Dataset Information

Rapid acquisition and model-based analysis of cell-free transcription-translation reactions from nonmodel bacteria.

ABSTRACT: Native cell-free transcription-translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription-translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription-translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.

SUBMITTER: Moore SJ

PROVIDER: S-EPMC5948957 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Publications

Rapid acquisition and model-based analysis of cell-free transcription-translation reactions from nonmodel bacteria.

Moore Simon J SJ MacDonald James T JT Wienecke Sarah S Ishwarbhai Alka A Tsipa Argyro A Aw Rochelle R Kylilis Nicolas N Bell David J DJ McClymont David W DW Jensen Kirsten K Polizzi Karen M KM Biedendieck Rebekka R Freemont Paul S PS

Proceedings of the National Academy of Sciences of the United States of America 20180417 19

Native cell-free transcription-translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, <i>Bacillus megaterium</i>, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated pl ...[more]

PMID: 29666238

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Project description:Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.

Dataset Information

Rapid acquisition and model-based analysis of cell-free transcription-translation reactions from nonmodel bacteria.

Publications

Rapid acquisition and model-based analysis of cell-free transcription-translation reactions from nonmodel bacteria.

Similar Datasets

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