Publications by authors named "E V Romantseva"

Article Synopsis
  • The text discusses the growing need for precise engineering of biological functions in synthetic biology, especially for programmed sensing that regulates gene expression based on stimuli.
  • It introduces two innovative methods, in silico selection and machine-learning-enabled forward engineering, that leverage a comprehensive dataset to develop genetic sensors with specifically defined dose-response characteristics.
  • The methods demonstrate the capability to fine-tune genetic sensors for various performance metrics, such as sensitivity and output, and to predictively engineer new sensor mutations beyond the existing dataset.
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DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows.

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Performance variability is a common challenge in cell-free protein production and hinders a wider adoption of these systems for both research and biomanufacturing. While the inherent stochasticity and complexity of biology likely contributes to variability, other systematic factors may also play a role, including the source and preparation of the cell extract, the composition of the supplemental reaction buffer, the facility at which experiments are conducted, and the human operator (Cole et al. ACS Synth Biol 8:2080-2091, 2019).

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Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function.

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