1. Rational programming of history-dependent logic in cellular populations
- Author
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Ana Zúñiga, Pauline Mayonove, Z. Ben Meriem, Miguel Camacho, Violaine Moreau, Pascal Hersen, Jerome Bonnet, Luca Ciandrini, Sarah Guiziou, Bodescot, Myriam, Infrastructure Française pour la Biologie Structurale Intégrée - - FRISBI2010 - ANR-10-INBS-0005 - INBS - VALID, Centre de Biochimie Structurale [Montpellier] (CBS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), University of Washington [Seattle], Matière et Systèmes Complexes (MSC (UMR_7057)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire Charles Coulomb (L2C), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Physico-Chimie Curie [Institut Curie] (PCC), Institut Curie [Paris]-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Support was provided by an ERC Starting Grant 'Compucell,' the INSERM Atip-Avenir program and the Bettencourt-Schueller Foundation. S.G. was supported by a Ph.D. fellowship from the French Ministry of Research and the French Foundation for Medical Research (FRM) FDT20170437282. Z.B.M. and P.H. were supported by an ERC Consolidator grant 'Smartcells.' The CBS acknowledges support from the French Infrastructure for Integrated Structural Biology (FRISBI) ANR-10-INSB-05-01., ANR-10-INBS-0005,FRISBI,Infrastructure Française pour la Biologie Structurale Intégrée(2010), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)
- Subjects
Logic ,Computer science ,Science ,Population ,[SDV.BC]Life Sciences [q-bio]/Cellular Biology ,ENCODE ,Article ,Cell Physiological Phenomena ,Workflow ,Recombinases ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Biomanufacturing ,education ,lcsh:Science ,[SDV.BC] Life Sciences [q-bio]/Cellular Biology ,Gene ,030304 developmental biology ,0303 health sciences ,education.field_of_study ,Models, Genetic ,Molecular engineering ,business.industry ,DNA ,Modular design ,Programming language ,Multicellular organism ,chemistry ,Scalability ,Synthetic Biology ,lcsh:Q ,Software engineering ,business ,Software ,030217 neurology & neurosurgery ,Biotechnology - 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., Automated frameworks to systematically implement robust history-dependent genetic programs in cellular populations.
- Published
- 2020