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.

The engineering of living cells able to learn algorithms by themselves, such as playing board games —a classic challenge for artificial intelligence— will allow complex ecosystems and tissues to be chemically reprogrammed to learn complex decisions. However, current engineered gene circuits encoding decision-making algorithms have failed to implement self-programmability and they require supervised tuning. We show a strategy for engineering gene circuits to rewire themselves by reinforcement learning. We created a scalable general-purpose library of Escherichia coli strains encoding elementary adaptive genetic systems capable of persistently adjusting their relative levels of expression according to their previous behavior. Our strains can learn the mastery of 3×3 board games such as tic-tac-toe, even when starting from a completely ignorant state. We provide a general genetic mechanism for the autonomous learning of decisions in changeable environments.One-Sentence SummaryWe propose a scalable strategy to engineer gene circuits capable of autonomously learning decision-making in complex environments.

E. coli cells, which are commonly used to study gene expression, show membraneless organization of gene expression components within the cell. Cellfree expression systems are also widely used to study gene expression, but a major limitation is the lack of a means to spatially organize DNA plasmids and ribosomes to mimic the cellular environment. In this work, we used computer simulations to guide experimental efforts to control the spatial organization of DNA and ribosomes in cell-sized vesicles using macromolecular crowding. Using Brownian dynamics simulations of coarse-grained models of DNA plasmids and crowders, we showed that plasmids remain uniformly distributed at low levels of crowding but become strongly adsorbed at confining surfaces at high levels of crowding. These effects are due to entropic depletion interactions resulting from the presence of crowding molecules. We validated the simulation results experimentally by using fluorescently-labelled DNA plasmids and ribosomes in cell-sized vesicles at different concentrations of the macromolecular crowder Ficoll-70. Large crowder concentrations resulted in preferential localization of plasmids at the walls of the vesicle, while ribosomes remained uniformly distributed throughout the vesicle. We then used kinetic Monte Carlo simulations to study how protein production was affected by crowding-induced changes in spatial organization and diffusion. The localization of DNA to the wall resulted in lower protein abundance and a decrease in translational efficiency with an increase in system size. Experimentally, we tracked the dynamics of transcription and translation in crowded vesicles using a coupled mRNA/protein reporter technique. These results were consistent with results from simulations. We thus used computer simulations to design a cell-free experimental platform capable of spatial organization of gene expression components. This platform can be used to better understand the spatial control of gene expression in cells.

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.