  RNA structure and functional dynamics play fundamental roles in controlling
biological systems. Molecular dynamics simulation, which can characterize
interactions at an atomistic level, can advance the understanding on new drug
discovery, manufacturing, and delivery mechanisms. However, it is
computationally unattainable to support the development of a digital twin for
enzymatic reaction network mechanism learning, and end-to-end bioprocess design
and control. Thus, we create a hybrid ("mechanistic + machine learning") model
characterizing the interdependence of RNA structure and functional dynamics
from atomistic to macroscopic levels. To assess the proposed modeling strategy,
in this paper, we consider RNA degradation which is a critical process in
cellular biology that affects gene expression. The empirical study on RNA
lifetime prediction demonstrates the promising performance of the proposed
multi-scale bioprocess hybrid modeling strategy.
