Quantifying dynamic mechanisms of auto-regulation in Escherichia coli with synthetic promoter in response to varying external phosphate levels View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Cansu Uluşeker, Jesús Torres-Bacete, José L. García, Martin M. Hanczyc, Juan Nogales, Ozan Kahramanoğulları

ABSTRACT

Escherichia coli have developed one of the most efficient regulatory response mechanisms to phosphate starvation. The machinery involves a cascade with a two-component system (TCS) that relays the external signal to the genetic circuit, resulting in a feedback response. Achieving a quantitative understanding of this system has implications in synthetic biology and biotechnology, for example, in applications for wastewater treatment. To this aim, we present a computational model and experimental results with a detailed description of the TCS, consisting of PhoR and PhoB, together with the mechanisms of gene expression. The model is parameterised within the feasible range, and fitted to the dynamic response of our experimental data on PhoB as well as PhoA, the product of this network that is used in alkaline phosphatase production. Deterministic and stochastic simulations with our model predict the regulation dynamics in higher external phosphate concentrations while reproducing the experimental observations. In a cycle of simulations and experimental verification, our model predicts and explores phenotypes with various synthetic promoter designs that can optimise the inorganic phosphate intake in E. coli. Sensitivity analysis demonstrates that the Pho-controlled genes have a significant influence over the phosphate response. Together with experimental findings, our model should thus provide insights for the investigations on engineering new sensors and regulators for living technologies. More... »

PAGES

2076

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-38223-w

DOI

http://dx.doi.org/10.1038/s41598-018-38223-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112135793

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30765722


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