qSanger: 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 nonneutral mutations by selection or counterselection, the identification of neutral mutations in a heterogeneous population usually requires expensive and time-consuming methods such as quantitative or droplet polymerase chain reaction and high-throughput sequencing. Neutral mutations could even become dominant under changing environmental conditions enforcing transitory selection or counterselection. We propose a novel method, which we called qSanger, to quantify DNA using amplitude ratios of aligned electropherogram peaks from mixed Sanger sequencing reads. Plasmids expressing enhanced green fluorescent protein and mCherry fluorescent markers were used to validate qSanger both in vitro and in cotransformed Escherichia coli via quantitative polymerase chain reaction and fluorescence quantifications. We show that qSanger allows the quantification of genetic variants, including single-base natural polymorphisms or de novo mutations, from mixed Sanger sequencing reads, with substantial reduction of labor and costs compared to canonical approaches.

Reinforcement learning in synthetic gene circuits.

Synthetic gene circuits allow programming in DNA the expression of a phenotype at a given environmental condition. The recent integration of memory systems with gene circuits opens the door to their adaptation to new conditions and their re-programming. This lays the foundation to emulate neuromorphic behaviour and solve complex problems similarly to artificial neural networks. Cellular products such as DNA or proteins can be used to store memory in both digital and analog formats, allowing cells to be turned into living computing devices able to record information regarding their previous states. In particular, synthetic gene circuits with memory can be engineered into living systems to allow their adaptation through reinforcement learning. The development of gene circuits able to adapt through reinforcement learning moves Sciences towards the ambitious goal: the bottom-up creation of a fully fledged living artificial intelligence.