Genomically encoded analog memory with precise in vivo DNA writing in living cell populations

Farzadfard, Fahim; Lu, Timothy K.

doi:10.1126/science.1256272

Cited by 282 publications

(304 citation statements)

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“…Genetic devices including oscillators, amplifiers and recorders, which have been developed based on fine-tuned relationships between input and output signals, are promising tools for programming and controlling gene expression in living cells [3][4][5] . These devices sense extracellular or intracellular perturbations and actuate cellular decision-making processes akin to logic gates in electrical circuits.…”

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confidence: 99%

Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast

et al. 2016

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TitleEngineering prokaryotic transcriptional activators as metabolite biosensors in yeast io-based production of chemicals and fuels is an attractive avenue to reduce dependence on petroleum. For bio-based production, biocatalysts must often be genetically modified to increase production. However, the current efficiency of genomeengineering methods and parts prospecting allows for unprecedented genotype diversity that vastly outstrips our ability to screen for best cell performance 1,2 .To meet current demand, bioengineers have started to develop genetically encoded devices and systems that enable screening and selection of better-performing biocatalysts in higher throughput. Genetic devices including oscillators, amplifiers and recorders, which have been developed based on fine-tuned relationships between input and output signals, are promising tools for programming and controlling gene expression in living cells [3][4][5] . These devices sense extracellular or intracellular perturbations and actuate cellular decision-making processes akin to logic gates in electrical circuits. Hence, from a diverse set of inputs, molecular gating components such as RNA aptamers and allosterically regulated transcription factors have been engineered to control outputs for applications such as high-throughput screening, actuation on cellular metabolism and evolution-based selection of optimal cell performance [6][7][8] .A key component in many of the reported devices is a ligandinducible transcriptional regulator. Transcriptional regulators are straightforward and powerful components, with many uses in genetic designs. Owing to their modular structure, transcriptional regulators have proven to be versatile platforms for genetically encoded Boolean logic functions 9,10 . In particular, gene switches based on ligand-binding transcriptional repressors bind to genomic targets in the absence of their cognate ligand and thereby repress gene expression of the downstream gene(s), whereas binding between ligand and repressor causes the release of the repressor from the DNA and thereby a derepression 11 . In such 'NOT' gates, simple steric hindrance of RNA polymerase progression, as in the case of the tetracycline-responsive gene switch TetR, have for decades been used for conditional control of gene expression in both prokaryotic and eukaryotic chassis 12,13 . Transcriptional repressors and other artificial transcriptional regulators can be further engineered, for example, via the addition of nuclear localization signals, destabilization domains and transcriptional activation regions, to repurpose conditional repressors into activators [13][14][15] . Though conceptually intriguing and practically relevant, the repurposing of logic gates can suffer from the inherent need for extensive engineering 9,16,17 .Though most ligand-inducible genetic devices adopted for eukaryotes historically have been founded on transcriptional repressors, a hitherto untapped resource for use in genetic designs is ligand-inducible transcriptional activators. Bac...

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Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast

et al. 2016

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“…Through the "control" level shown in Figure 10, the programmable memory can drive the generation of single-stranded DNA in response to external and internal signals, e.g. using recently developed DNA writing technologies (SCRIBE [49]). The optimal strategy needed for SCRIBE or CRISPR-Cas9 for genome engineering [50], i.e.…”

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confidence: 99%

Analysis and design of molecular machines

Angione

Costanza

Carapezza

et al. 2015

Theoretical Computer Science

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Biologically inspired computation has been recently used with mathematical models towards the design of new synthetic organisms. In this work, we use Pareto optimality to optimize these organisms in a multi-objective fashion. We infer the best knockout strategies to perform specific tasks in bacteria, which involve concurrent maximization/minimization of multiple functions (codomain) and optimization of several decision variables (domain). Furthermore, we propose and exploit a mapping between the metabolism and a register machine. We show that optimized bacteria have computational capability and act as molecular Turing machines programmed using a Pareto optimal solution. Finally, we investigate communication between bacteria as a means to evaluate their computational capability. We report that the density and gradient of the Pareto curve are useful tools to compare models and understand their structure, while modelling organisms as computers proves useful to carry out computation using biological machines with specific input-output conditions, as well as to estimate the bacterial computational effort for specific tasks.

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“…Orthogonal integrases that can irreversibly flip DNA segments have been engineered to encode genetic memory [132,133], and recombinase-based systems have been used to record the magnitude and duration of trigger exposure through regulated co-expression of recombinase and retron elements that modify genomic DNA [134].…”

Section: Synthetic Probiotics As Living Diagnosticsmentioning

confidence: 99%