Journal Information

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (TMBMC)

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Impact Factor:
2.3
Publisher:
IEEE
ISSN:
2372-2061
Viewed:
16158
Tracked:
0

Call For Papers

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (TMBMC) is an academic journal published by IEEE. (ISSN 2372-2061, impact factor 2.3).

As a result of recent advances in MEMS/NEMS and systems biology, as well as the emergence of synthetic bacteria and lab/process-on-a-chip techniques, it is now possible to design chemical “circuits”, custom organisms, micro/nanoscale swarms of devices, and a host of other new systems at small length scales, and across multiple scales (e.g., micro to macro). This success opens up a new frontier for interdisciplinary signaling techniques using chemistry, biology, novel electron transfer, and other principles not previously examined. Scope This journal is devoted to the principles, design, and analysis of signaling and information systems that use physics beyond conventional electromagnetism, particularly for small-scale and multi-scale applications. This includes: molecular, quantum, and other physical, chemical and biological (and biologically-inspired) techniques; as well as new signaling techniques at these scales. As the boundaries between communication, sensing and control are blurred in these novel signaling systems, research contributions in a variety of areas are invited. Original research articles on one or more of the following topics are within the scope of the journal: mathematical modeling, information/communication-theoretic or network-theoretic analysis, networking, implementations and laboratory experiments, systems biology, data-starved or data-rich statistical analyses of biological systems, industrial applications, biological circuits, biosystems analysis and control, information/communication theory for analysis of biological systems, unconventional electromagnetism for small or multi-scale applications, and experiment-based studies on information processes or networks in biology. Contributions on related topics would also be considered for publication.
Last updated by Dou Sun in

Special Issues

Special Issue on AI-Powered Frontiers in Molecular, Biological, and Multi-Scale Communication Submission Date: 2026-10-01 Important Dates Manuscript Submission Deadline: 1 October 2026 First Round of Reviews Completed: 15 December 2026 Revised Manuscripts Due: 15 February 2027 Final Decisions Communicated: 1 April 2027 Final Manuscripts Due: 1 May 2027 Publication Date (Tentative): Third Quarter 2027 Scope The field of molecular, biological, and multi-scale communications is undergoing a paradigm shift. Specifically, it moves beyond classical information theory, and it becomes capable of engineering communication systems within biological environments, designing synthetic organisms for targeted therapies, and deploying swarms of nanomachines for intricate sensing tasks. Internet of Bio-Nano Things (IoBNT) and advanced molecular communication paradigms are attracting great attention in multiple in-field applications. However, these systems are characterized by unprecedented complexity, inherent stochasticity, and non-linear dynamics, which introduce significant challenges for traditional modeling, analysis, and control techniques. Concurrently, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools capable of identifying complex patterns and making intelligent decisions in data-rich, uncertain environments. The integration of advanced AI into the molecular and biological communication systems represents a pivotal, yet largely unexplored, field of research. Applying AI-driven techniques promises to unlock solutions to long-standing challenges in signal decoding, system control, and network orchestration at the micro and nano scales, heralding a new era of intelligent bio-compatible systems. This Special Issue aims to bring together leading researchers from the disparate fields of communications engineering, computer science, and systems biology to explore the transformative potential of AI in molecular, biological, and multi-scale communication. Our objective is to create a landmark collection of works that define the state-of-the-art and illuminate the future trajectory of this exciting interdisciplinary domain. We seek high-quality, original research that addresses the fundamental challenges and opportunities at the intersection of AI and non-conventional communication systems. The scope is intentionally broad, inviting contributions that range from novel theoretical frameworks and advanced algorithmic designs to innovative system implementations and experimental validations. Topics of interest for this special issue include, but are not limited to: Deep Learning for Signal Processing and Information Decoding in Molecular Communication Channels Reinforcement Learning for Autonomous Control and Navigation of Bio-Nano-Machine Swarms AI-Driven Design and Optimization of Synthetic Biological Circuits and Signaling Pathways Federated and Distributed Learning for Collaborative Sensing in the Internet of Bio-Nano Things (IoBNT) Physics-Informed and Biologically-Inspired Machine Learning for Modeling Multi-Scale Systems Generative AI for Creating Novel Molecular Information Encoding and Decoding Schemes Adversarial AI for Security Analysis: Modeling and Mitigating Threats in Intra-Body Networks Machine Learning for End-to-End Design of Drug Delivery and Diagnostic Systems Data-Driven and Causal Analysis of Information Processing in Cellular and Neural Systems Information-Theoretic and Performance Limits of AI-Enhanced Biological Communication Systems
Last updated by Dou Sun in

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