Design and Computational Simulation of a Continuous Cell-Free mRNA Production Line Using Artificial Intelligence for Optimization

Messenger RNA (mRNA) therapeutics have reshaped modern biotechnology by enabling rapid vaccine development, gene delivery, and personalized medicine. Despite their promise, optimizing mRNA synthesis, lipid nanoparticle (LNP) encapsulation, and purification processes remains experimentally intensive and costly. In this study, we present an end-to-end, fully computational framework that integrates kinetic modeling, LNP formation simulation, purification modeling, and AI-driven optimization using a genetic algorithm. The pipeline accurately predicts mRNA yield, encapsulation efficiency, particle size, and impurity removal across multiple processing stages. The genetic algorithm rapidly converged on an optimal parameter set, yielding a predicted final mRNA recovery of 159.36%, 90% encapsulation efficiency, and 4.4% residual impurities. Our results demonstrate that computational optimization provides a scalable, globally accessible, and laboratory-free strategy for improving mRNA bio manufacturing workflows. This framework can serve as a foundation for future experimental validation and the development of next-generation RNA therapeutics.