Effective Dynamic Modeling of Transcriptional RNA Circuitry in Cell-Free Transcription-Translation (TX-TL) Systems

RNA genetic circuitry is emerging as a powerful tool to dynamically control gene expression, and RNA networks of increasing complexity are beginning to be demonstrated. However, little work has been done to create a theoretical foundation for RNA network design. A prerequisite to this is a quantitative modeling framework that accurately captures the dynamics of RNA networks. In this work, we set out to develop an effective dynamical model of RNA transcriptional circuitry using our recent work on measuring the dynamics of an RNA transcriptional cascade using cell-free transcription-translation (TX-TL) reactions as a test case. For simplicity, we choose as a starting point the minimal number of ordinary differential equations that model the basic processes of RNA transcription and degradation and the translation of a fluorescent reporter protein. As a framework for obtaining parameters for this model, we use a model identification strategy that takes into account the full time course of dynamic experiments to design a sequential set of TX-TL experiments to perform. We show that all 15 parameters of this model can be determined from only four simple experiments. Interestingly, we show that inconsistencies between model predictions and experimental time trajectories allowed us to discover a previously unknown maturation step required for proper RNA regulator function, demonstrating that our approach can lead to model-guided discovery of basic mechanistic features of regulator function. We conclude with a description of how our model can be used to predict the dynamics of new RNA networks, and how parameter sensitivity analysis will allow us to identify the most important features for dynamic tuning in RNA network design.