An Automated Design Framework for Multicellular Recombinase Logic

Sarah Guiziou 1 Federico Ulliana 2 Violaine Moreau 1 Michel Leclère 2 Jérôme Bonnet 3
2 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Tools to systematically reprogram cellular behavior are crucial to address pressing challenges in manufacturing, environment, or healthcare. Recombinases can very efficiently encode Boolean and history-dependent logic in many species, yet current designs are performed on a case-by-case basis, limiting their scalability and requiring time-consuming optimization. Here we present an automated workflow for designing recombinase logic devices executing Boolean functions. Our theoretical framework uses a reduced library of computational devices distributed into different cellular subpopulations, which are then composed in various manners to implement all desired logic functions at the multicellular level. Our design platform called CALIN (Composable Asynchronous Logic using Integrase Networks) is broadly accessible via a web server, taking truth tables as inputs and providing corresponding DNA designs and sequences as outputs (available at http://synbio.cbs.cnrs.fr/calin). We anticipate that this automated design workflow will streamline the implementation of Boolean functions in many organisms and for various applications.
Complete list of metadatas

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01934682
Contributor : Federico Ulliana <>
Submitted on : Monday, November 26, 2018 - 10:38:23 AM
Last modification on : Saturday, June 29, 2019 - 12:44:02 PM

Links full text

Identifiers

Collections

Citation

Sarah Guiziou, Federico Ulliana, Violaine Moreau, Michel Leclère, Jérôme Bonnet. An Automated Design Framework for Multicellular Recombinase Logic. ACS Synthetic Biology, American Chemical Society, 2018, 7 (5), pp.1406-1412. ⟨10.1021/acssynbio.8b00016⟩. ⟨lirmm-01934682⟩

Share

Metrics

Record views

123