Rational programming of history-dependent logic in cellular populations

Ana Zúñiga, Sarah Guiziou, Pauline Mayonove, Zachary Ben Meriem, Miguel Camacho, Violaine Moreau, Luca Ciandrini, Pascal Hersen and Jerome Bonnet ()
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Ana Zúñiga: University of Montpellier
Sarah Guiziou: University of Montpellier
Pauline Mayonove: University of Montpellier
Zachary Ben Meriem: UMR 7057 CNRS & Université Paris Diderot
Miguel Camacho: University of Montpellier
Violaine Moreau: University of Montpellier
Luca Ciandrini: University of Montpellier
Pascal Hersen: UMR 7057 CNRS & Université Paris Diderot
Jerome Bonnet: University of Montpellier

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-18455-z

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