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Racovita, A.

Publications and source records attributed to Racovita, A..

2 recordsLinked to original sources

Quantification of genetic variants in bacterial cultures by Sanger sequencing

Genetic variations such as mutations and recombinations arise spontaneously in all cultured organisms. Although it is possible to identify non-neutral mutations by selection or counter- selection, neutral mutations usually require DNA sequencing to be identified in a population, which are normally expensive and time-consuming. Neutral mutations could even become dominant under changing environmental conditions enforcing transitory selection or counter- selection. We propose a novel methodology to quantify DNA using Sanger sequencing, that we validated experimentally with specially-engineered plasmids both in vitro and in co-transformed E. coli by and assessing our predictions with qPCR and fluorescence quantifications. The method relies on the alignment of the electropherograms from the query and reference samples, where we quantify the DNA concentration from the amplitude ratio of aligned electropherogram peaks. Our DNA quantification will allow quantifying genetic variants, including single-base natural polymorphisms or de novo mutations, from mixed Sanger sequencing reads, with consistent reduction of costs compared to canonical approaches such as qPCR.

synthetic biology↗

Engineered gene circuits with reinforcement learning allow bacteria to master gameplaying

Gene circuits enable cells to make decisions by controlling the expression of genes in reaction to specific environmental factors1. These circuits can be designed to encode logical operations2-7, but implementation of more complex algorithms has proved more challenging. Directed evolution optimizes gene circuits8 without the need for design knowledge9, but adjusting multiple genes and conditions10 in genotype searches poses challenges11. Here we show a multicellular sensor system, AdaptoCells, in Escherichia coli, that can evolve complex behavior through an accelerated adaptation to chemical environments. AdaptoCells recognize chemical patterns and act as a decision-making system. Using an iterative improvement method, we demonstrate that the AdaptoCells can evolve to achieve mastery in the game of tic-tac-toe, demonstrating an unprecedented level of complexity for engineered living cells. We provide an effective and straightforward way to encode complexity in gene circuits, allowing for fast adaptation in response to dynamic environments and leading to optimal decisions.

synthetic biology↗