Pub Date : 2025-09-03DOI: 10.1109/TMBMC.2025.3605779
Huiyu Luo;Kun Deng;Zhe Yuan;Ying Qin;Junfang Zhang;Saied M. Abd El-Atty;Lin Lin
Advances in molecular communication and the Internet of Nanothings (IoNT) have opened new possibilities for in-body nanodevice networks in medicine. As a promising approach for transmitting information from IoNT to external devices, neural communication leverages the nervous system as a data transmission interface. However, reliable transmission depends on accurate channel parameter estimation and research in this area remains limited. To address this, we take pH as the primary channel parameter and experimentally propose channel parameter estimation schemes using the bullfrog sciatic nerve as the neural communication channel. Here, compound action potentials (CAPs), which are the electrical responses of the nerve channel, are employed to characterize the channel. To establish the relationship between CAPs and pH values, we develop an experimental platform to collect CAPs at different pH values. Then, we design regression models including the random forest (RF) and long short-term memory (LSTM) methods, and further propose an enhanced LSTM model to address their limitations. The enhanced model uses convolutional layers to extract local spatial features from raw CAPs waveforms, followed by LSTM layers for temporal modeling, and concludes with MLP layers for continuous pH prediction. Experimental results reveal that the corresponding pH value can be accurately detected when new CAPs are fed into the trained models, with the enhanced LSTM model demonstrating superior accuracy. This study paves the way for conducting experimental research to ensure reliable data transmission from in-vivo IoNT to external networks.
{"title":"Channel Parameter Estimation in Neural Communication Based on Bullfrog Sciatic Nerve","authors":"Huiyu Luo;Kun Deng;Zhe Yuan;Ying Qin;Junfang Zhang;Saied M. Abd El-Atty;Lin Lin","doi":"10.1109/TMBMC.2025.3605779","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3605779","url":null,"abstract":"Advances in molecular communication and the Internet of Nanothings (IoNT) have opened new possibilities for in-body nanodevice networks in medicine. As a promising approach for transmitting information from IoNT to external devices, neural communication leverages the nervous system as a data transmission interface. However, reliable transmission depends on accurate channel parameter estimation and research in this area remains limited. To address this, we take pH as the primary channel parameter and experimentally propose channel parameter estimation schemes using the bullfrog sciatic nerve as the neural communication channel. Here, compound action potentials (CAPs), which are the electrical responses of the nerve channel, are employed to characterize the channel. To establish the relationship between CAPs and pH values, we develop an experimental platform to collect CAPs at different pH values. Then, we design regression models including the random forest (RF) and long short-term memory (LSTM) methods, and further propose an enhanced LSTM model to address their limitations. The enhanced model uses convolutional layers to extract local spatial features from raw CAPs waveforms, followed by LSTM layers for temporal modeling, and concludes with MLP layers for continuous pH prediction. Experimental results reveal that the corresponding pH value can be accurately detected when new CAPs are fed into the trained models, with the enhanced LSTM model demonstrating superior accuracy. This study paves the way for conducting experimental research to ensure reliable data transmission from in-vivo IoNT to external networks.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"185-196"},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929460","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 : 2025-09-03DOI: 10.1109/TMBMC.2025.3605778
Yuankun Tang;Qianqian Wang;Zhanjun Hao;Zhongyu Ma;Weidong Gao;Lie-Liang Yang
The Internet of Bio-Nano Things (IoBNT) is expected to play a pivotal role in the future healthcare systems. This paper proposes a molecular code index modulation (MCIM) scheme to achieve reliable information transmission for molecular communication (MC), which may find applications in micro/nano scale networks, including the IoBNT. The MCIM system, making use of two distinct types of molecules, encodes information into the indices of both spreading codes and molecular types. At the receiver, the concentration differences between the molecules of two types with respect to all chips of a code are exploited for information detection. Correspondingly, two low-complexity detectors dispensing with channel state information, namely joint detector and stepwise detector, are proposed based on the correlation detection principles. Furthermore, to mitigate the inter-symbol interference (ISI) caused by the MC channel and specifically structured spreading code, an enhanced stepwise detector is developed to improve the performance of the stepwise detector. The bit error rate (BER) upper bound of the MCIM systems with joint detection, as well as the throughput and the computational complexity of MCIM systems with any detection schemes are analyzed. Our studies demonstrate that the proposed MCIM scheme has the potential to achieve the superior BER and throughput performance at low computational complexity, when compared with the existing modulation schemes employing two molecular types, such as molecular shift keying and molecular type permutation shift keying schemes.
{"title":"Molecular Code Index Modulation: Signaling, Detection, and Performance Analysis","authors":"Yuankun Tang;Qianqian Wang;Zhanjun Hao;Zhongyu Ma;Weidong Gao;Lie-Liang Yang","doi":"10.1109/TMBMC.2025.3605778","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3605778","url":null,"abstract":"The Internet of Bio-Nano Things (IoBNT) is expected to play a pivotal role in the future healthcare systems. This paper proposes a molecular code index modulation (MCIM) scheme to achieve reliable information transmission for molecular communication (MC), which may find applications in micro/nano scale networks, including the IoBNT. The MCIM system, making use of two distinct types of molecules, encodes information into the indices of both spreading codes and molecular types. At the receiver, the concentration differences between the molecules of two types with respect to all chips of a code are exploited for information detection. Correspondingly, two low-complexity detectors dispensing with channel state information, namely joint detector and stepwise detector, are proposed based on the correlation detection principles. Furthermore, to mitigate the inter-symbol interference (ISI) caused by the MC channel and specifically structured spreading code, an enhanced stepwise detector is developed to improve the performance of the stepwise detector. The bit error rate (BER) upper bound of the MCIM systems with joint detection, as well as the throughput and the computational complexity of MCIM systems with any detection schemes are analyzed. Our studies demonstrate that the proposed MCIM scheme has the potential to achieve the superior BER and throughput performance at low computational complexity, when compared with the existing modulation schemes employing two molecular types, such as molecular shift keying and molecular type permutation shift keying schemes.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"208-217"},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929466","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 vivo localization of infection sources is essential for effective diagnosis and targeted disease treatment. In this work, we leverage machine learning models to associate the temporal dynamics of biomarkers detected at static gateway positions with different infection source locations. In particular, we introduce a simulation that models infection sources, the release of biomarkers, and their decay as they flow through the bloodstream. From this, we extract time-series biomarker data with varying decay rates to capture temporal patterns from different infection sources at specific gateway positions. We then train a stacked ensemble model using LightGBM and BernoulliNB to analyze biomarker time-series data for classification. Our results reveal that higher biomarker degradation rates significantly reduce the localization accuracy by limiting the biomarker signal detected at the gateways. A fivefold increase in decay rate lowers the mean cross-validation accuracy from $sim {mathrm {92~%}}$ to $sim {mathrm {66~%}}$ . This effect is more pronounced for infection sources located farther from the gateways, e.g., the kidneys. Due to the longer distance, more biomarkers degrade before reaching the wrist-located gateways, leading to a substantial decline in classification performance.
{"title":"Machine Learning-Driven Localization of Infection Sources in the Human Cardiovascular System","authors":"Saswati Pal;Jorge Torres Gómez;Lisa Y. Debus;Regine Wendt;Florian-Lennert Lau;Cyrus Khandanpour;Malte Sieren;Stefan Fischer;Falko Dressler","doi":"10.1109/TMBMC.2025.3605770","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3605770","url":null,"abstract":"In vivo localization of infection sources is essential for effective diagnosis and targeted disease treatment. In this work, we leverage machine learning models to associate the temporal dynamics of biomarkers detected at static gateway positions with different infection source locations. In particular, we introduce a simulation that models infection sources, the release of biomarkers, and their decay as they flow through the bloodstream. From this, we extract time-series biomarker data with varying decay rates to capture temporal patterns from different infection sources at specific gateway positions. We then train a stacked ensemble model using LightGBM and BernoulliNB to analyze biomarker time-series data for classification. Our results reveal that higher biomarker degradation rates significantly reduce the localization accuracy by limiting the biomarker signal detected at the gateways. A fivefold increase in decay rate lowers the mean cross-validation accuracy from <inline-formula> <tex-math>$sim {mathrm {92~%}}$ </tex-math></inline-formula> to <inline-formula> <tex-math>$sim {mathrm {66~%}}$ </tex-math></inline-formula>. This effect is more pronounced for infection sources located farther from the gateways, e.g., the kidneys. Due to the longer distance, more biomarkers degrade before reaching the wrist-located gateways, leading to a substantial decline in classification performance.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"524-530"},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760898","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 communications is an active research area developed in the last quarter of century, trying to combine communications theory results with biological and unconventional environments. The main characteristic of molecular communications is the use of molecules as information carriers instead of electromagnetic signals to implement communications between nanomachines, natural cells, or synthetic ones, able to transmit and receive these signals, which may be useful when electromagnetic communications are not possible or undesirable. However, this new application domain comes with significant issues when it is necessary to switch from design and/or simulation to practical experimentation. In this letter, we critically discuss the transition from design to testbed experimentation, using a practical case study as driving example. The case study is relevant to the application of molecular communications for building a monitoring device, able to detect with local and minimally invasive technology the condition of blood hyperviscosity for continuous patient monitoring. We present the issues arose during the experimentation that have an impact on testbed design, and identify potential, practical solutions to address them, thus providing contributions in the area of testbed and platform design. These methodologies have a general applicability beyond the scope of this specific application, thus offering insights for broader molecular communication applications.
{"title":"From Design to Experimentation in Molecular Communications: Discussion Through a Case Study","authors":"Mauro Femminella;Gianluca Reali;Federico Calì;Nunzio Tuccitto","doi":"10.1109/TMBMC.2025.3603412","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3603412","url":null,"abstract":"Molecular communications is an active research area developed in the last quarter of century, trying to combine communications theory results with biological and unconventional environments. The main characteristic of molecular communications is the use of molecules as information carriers instead of electromagnetic signals to implement communications between nanomachines, natural cells, or synthetic ones, able to transmit and receive these signals, which may be useful when electromagnetic communications are not possible or undesirable. However, this new application domain comes with significant issues when it is necessary to switch from design and/or simulation to practical experimentation. In this letter, we critically discuss the transition from design to testbed experimentation, using a practical case study as driving example. The case study is relevant to the application of molecular communications for building a monitoring device, able to detect with local and minimally invasive technology the condition of blood hyperviscosity for continuous patient monitoring. We present the issues arose during the experimentation that have an impact on testbed design, and identify potential, practical solutions to address them, thus providing contributions in the area of testbed and platform design. These methodologies have a general applicability beyond the scope of this specific application, thus offering insights for broader molecular communication applications.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"500-505"},"PeriodicalIF":2.3,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760892","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 : 2025-08-26DOI: 10.1109/TMBMC.2025.3602957
Xinying Ren
Negative feedback is a well-known mechanism for attenuating noise and enhancing robustness in biological systems. When coupled with cell-cell signaling, it provides a strategy for achieving population-level control in multicellular systems. While cell-cell signaling alone tends to reduce cell-to-cell variability by averaging fluctuations across a population, its interplay with negative feedback can produce contrasting effects on noise regulation at the single-cell and population levels. Therefore, to design population-level controllers that achieve robust behaviors with attenuated noise, a systematic understanding of how different noise sources impact gene expression at each level becomes a critical challenge. Here, we investigate noise regulation in a quorum sensing-based negative feedback system, focusing on two extrinsic noise sources: process noise from target gene dynamics and measurement noise from quorum sensing dynamics. Our results reveal that signal-based negative feedback significantly reduces process noise at the population level, especially dynamic noise. However, at the single-cell level, it enhances variability, leading to increased noise levels under conditions of faster signal diffusion and larger population sizes. In contrast, measurement noise is consistently attenuated at both single-cell and population levels through the combined effect of cell-cell signaling and negative feedback, under conditions of faster diffusion and larger population sizes.
{"title":"The Interplay Between Cell-Cell Signaling and Negative Feedback Reduces Population Noise While Enhancing Single-Cell Variability","authors":"Xinying Ren","doi":"10.1109/TMBMC.2025.3602957","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3602957","url":null,"abstract":"Negative feedback is a well-known mechanism for attenuating noise and enhancing robustness in biological systems. When coupled with cell-cell signaling, it provides a strategy for achieving population-level control in multicellular systems. While cell-cell signaling alone tends to reduce cell-to-cell variability by averaging fluctuations across a population, its interplay with negative feedback can produce contrasting effects on noise regulation at the single-cell and population levels. Therefore, to design population-level controllers that achieve robust behaviors with attenuated noise, a systematic understanding of how different noise sources impact gene expression at each level becomes a critical challenge. Here, we investigate noise regulation in a quorum sensing-based negative feedback system, focusing on two extrinsic noise sources: process noise from target gene dynamics and measurement noise from quorum sensing dynamics. Our results reveal that signal-based negative feedback significantly reduces process noise at the population level, especially dynamic noise. However, at the single-cell level, it enhances variability, leading to increased noise levels under conditions of faster signal diffusion and larger population sizes. In contrast, measurement noise is consistently attenuated at both single-cell and population levels through the combined effect of cell-cell signaling and negative feedback, under conditions of faster diffusion and larger population sizes.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"639-651"},"PeriodicalIF":2.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760914","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 : 2025-08-25DOI: 10.1109/TMBMC.2025.3602650
Lisa Y. Debus;Mario J. Wilhelm;Henri Wolff;Luiz C. P. Wille;Tim Rese;Michael Lommel;Jens Kirchner;Falko Dressler
The experimental appraisal of existing molecular communication (MC) testbeds and modeling frameworks in real blood is an important step for future Internet of Bio-Nano Things applications. In this article, we experimentally compare the MC flow characteristics of water, blood substitute, and real porcine blood for a previously presented superparamagnetic iron oxide nanoparticles (SPION) MC testbed. We perform an extensive analysis of the channel impulse response (CIR) behavior of the testbed for the different fluids. Based on the identified MC flow characteristics, we extend an existing mathematical framework for our SPION testbed to capture the flow properties of blood. We evaluate its applicability to the collected data in comparison to two existing theoretical CIR models for MC in blood. In our work, we see that the added complexity of the transmission in blood opens up promising new possibilities to improve communication through the human circulatory system.
{"title":"Blood Makes a Difference: Experimental Evaluation of Molecular Communication in Different Fluids","authors":"Lisa Y. Debus;Mario J. Wilhelm;Henri Wolff;Luiz C. P. Wille;Tim Rese;Michael Lommel;Jens Kirchner;Falko Dressler","doi":"10.1109/TMBMC.2025.3602650","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3602650","url":null,"abstract":"The experimental appraisal of existing molecular communication (MC) testbeds and modeling frameworks in real blood is an important step for future Internet of Bio-Nano Things applications. In this article, we experimentally compare the MC flow characteristics of water, blood substitute, and real porcine blood for a previously presented superparamagnetic iron oxide nanoparticles (SPION) MC testbed. We perform an extensive analysis of the channel impulse response (CIR) behavior of the testbed for the different fluids. Based on the identified MC flow characteristics, we extend an existing mathematical framework for our SPION testbed to capture the flow properties of blood. We evaluate its applicability to the collected data in comparison to two existing theoretical CIR models for MC in blood. In our work, we see that the added complexity of the transmission in blood opens up promising new possibilities to improve communication through the human circulatory system.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"493-499"},"PeriodicalIF":2.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141504","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760883","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}
Molecular communication (MC) research is increasingly focused on applications within the human body, such as health monitoring and drug delivery, which require testing in realistic and living environments. Thus, elevating experimental MC research to the next level requires developing realistic in vivo experimental testbeds. In this paper, we introduce the chorioallantoic membrane (CAM) model as the first versatile 3D in vivo MC testbed. The CAM itself is a highly vascularized membrane formed in fertilized chicken eggs and the CAM model has gained significance in various research fields, including bioengineering, cancer research, and drug development. Its versatility, reproducibility, and realistic biological properties make it perfectly suited for next-generation MC testbeds, facilitating the transition from proof-of-concept systems to practical applications. In this paper, we provide a comprehensive introduction to the CAM model, its properties, and its applications in experimental research. Additionally, we present a characterization of the CAM model as an MC system. As an experimental study, we investigate the distribution of fluorescent molecules in the closed-loop vascular system of the CAM model. We derive an analytical model based on the wrapped normal distribution to describe the propagation of particles in dispersive closed-loop systems, where the propagation of particles is mainly influenced by diffusion and flow. Based on this analytical model, we propose parametric models to approximate the particle propagation dynamics inside the CAM model. The model parameters are estimated via curve fitting to experimental results using a nonlinear least squares method. We provide a dataset containing experimental results for 69 regions in 25 eggs, on which we evaluate the proposed parametric models. Moreover, we discuss the estimated parameters, their relationships, and plausibility. Furthermore, we investigate and develop a parametric model for the long-term behavior of particles in the CAM model and their accumulation in the chick embryo’s liver.
{"title":"The CAM Model: An in Vivo Testbed for Molecular Communication Systems","authors":"Fardad Vakilipoor;Andreas Ettner-Sitter;Lukas Brand;Sebastian Lotter;Thiha Aung;Silke Härteis;Robert Schober;Maximilian Schäfer","doi":"10.1109/TMBMC.2025.3601432","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3601432","url":null,"abstract":"Molecular communication (MC) research is increasingly focused on applications within the human body, such as health monitoring and drug delivery, which require testing in realistic and living environments. Thus, elevating experimental MC research to the next level requires developing realistic <italic>in vivo</i> experimental testbeds. In this paper, we introduce the chorioallantoic membrane (CAM) model as the first versatile 3D <italic>in vivo</i> MC testbed. The CAM itself is a highly vascularized membrane formed in fertilized chicken eggs and the CAM model has gained significance in various research fields, including bioengineering, cancer research, and drug development. Its versatility, reproducibility, and realistic biological properties make it perfectly suited for next-generation MC testbeds, facilitating the transition from proof-of-concept systems to practical applications. In this paper, we provide a comprehensive introduction to the CAM model, its properties, and its applications in experimental research. Additionally, we present a characterization of the CAM model as an MC system. As an experimental study, we investigate the distribution of fluorescent molecules in the closed-loop vascular system of the CAM model. We derive an analytical model based on the wrapped normal distribution to describe the propagation of particles in dispersive closed-loop systems, where the propagation of particles is mainly influenced by diffusion and flow. Based on this analytical model, we propose parametric models to approximate the particle propagation dynamics inside the CAM model. The model parameters are estimated via curve fitting to experimental results using a nonlinear least squares method. We provide a dataset containing experimental results for 69 regions in 25 eggs, on which we evaluate the proposed parametric models. Moreover, we discuss the estimated parameters, their relationships, and plausibility. Furthermore, we investigate and develop a parametric model for the long-term behavior of particles in the CAM model and their accumulation in the chick embryo’s liver.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"618-638"},"PeriodicalIF":2.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11133484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760920","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 : 2025-08-21DOI: 10.1109/TMBMC.2025.3601367
Mohamadbagher Zeraatpisheh;Zahra Kamal
GC-content and homopolymer run constraints play a crucial role in minimizing errors during DNA synthesis and sequencing processes. This work presents a polynomial-time algebraic framework that enforces three code design criteria in the payload strand: (i) balanced GC-content, (ii) homopolymer run length $leq 6$ , and (iii) substitution error-correction schemes. The framework also integrates k-weakly mutually uncorrelated addressing for enhanced random access. The construction of codes is based on linear systematic codes and Knuth balancing technique. Furthermore, our comparative analysis demonstrates that imposing homopolymer run constraint yields negligible rate loss in the coding scheme.
{"title":"Straightforward and Efficient Constrained Codes","authors":"Mohamadbagher Zeraatpisheh;Zahra Kamal","doi":"10.1109/TMBMC.2025.3601367","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3601367","url":null,"abstract":"GC-content and homopolymer run constraints play a crucial role in minimizing errors during DNA synthesis and sequencing processes. This work presents a polynomial-time algebraic framework that enforces three code design criteria in the payload strand: (i) balanced GC-content, (ii) homopolymer run length <inline-formula> <tex-math>$leq 6$ </tex-math></inline-formula>, and (iii) substitution error-correction schemes. The framework also integrates k-weakly mutually uncorrelated addressing for enhanced random access. The construction of codes is based on linear systematic codes and Knuth balancing technique. Furthermore, our comparative analysis demonstrates that imposing homopolymer run constraint yields negligible rate loss in the coding scheme.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"610-617"},"PeriodicalIF":2.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760928","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 : 2025-08-19DOI: 10.1109/TMBMC.2025.3600516
Akshay Uttarkar;Vidya Niranjan
Protein folding is a fundamental process crucial for the functionality of biological molecules. Despite its significance, predicting protein structures accurately remains a challenging task due to the complex nature of folding pathways and interactions. In this study, we explore the application of quantum computing, specifically error mitigated VQE, in investigating the folding of disordered regions in Ubiquitin C. By integrating advanced simulation techniques and quantum algorithms, we aim to unravel the intricate dynamics of protein folding at a molecular level. We employ a combination of molecular dynamics simulations and quantum VQE algorithms to analyze the folding kinetics and stability of C-terminal region of Ubiquitin C. Utilizing state-of-the-art quantum simulators and computational tools, we track the evolution of protein conformations and assess ground state energy values to elucidate the folding process. Our results demonstrate the effectiveness of error mitigated VQE in providing accurate ground state energy values compared to traditional methods like MD simulations with difference less than −0.91 kcal/mol. The analysis reveals insights into the structural transitions and stability of Ubiquitin C during the folding process, shedding light on key interactions and conformational changes. This study underscores the potential of quantum computing in advancing our understanding of protein folding dynamics.
{"title":"Quantum-Enabled Protein Folding of Disordered Regions in Ubiquitin C via Error-Mitigated VQE Benchmarked on Tensor Network Simulator and Aria 1","authors":"Akshay Uttarkar;Vidya Niranjan","doi":"10.1109/TMBMC.2025.3600516","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3600516","url":null,"abstract":"Protein folding is a fundamental process crucial for the functionality of biological molecules. Despite its significance, predicting protein structures accurately remains a challenging task due to the complex nature of folding pathways and interactions. In this study, we explore the application of quantum computing, specifically error mitigated VQE, in investigating the folding of disordered regions in Ubiquitin C. By integrating advanced simulation techniques and quantum algorithms, we aim to unravel the intricate dynamics of protein folding at a molecular level. We employ a combination of molecular dynamics simulations and quantum VQE algorithms to analyze the folding kinetics and stability of C-terminal region of Ubiquitin C. Utilizing state-of-the-art quantum simulators and computational tools, we track the evolution of protein conformations and assess ground state energy values to elucidate the folding process. Our results demonstrate the effectiveness of error mitigated VQE in providing accurate ground state energy values compared to traditional methods like MD simulations with difference less than −0.91 kcal/mol. The analysis reveals insights into the structural transitions and stability of Ubiquitin C during the folding process, shedding light on key interactions and conformational changes. This study underscores the potential of quantum computing in advancing our understanding of protein folding dynamics.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"118-125"},"PeriodicalIF":2.3,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929409","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 : 2025-07-30DOI: 10.1109/TMBMC.2025.3594064
Sunil Kumar;Prabhat Kumar Sharma;Anamika Singh;Adam Noel;Manav R. Bhatnagar
This study explores the collaborative behavior of engineered transmitter nano-machines (TNMs) in forming a network for efficient information transmission. The integrated units within TNMs enable them to monitor and regulate their behavior based on a system of punishments. Specifically, the research investigates how punishment impacts the system performance of TNM interacting to detect the region of interest (RoI) within a three-dimensional (3D) drift-diffusive channel. The TNMs communicate their observations about the RoI using information molecules (IMs) to a passive supervisor nano-machine (SNM), which makes RoI decisions comprehensively using AND and OR fusion rules. Inspired by nature, TNM opts for either cooperative or greedy strategies to produce IMs by consuming food from the environment. In the cooperative strategy, a TNM produces IMs and shares them equally among TNMs in the group, whereas in the greedy strategy, a TNM does not share the produced IMs, but it can continue receiving IMs shared by cooperative TNMs. A system of punishment for greedy TNMs is considered as per Tit-for-Tat and Grude policies. The study evaluates system performance in terms of the rate of success (RoS) of RoI detection, and the effects of factors such as diffusion coefficient, drift velocity, and the number of cooperating TNMs, on system performance. The results are validated using Monte Carlo simulation.
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