Physics-Informed Neural Networks for Bio-Nano Digital Twins: A Multimodel Framework With IoBNT Integration

Digital Twin (DT) technology is transforming biological processes by enabling real-time predictive modeling and optimization. However, implementing DTs at the micro- and nano-scale presents significant challenges in data extraction, transmission, and computation due to the complexity of biological environments. To address these challenges, this work proposes a multi-model Physics-Informed Neural Network (PINN) framework, enhanced by the Internet of Bio-Nano Things (IoBNT), to improve real-time bioprocess monitoring and prediction. The proposed framework integrates IoBNT for efficient biological data acquisition and transmission with advanced deep neural networks (DNNs), including Fully Connected Neural Networks (FCNN), Residual Block Neural Networks (ResBlock NN), and Recurrent Neural Networks (RNN), to enhance predictive accuracy. By incorporating physics-based constraints into the learning process, the PINN architecture ensures biologically plausible and data-efficient predictions. Experimental results demonstrate the framework’s effectiveness in monitoring microbial and substrate growth using Monod equations, achieving an average mean absolute error (MAE) of 0.0275 g/L, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value close to 1, and a root mean squared error (RMSE) of 0.0425 g/L across four of the five proposed architectures. Additionally, the integration of IoBNT enhances data reliability, reducing transmission errors by up to 98%. These findings highlight the potential of IoBNT-powered PINN frameworks for developing high-fidelity, self-adaptive DTs in digital biological and healthcare applications.