the Internet of Bio-Nano Things (IoBNT), nano-devices are envisioned to perform complex tasks collaboratively, i.e., by communicating with each other. One candidate for the implementation of such devices are engineered cells due to their inherent biocompatibility. However, because each engineered cell has only little computational capabilities, transmitter and receiver (RX) functionalities can afford only limited complexity. In this paper, we propose a simple, yet modular, architecture for a cellular RX that is capable of processing a stream of observed symbols using chemical reaction networks. Furthermore, we propose two specific detector implementations for the RX. The first detector is based on a machine learning model that is trained offline, i.e., before the cellular RX is deployed. The second detector utilizes pilot symbol-based training and is therefore able to continuously adapt to changing channel conditions online, i.e., after deployment. To coordinate the different chemical processing steps involved in symbol detection, the proposed cellular RX leverages an internal chemical timer. Furthermore, the RX is synchronized with the transmitter via external, i.e., extracellular, signals. Finally, the proposed architecture is validated using theoretical analysis and stochastic simulations. The presented results confirm the feasibility of both proposed implementations and reveal that the proposed online learning-based RX is able to perform reliable detection even in initially unknown or slowly changing channels. By its modular design and exclusively chemical implementation, the proposed RX contributes towards the realization of versatile and biocompatible nano-scale communication networks for IoBNT applications narrowing the existing implementation gap in cellular molecular communication (MC).","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"30-50"},"PeriodicalIF":2.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637888","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 : 2024-10-21DOI: 10.1109/TMBMC.2024.3476197
Melih Şahin;Beyza Ezgi Ortlek;Ozgur B. Akan
Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman coding. We first show that while MC-adapted Huffman coding improves symbol error rate (SER), it does not always produce an optimal prefix codebook in terms of coding length and power. To address this, we propose optimal molecular prefix coding (MoPC). The major result of this paper is the Molecular Arithmetic Coding (MoAC), which we derive based on an existing general construction principle for constrained arithmetic channel coding, equipping it with error correction and data compression capabilities for any finite source alphabet. We theoretically and practically show the superiority of MoAC to SAC, our another adaptation of arithmetic source coding to MC. However, MoAC’s unique decodability is limited by bit precision. Accordingly, a uniquely-decodable new coding scheme named Molecular Arithmetic with Prefix Coding (MoAPC) is introduced. On two nucleotide alphabets, we show that MoAPC has a better compression performance than optimized MoPC. MC simulation results demonstrate the effectiveness of the proposed methods.
{"title":"Molecular Arithmetic Coding (MoAC) and Optimized Molecular Prefix Coding (MoPC) for Diffusion-Based Molecular Communication","authors":"Melih Şahin;Beyza Ezgi Ortlek;Ozgur B. Akan","doi":"10.1109/TMBMC.2024.3476197","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3476197","url":null,"abstract":"Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman coding. We first show that while MC-adapted Huffman coding improves symbol error rate (SER), it does not always produce an optimal prefix codebook in terms of coding length and power. To address this, we propose optimal molecular prefix coding (MoPC). The major result of this paper is the Molecular Arithmetic Coding (MoAC), which we derive based on an existing general construction principle for constrained arithmetic channel coding, equipping it with error correction and data compression capabilities for any finite source alphabet. We theoretically and practically show the superiority of MoAC to SAC, our another adaptation of arithmetic source coding to MC. However, MoAC’s unique decodability is limited by bit precision. Accordingly, a uniquely-decodable new coding scheme named Molecular Arithmetic with Prefix Coding (MoAPC) is introduced. On two nucleotide alphabets, we show that MoAPC has a better compression performance than optimized MoPC. MC simulation results demonstrate the effectiveness of the proposed methods.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"66-77"},"PeriodicalIF":2.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637891","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 : 2024-10-08DOI: 10.1109/TMBMC.2024.3476192
Nabiul Islam;Saswati Pal;Sudip Misra
Bacterial networks-based novel healthcare applications integrated with the Internet of Bio-Nano Things (IoBNT) have been on the rise, particularly due to their high efficacy in delivering drugs at targeted sites. Nevertheless, these networks are vulnerable to various cyber security risks such as unauthorized access, data tampering, and malicious attacks from internal and external intruders. By leveraging the property of quantum entanglement, we propose a security protocol, QBaN, to detect and thwart security breaches posed by intruders and securely send the information to the intended receiver. We use the von Neumann entropy metric to detect changes in the entangled quantum states. We evaluate the QBaN’s capability of detecting eavesdropping events by varying threshold values. Simulation results demonstrate the protocol’s efficacy in intrusion detection with an AUC of 0.78 on the ROC curve. The energy consumption for quantum entanglement is approximately 66.82% and 98.86% less than that for the bacterial propagation and DNA replication, respectively.
{"title":"QBaN: Quantum Bacterial Nanonetworks for Secure Molecular Communication","authors":"Nabiul Islam;Saswati Pal;Sudip Misra","doi":"10.1109/TMBMC.2024.3476192","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3476192","url":null,"abstract":"Bacterial networks-based novel healthcare applications integrated with the Internet of Bio-Nano Things (IoBNT) have been on the rise, particularly due to their high efficacy in delivering drugs at targeted sites. Nevertheless, these networks are vulnerable to various cyber security risks such as unauthorized access, data tampering, and malicious attacks from internal and external intruders. By leveraging the property of quantum entanglement, we propose a security protocol, QBaN, to detect and thwart security breaches posed by intruders and securely send the information to the intended receiver. We use the von Neumann entropy metric to detect changes in the entangled quantum states. We evaluate the QBaN’s capability of detecting eavesdropping events by varying threshold values. Simulation results demonstrate the protocol’s efficacy in intrusion detection with an AUC of 0.78 on the ROC curve. The energy consumption for quantum entanglement is approximately 66.82% and 98.86% less than that for the bacterial propagation and DNA replication, respectively.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 4","pages":"633-641"},"PeriodicalIF":2.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844539","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}
The discovery that tumor cells discharge vast quantities of extracellular vesicles (EVs) that contain functional molecules which promote immune modulation and drug resistance, urges the need for novel therapeutic interventions. Here we take an approach based on the EV-release-modulation strategy to treat tumors, suppress their spread, and monitor the therapy efficacy. We propose a molecular communication (MC)-based system model to implement the oncogenic EV release modulation and monitor the EV spatiotemporal biodistribution. The proposed system uses drugs which target the tumor cell pH regulatory biochemical mechanisms. We develop a comprehensive computational framework where we integrate adapted and extended versions of the biophysical model of tumor cell pH regulation, the tumor cell proliferation model, and our previously developed MC model of pHe-dependent EV biodistribution. We fix specific parameter values of the system model by combining available experimental data performed in diverse tumor cell systems. Using the developed system, we analyse the dynamics of intracellular pH (pHi), extracellular pH (pHe), tumor cell growth pattern, and EV release and biodistribution. Our proposed system and computational framework can be used as a tool to track the oncogenic EV biodistribution, which can be used as a biomarker to monitor the tumor and optimize anticancer therapy.
{"title":"Modulating Tumor Cell Extracellular Vesicle Signaling for Therapeutic Intervention and Monitoring","authors":"Milica Lekić;Mladen Veletić;Martin Damrath;Mohammad Zoofaghari;Ilangko Balasingham","doi":"10.1109/TMBMC.2024.3473694","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3473694","url":null,"abstract":"The discovery that tumor cells discharge vast quantities of extracellular vesicles (EVs) that contain functional molecules which promote immune modulation and drug resistance, urges the need for novel therapeutic interventions. Here we take an approach based on the EV-release-modulation strategy to treat tumors, suppress their spread, and monitor the therapy efficacy. We propose a molecular communication (MC)-based system model to implement the oncogenic EV release modulation and monitor the EV spatiotemporal biodistribution. The proposed system uses drugs which target the tumor cell pH regulatory biochemical mechanisms. We develop a comprehensive computational framework where we integrate adapted and extended versions of the biophysical model of tumor cell pH regulation, the tumor cell proliferation model, and our previously developed MC model of pHe-dependent EV biodistribution. We fix specific parameter values of the system model by combining available experimental data performed in diverse tumor cell systems. Using the developed system, we analyse the dynamics of intracellular pH (pHi), extracellular pH (pHe), tumor cell growth pattern, and EV release and biodistribution. Our proposed system and computational framework can be used as a tool to track the oncogenic EV biodistribution, which can be used as a biomarker to monitor the tumor and optimize anticancer therapy.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"51-65"},"PeriodicalIF":2.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637892","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 : 2024-09-30DOI: 10.1109/TMBMC.2024.3471189
Phuong-Nam Nguyen
Quantum biology, at the intersection of quantum mechanics and biology, investigates the involvement of quantum phenomena in biological processes. A pivotal focus is quantum tunneling, wherein particles traverse energy barriers, a phenomenon with potential significance in various biological contexts. This article introduces a new class of linear potential functions for studying quantum tunneling in biological processes. The simplicity of linear potentials enables analytical solutions to the Schrödinger equation, offering efficiency compared to more complex numerical methods. The proposed linear potential functions are derived using parabolic curves, providing an analytical form with physical interpretations. The corresponding energy function and transmission coefficients are presented, facilitating a simplified understanding of tunneling behavior. Theoretical implications of the proposed model are discussed, emphasizing the ease of parameter variation and its applicability to diverse biological scenarios. In the numerical demonstration, two case studies are presented: (1) examining proton tunneling in DNA point mutations and (2) exploring electron tunneling in biological receptors, specifically the ACE2 receptor in the context of SARS-CoV-2.
量子生物学是量子力学与生物学的交叉学科，研究生物过程中的量子现象。量子隧穿是其中一个关键重点，粒子在隧穿过程中会穿越能量壁垒，这种现象在各种生物环境中都具有潜在意义。本文介绍了一类新的线性势函数，用于研究生物过程中的量子隧穿。线性势函数的简单性使得薛定谔方程的解析解成为可能，与更复杂的数值方法相比，它具有更高的效率。所提出的线性势函数是利用抛物线曲线推导出来的，提供了一种具有物理解释的分析形式。提出了相应的能量函数和传输系数，有助于简化对隧道行为的理解。讨论了所提模型的理论意义，强调了参数变化的简易性及其对不同生物场景的适用性。在数值演示中，介绍了两个案例研究：(1) 研究 DNA 点突变中的质子隧道效应；(2) 探索生物受体中的电子隧道效应，特别是 SARS-CoV-2 背景下的 ACE2 受体。
{"title":"Quantum Tunneling With Linear Potential: Case Studies in Biological Processes","authors":"Phuong-Nam Nguyen","doi":"10.1109/TMBMC.2024.3471189","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3471189","url":null,"abstract":"Quantum biology, at the intersection of quantum mechanics and biology, investigates the involvement of quantum phenomena in biological processes. A pivotal focus is quantum tunneling, wherein particles traverse energy barriers, a phenomenon with potential significance in various biological contexts. This article introduces a new class of linear potential functions for studying quantum tunneling in biological processes. The simplicity of linear potentials enables analytical solutions to the Schrödinger equation, offering efficiency compared to more complex numerical methods. The proposed linear potential functions are derived using parabolic curves, providing an analytical form with physical interpretations. The corresponding energy function and transmission coefficients are presented, facilitating a simplified understanding of tunneling behavior. Theoretical implications of the proposed model are discussed, emphasizing the ease of parameter variation and its applicability to diverse biological scenarios. In the numerical demonstration, two case studies are presented: (1) examining proton tunneling in DNA point mutations and (2) exploring electron tunneling in biological receptors, specifically the ACE2 receptor in the context of SARS-CoV-2.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 4","pages":"623-632"},"PeriodicalIF":2.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844595","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 : 2024-09-25DOI: 10.1109/TMBMC.2024.3458329
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TMBMC.2024.3458329","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3458329","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 3","pages":"C3-C3"},"PeriodicalIF":2.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320485","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 : 2024-09-25DOI: 10.1109/TMBMC.2024.3463128
Adam Noel;Andrew W. Eckford;Radek Erban;Matteo Icardi;Gregory Reeves
{"title":"Guest Editorial Special Feature on Seeing Through the Crowd: Molecular Communication in Crowded and Multi-Cellular Environments","authors":"Adam Noel;Andrew W. Eckford;Radek Erban;Matteo Icardi;Gregory Reeves","doi":"10.1109/TMBMC.2024.3463128","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3463128","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 3","pages":"422-424"},"PeriodicalIF":2.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694727","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320457","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 : 2024-09-25DOI: 10.1109/TMBMC.2024.3458670
Chun Tung Chou;Mohammad Zoofaghari;Ozgur B. Akan;Mladen Veletic;Ilangko Balasingham
{"title":"Guest Editorial Introduction to the Special Feature on the 8th Workshop on Molecular Communications","authors":"Chun Tung Chou;Mohammad Zoofaghari;Ozgur B. Akan;Mladen Veletic;Ilangko Balasingham","doi":"10.1109/TMBMC.2024.3458670","DOIUrl":"https://doi.org/10.1109/TMBMC.2024.3458670","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 3","pages":"455-457"},"PeriodicalIF":2.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320456","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}
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