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<p>Biological systems, such as microbial biofilms, exhibit distributed behavior driven by local cellular interactions that yield emergent computational dynamics. Translating these mechanisms into engineered platforms opens new possibilities for collective behavior in future wireless systems. In this work, we investigate biofilm-integrated gene regulatory networks (GRNNs) as living edge processors for next-generation wireless networks, with emphasis on massive machine-type communication (mMTC) in 6G. Using a dual-layer approach, we first analyze GRNN subnetworks from a network-theoretic perspective, quantifying structural complexity, mutual information, and nonlinear signal processing to reveal principles of capacity, adaptability, and robustness. We then simulate light-driven gene activation in biofilms with embedded upconversion nanoparticles (UCNPs), showing how optical parameters, nanoparticle concentration, and molecular quenching shape the operational envelope of computation. Gateway nodes enable hierarchical, reconfigurable processing, while biophysical constraints define the subset of functionally active subnetworks. Collectively, these results establish a co-designed system in which network architecture and optical-molecular parameters jointly govern computational fidelity, resource allocation, and signal distribution. This framework lays the foundation for the Biofilm Living AI Device (BLAID) architecture: a transformative step toward biologically integrated wireless networks.</p>

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Integrating biological intelligence into 6G mMTC systems using gene regulatory neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Hadeel Elayan,
  • Samitha Somathilaka,
  • Josep M. Jornet,
  • Sasitharan Balasubramaniam

摘要

Biological systems, such as microbial biofilms, exhibit distributed behavior driven by local cellular interactions that yield emergent computational dynamics. Translating these mechanisms into engineered platforms opens new possibilities for collective behavior in future wireless systems. In this work, we investigate biofilm-integrated gene regulatory networks (GRNNs) as living edge processors for next-generation wireless networks, with emphasis on massive machine-type communication (mMTC) in 6G. Using a dual-layer approach, we first analyze GRNN subnetworks from a network-theoretic perspective, quantifying structural complexity, mutual information, and nonlinear signal processing to reveal principles of capacity, adaptability, and robustness. We then simulate light-driven gene activation in biofilms with embedded upconversion nanoparticles (UCNPs), showing how optical parameters, nanoparticle concentration, and molecular quenching shape the operational envelope of computation. Gateway nodes enable hierarchical, reconfigurable processing, while biophysical constraints define the subset of functionally active subnetworks. Collectively, these results establish a co-designed system in which network architecture and optical-molecular parameters jointly govern computational fidelity, resource allocation, and signal distribution. This framework lays the foundation for the Biofilm Living AI Device (BLAID) architecture: a transformative step toward biologically integrated wireless networks.