Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with r...

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Bibliographic Details
Published in:Nature communications 2020-09, Vol.11 (1), p.4758-4758, Article 4758
Main Authors: Zúñiga, Ana, Guiziou, Sarah, Mayonove, Pauline, Meriem, Zachary Ben, Camacho, Miguel, Moreau, Violaine, Ciandrini, Luca, Hersen, Pascal, Bonnet, Jerome
Format: Article
Language:English
Subjects:
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631/114/794
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Cell Physiological Phenomena - genetics
DNA - genetics
DNA - metabolism
Humanities and Social Sciences
Life Sciences
Logic
Models, Genetic
multidisciplinary
Recombinases - genetics
Recombinases - metabolism
Science
Science (multidisciplinary)
Software
Synthetic Biology - methods
Workflow
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Summary:Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare. Automated frameworks to systematically implement robust history-dependent genetic programs in cellular populations.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-18455-z