Programmable bacterial biosensors for medical diagnosis

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Alexis Courbet, Jerome Bonnet, Patrick Amar, Franck Molina

Sysdiag, France

We propose to develop a programmable, multiplexed biosensing platform using genetically engineered bacteria for in vitro biomarkers detection. Such bacteria would be capable of multiplexed detection, logic processing, and data storage in a relevant clinical context while being simple and inexpensive to use. We first validate our in vivo computation strategy via an in silico, whole-cell stochastic model. Analysis and refinement of the model allows us to predict functional properties and limits of our system and to analyze its robustness at the population level. We then demonstrate the operability and robustness of the system in vitro in clinical samples using standard inducible promoters in an Escherichia coli chassis. Finally, we incorporate synthetic parts and devices recently developed to produce a first prototype capable of multiplexed biomarkers detection, signal processing based on Boolean logic and visual readout, all in a clinically relevant context. Our modular platform could be reprogrammed for the detection of different pathologies but also for applications in other fields of engineering like environmental detection and remediation.

Alexis Courbet*2, Jerome Bonnet*1, Patrick Amar3, Drew Endy1, and Franck Molina2.

* co-first authors
1 Department of Bioengineering, Room 269B, Y2E2 Building, 473 Via Ortega, Stanford University, Stanford, CA 94305
2 SysDiag UMR 3145 CNRS/Bio-Rad, Modélisation et ingénierie de systèmes complexes biologiques pour le diagnostic, Cap Delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4
3 Laboratoire de Recherche en Informatique, CNRS, UMR 8623, Université Paris-Sud 11, 91405 Orsay Cedex, France