Modelling cell-free RNA and protein synthesis with minimal systems.

DNA-guided cell-free protein synthesis using a minimal set of purified components has emerged as a versatile platform in constructive biology. The E. coli-based PURE (protein synthesis using recombinant elements) system offers the basic protein synthesis factory in a prospective minimal cell relying on extant molecules. However, there is an urgent need to improve the system's performance and to build a mechanistic computational model that can help interpret and predict gene expression dynamics. Herein, we utilized all three commercially available PURE system variants: PURExpress, PUREfrex and PUREfrex2.0. We monitored apparent kinetics of mRNA and protein synthesis by fluorescence spectroscopy at different concentrations of DNA template. Analysis of polysome distributions by atomic force microscopy, combined with a stochastic model of translation, revealed inefficient usage of ribosomes, consistent with the idea that translation initiation is a limiting step. This preliminary dataset was used to formulate hypotheses regarding possible mechanisms impeding robust gene expression. Next, we challenged these hypotheses by devising targeted experiments aimed to alleviate the current limitations of PUREfrex. We identified depletion of key initiation factors (IFs) by translationally inactive mRNA as a possible inhibitory mechanism. This adverse process could partly be remedied by targeted mRNA degradation, whereas addition of more IFs and of the hrpA RNA helicase had no substantial effects. Moreover, the depletion of tRNAs as peptidyl-tRNAs can become limiting in PUREfrex (but not in PURExpress), which can be alleviated by addition of peptidyl-tRNA-hydrolase (PTH). We attempted to build a new model for PURE system dynamics integrating all experimental observations. Although a satisfying global fit can be obtained in specific conditions (with PTH), a unifying system's level model is still missing.

Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability.

Signal transduction by prokaryotes almost exclusively relies on two-component systems for sensing and responding to (extracellular) signals. Here, we use stochastic models of two-component systems to better understand the impact of stochasticity on the fidelity and robustness of signal transmission, the outcome of autoregulatory gene expression and the influence of cell growth and division. We report that two-component systems are remarkably robust against copy number fluctuations of the signalling proteins they are composed of, which enhances signal transmission fidelity. Furthermore, we find that due to stochasticity these systems can get locked in an active state for extended time periods when (initially high) signal levels drop to zero. This behaviour can contribute to a bet-hedging adaptation strategy, aiding survival in fluctuating environments. Additionally, autoregulatory gene expression can cause two-component systems to become bistable at realistic parameter values. As a result, two sub-populations of cells can co-exist-active and inactive cells, which contributes to fitness in unpredictable environments. Bistability proved robust with respect to cell growth and division, and is tunable by the growth rate. In conclusion, our results indicate how single cells can cope with the inevitable stochasticity occurring in the activity of their two-component systems. They are robust to disadvantageous fluctuations that scramble signal transduction and they exploit beneficial stochasticity that generates fitness-enhancing heterogeneity across an isogenic population of cells.