Pub Date : 2025-12-17DOI: 10.1109/TMBMC.2025.3645247
T. Sai Krishna Charitha;Lokendra Chouhan;Abhishek K. Gupta;Rik Dey;Eswar Kadali;Sairam Mente;Rituraj;Prabhat K. Sharma
In this work, we develop the channel model of a molecular communication (MC) system with molecules propagating via anomalous diffusion in a confined environment, in particular, inside a spherical region. The MC system consists of an absorbing receiver located at the center of the region and a point transmitter, whereas the outer boundary is fully reflecting. We first obtain the concentration profile of molecules inside the region at a given time. Further, we derive the hitting rate and hitting probability to characterize the channel.
{"title":"Molecular Communication in Bounded Spherical Region With Anomalous Diffusion Phenomenon","authors":"T. Sai Krishna Charitha;Lokendra Chouhan;Abhishek K. Gupta;Rik Dey;Eswar Kadali;Sairam Mente;Rituraj;Prabhat K. Sharma","doi":"10.1109/TMBMC.2025.3645247","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3645247","url":null,"abstract":"In this work, we develop the channel model of a molecular communication (MC) system with molecules propagating via anomalous diffusion in a confined environment, in particular, inside a spherical region. The MC system consists of an absorbing receiver located at the center of the region and a point transmitter, whereas the outer boundary is fully reflecting. We first obtain the concentration profile of molecules inside the region at a given time. Further, we derive the hitting rate and hitting probability to characterize the channel.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"105-110"},"PeriodicalIF":2.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886657","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}
{"title":"Guest Editorial Introduction to the Special Feature on the 9th Workshop on Molecular Communications","authors":"Jens Kirchner;Bhuvana Krishnaswamy;Lin Lin;Laura Galluccio;Nunzio Tuccitto","doi":"10.1109/TMBMC.2025.3637924","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3637924","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"482-485"},"PeriodicalIF":2.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778251","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 : 2025-12-16DOI: 10.1109/TMBMC.2025.3637917
{"title":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Publication Information","authors":"","doi":"10.1109/TMBMC.2025.3637917","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3637917","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"C2-C2"},"PeriodicalIF":2.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760888","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 : 2025-11-28DOI: 10.1109/TMBMC.2025.3638668
Nithin V. Sabu;Kaushlendra Kumar Pandey;Abhishek K. Gupta;S. M. Sameer
This work focuses on the development of an analytical framework to study a diffusion-assisted molecular communication-based network of nano-machines (NMs) with a clustered initial deployment to detect a target in a three-dimensional (3D) medium. Leveraging the Poisson cluster process to model the initial locations of clustered NMs, we derive the analytical expression for the target detection probability with respect to time along with relevant bounds. We also investigate a single-cluster scenario. All the derived expressions are validated through extensive particle-based simulations. Furthermore, we analyze the impact of key parameters, such as the mean number of NMs per cluster, the density of the cluster, and the spatial spread, on the detection performance. Our results show that detection probability is greatly influenced by clustering, and different spatial arrangements produce varying performances. The results offer a better understanding of how molecular communication systems should be designed for optimal target detection in nanoscale and biological environments.
{"title":"Target Detection in Clustered Mobile Nanomachine Networks","authors":"Nithin V. Sabu;Kaushlendra Kumar Pandey;Abhishek K. Gupta;S. M. Sameer","doi":"10.1109/TMBMC.2025.3638668","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3638668","url":null,"abstract":"This work focuses on the development of an analytical framework to study a diffusion-assisted molecular communication-based network of nano-machines (NMs) with a clustered initial deployment to detect a target in a three-dimensional (3D) medium. Leveraging the Poisson cluster process to model the initial locations of clustered NMs, we derive the analytical expression for the target detection probability with respect to time along with relevant bounds. We also investigate a single-cluster scenario. All the derived expressions are validated through extensive particle-based simulations. Furthermore, we analyze the impact of key parameters, such as the mean number of NMs per cluster, the density of the cluster, and the spatial spread, on the detection performance. Our results show that detection probability is greatly influenced by clustering, and different spatial arrangements produce varying performances. The results offer a better understanding of how molecular communication systems should be designed for optimal target detection in nanoscale and biological environments.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"92-104"},"PeriodicalIF":2.3,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861231","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 : 2025-11-17DOI: 10.1109/TMBMC.2025.3633450
Arash Tirandaz;Vahid Salari
A quantum threat to the fidelity of information transfer in transcription of DNA molecule, is the fluctuation of protons on the base pairs. Point mutations can arise when a hydrogen-bonded proton tunnels between donor and acceptor sites in DNA, transiently creating tautomeric forms that mispair during replication. Despite the elastic way of proton tunneling, we analyze inelastic proton tunneling accompanied by energy exchange with local vibrational/electronic modes and the environment. Within an open-quantum-system framework, we derive Born–Markov master equations for a two-state (double-well) Hamiltonian parameterized by hydrogen-bond geometry and environmental spectral properties. Quantum-chemical parameters are estimated by DFT (B3LYP/6311G, Gaussian09) with water as solvent. We examine temperature dependence and kinetic isotope effects (H/D). It is shown that inelastic tunneling can extend tautomer lifetimes and enhance mispairing probabilities. These results provide a quantitative route to connect microscopic proton dynamics biologically relevant mutation pathways.
{"title":"The Role of Inelastic Proton Tunneling in Generating Point Mutations and Genetic Diversity in DNA","authors":"Arash Tirandaz;Vahid Salari","doi":"10.1109/TMBMC.2025.3633450","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3633450","url":null,"abstract":"A quantum threat to the fidelity of information transfer in transcription of DNA molecule, is the fluctuation of protons on the base pairs. Point mutations can arise when a hydrogen-bonded proton tunnels between donor and acceptor sites in DNA, transiently creating tautomeric forms that mispair during replication. Despite the elastic way of proton tunneling, we analyze inelastic proton tunneling accompanied by energy exchange with local vibrational/electronic modes and the environment. Within an open-quantum-system framework, we derive Born–Markov master equations for a two-state (double-well) Hamiltonian parameterized by hydrogen-bond geometry and environmental spectral properties. Quantum-chemical parameters are estimated by DFT (B3LYP/6311G, Gaussian09) with water as solvent. We examine temperature dependence and kinetic isotope effects (H/D). It is shown that inelastic tunneling can extend tautomer lifetimes and enhance mispairing probabilities. These results provide a quantitative route to connect microscopic proton dynamics biologically relevant mutation pathways.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"323-337"},"PeriodicalIF":2.3,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175757","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 : 2025-10-31DOI: 10.1109/TMBMC.2025.3627871
Hoai-Nhan Tran;Nguyen-Phuc-Xuan Quynh;Haochen Zhao;Jianxin Wang
Recently, a range of effective methods have been developed for predicting protein-protein interactions (PPIs). Among them, the methods based on data derived from protein sequences and structures have shown promising results. However, most existing structure-based methods consider only whole-protein structural information, potentially reducing predictive performance by overlooking local regions. To overcome this limitation, we propose a protein representation method that is capable of modeling local regions of protein structure. The local structural regions are then processed by multiple graph neural networks to identify local interaction regions. At the same time, for our PPI prediction model, we incorporate a lightweight neural network to leverage feature-rich sequence embeddings derived from protein language models to further improve the PPI prediction performance. We compare the performance of our methods with that of robust existing sequence-based and structure-based methods. The results show that our methods outperform these methods across all metrics on the Yeast core dataset and most metrics on other benchmark datasets obtained from various organisms. The source code of our method is available at https://gitlab.com/nhanth/MMProtRepPPI.
{"title":"MMProtRepPPI: A Multimodal Protein Representation for Predicting Protein–Protein Interactions Using Sequence and Structure","authors":"Hoai-Nhan Tran;Nguyen-Phuc-Xuan Quynh;Haochen Zhao;Jianxin Wang","doi":"10.1109/TMBMC.2025.3627871","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3627871","url":null,"abstract":"Recently, a range of effective methods have been developed for predicting protein-protein interactions (PPIs). Among them, the methods based on data derived from protein sequences and structures have shown promising results. However, most existing structure-based methods consider only whole-protein structural information, potentially reducing predictive performance by overlooking local regions. To overcome this limitation, we propose a protein representation method that is capable of modeling local regions of protein structure. The local structural regions are then processed by multiple graph neural networks to identify local interaction regions. At the same time, for our PPI prediction model, we incorporate a lightweight neural network to leverage feature-rich sequence embeddings derived from protein language models to further improve the PPI prediction performance. We compare the performance of our methods with that of robust existing sequence-based and structure-based methods. The results show that our methods outperform these methods across all metrics on the Yeast core dataset and most metrics on other benchmark datasets obtained from various organisms. The source code of our method is available at <uri>https://gitlab.com/nhanth/MMProtRepPPI</uri>.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"126-135"},"PeriodicalIF":2.3,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929505","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 : 2025-10-30DOI: 10.1109/TMBMC.2025.3626748
Mehdi Hosseinali Zadeh;Musaab Saeed;Mehdi Maleki;Hamid Reza Bahrami
Inter-symbol interference (ISI) is a fundamental source of performance degradation in diffusive molecular communication channels. Comprehensive study and modeling of the ISI phenomenon is crucial in better understanding the behavior and performance of molecular communication systems. In this work, a novel, realistic ISI model is proposed, where the probability distribution of the ISI in a three-dimensional fluid environment with a spherical receiver is derived. A reversible reaction mechanism is considered to model the reception process, which involves the reactions of the information molecules to activate the receptors on the receiver surface, and to reverse back to the environment. The ISI distribution is derived, and is shown to converge to a Gaussian model for very short transmission intervals. Numerical simulations, based on the proposed model, show the impact of the time-slot duration, and the reverse reaction rate on the bit error rate performance.
{"title":"An Inter-Symbol Interference Model for Diffusion-Based Molecular Communication With Reversible Absorption Receiver","authors":"Mehdi Hosseinali Zadeh;Musaab Saeed;Mehdi Maleki;Hamid Reza Bahrami","doi":"10.1109/TMBMC.2025.3626748","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3626748","url":null,"abstract":"Inter-symbol interference (ISI) is a fundamental source of performance degradation in diffusive molecular communication channels. Comprehensive study and modeling of the ISI phenomenon is crucial in better understanding the behavior and performance of molecular communication systems. In this work, a novel, realistic ISI model is proposed, where the probability distribution of the ISI in a three-dimensional fluid environment with a spherical receiver is derived. A reversible reaction mechanism is considered to model the reception process, which involves the reactions of the information molecules to activate the receptors on the receiver surface, and to reverse back to the environment. The ISI distribution is derived, and is shown to converge to a Gaussian model for very short transmission intervals. Numerical simulations, based on the proposed model, show the impact of the time-slot duration, and the reverse reaction rate on the bit error rate performance.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"79-91"},"PeriodicalIF":2.3,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859868","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 : 2025-10-30DOI: 10.1109/TMBMC.2025.3626753
Sai Sowkhya Dasari;Lokendra Chouhan;Bodhibratha Mukhopadhyay;Prabhat K. Sharma;Anamika Singh;Mohamed-Slim Alouini
Localization of source in molecular communication (MC) mimics the finding of cancer cells within the organisms. In this study, our primary aim is to implement a cooperative localization (CL) scheme to pinpoint the positions of multiple transmitters (Txs) within an anomalous diffusion (AD)-based MC (AD-MC) system. We employ maximum likelihood estimation (ML) to formulate the Txs’ locations while also factoring in the practical considerations of inter-symbol interference (ISI) and Brownian noise. Further, we analyze the performance of proposed localization scheme through root mean square error (RMSE) for multiple Tx localization schemes. The coordinates of the Txs are determined by solving the ML using the multi-variable Newton-Raphson method. Furthermore, Monte Carlo simulations are used to evaluate the influence of fluctuating noise levels on the system. Aggregated results from simulations conducted across varied noise values are used for analysis. Our simulations demonstrate the superior performance of our proposed method compared to the existing least squares method employed in diffusion-based MC systems.
{"title":"Localization of Nanomachines in Anomalous Diffusion-Based Molecular Communication","authors":"Sai Sowkhya Dasari;Lokendra Chouhan;Bodhibratha Mukhopadhyay;Prabhat K. Sharma;Anamika Singh;Mohamed-Slim Alouini","doi":"10.1109/TMBMC.2025.3626753","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3626753","url":null,"abstract":"Localization of source in molecular communication (MC) mimics the finding of cancer cells within the organisms. In this study, our primary aim is to implement a cooperative localization (CL) scheme to pinpoint the positions of multiple transmitters (Txs) within an anomalous diffusion (AD)-based MC (AD-MC) system. We employ maximum likelihood estimation (ML) to formulate the Txs’ locations while also factoring in the practical considerations of inter-symbol interference (ISI) and Brownian noise. Further, we analyze the performance of proposed localization scheme through root mean square error (RMSE) for multiple Tx localization schemes. The coordinates of the Txs are determined by solving the ML using the multi-variable Newton-Raphson method. Furthermore, Monte Carlo simulations are used to evaluate the influence of fluctuating noise levels on the system. Aggregated results from simulations conducted across varied noise values are used for analysis. Our simulations demonstrate the superior performance of our proposed method compared to the existing least squares method employed in diffusion-based MC systems.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"42-46"},"PeriodicalIF":2.3,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861222","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 : 2025-10-27DOI: 10.1109/TMBMC.2025.3626218
Ahmet Burak Kilic;Ozgur B. Akan
Molecular Communication (MC) is a fundamental communication paradigm observed in nature. A notable subtype, Odor-based Molecular Communication (OMC), offers promising potential and a wide range of applications. In this study, we investigate OMC between plants in the context of stress communication, focusing on how plants emit Biological Volatile Organic Compounds (BVOCs) to convey information about experienced stress to neighboring plants. We present an end-to-end mathematical model that captures the physical and biological processes involved in plant-to-plant stress signaling. To the best of our knowledge, this is the first study to model stress communication in plants from transmission to reception. The system is analyzed numerically under various scenarios using MATLAB. Using experimental data from the literature, we show that BVOC emissions under different stress conditions can be approximated through a continuous gene regulation model. This model is applied to multiple stressors and plant species to simulate emission dynamics accurately. Additionally, we examine a modulation strategy observed in plants, known as Ratio Shift Keying, which enables the encoding of information in the relative concentrations of different BVOCs. This method limits the ability of competing plants to extract the transmitted information.
{"title":"End-to-End Mathematical Modeling of Stress Communication Between Plants","authors":"Ahmet Burak Kilic;Ozgur B. Akan","doi":"10.1109/TMBMC.2025.3626218","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3626218","url":null,"abstract":"Molecular Communication (MC) is a fundamental communication paradigm observed in nature. A notable subtype, Odor-based Molecular Communication (OMC), offers promising potential and a wide range of applications. In this study, we investigate OMC between plants in the context of stress communication, focusing on how plants emit Biological Volatile Organic Compounds (BVOCs) to convey information about experienced stress to neighboring plants. We present an end-to-end mathematical model that captures the physical and biological processes involved in plant-to-plant stress signaling. To the best of our knowledge, this is the first study to model stress communication in plants from transmission to reception. The system is analyzed numerically under various scenarios using MATLAB. Using experimental data from the literature, we show that BVOC emissions under different stress conditions can be approximated through a continuous gene regulation model. This model is applied to multiple stressors and plant species to simulate emission dynamics accurately. Additionally, we examine a modulation strategy observed in plants, known as Ratio Shift Keying, which enables the encoding of information in the relative concentrations of different BVOCs. This method limits the ability of competing plants to extract the transmitted information.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"69-78"},"PeriodicalIF":2.3,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861228","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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