Sensing enabling metabolic pathways (SEMPs) for Biosensors Engineering

We have developed a technology named ‘Sensing Enabling Metabolic Pathway’ making use of synthetic networks to expand the portfolio of molecules that are currently known to be detected by natural systems. Our method consists of transforming a priori non-detectable molecules into detectable ones.

Sensing enabling metabolic pathways can be engineered in whole-cell systems
or in cell-free systems

The SEMP technology comprises several computational tools and experimental protocols that can be used together or independently.

  • A dataset of detectable molecules (effectors) [8] that activate or repress transcription factors and or riboswitches.
  • The RetroPath software [10] to search metabolic pathways transforming any molecule one wishes to detect into an effector.
  • The Sensipath web server [12] to visualize SEMPs and get sequence information on transforming enzymes, transcription factors, and riboswitches.
  • Protocols to build and test biosensors in whole-cell [2,3,9,13,14] and cell-free systems [1,4,5]

We have benchmarked the technology for a variety of molecules, ranging from detecting central metabolites [14], secondary metabolites [2,9,13], sugars [3,4], chemicals [13] and biomarkers [1,5]. We have showcased the applicability of the SEMP technology in the context of enzyme and metabolic engineering [2,3,4,9,14], environmental pollutant detection [13], and clinical sample analysis [1,5].

For clinical sample analysis, we have started an ANR funded project (ANR-18-CE44-0015) with the CBS (INSERM) and CHU of Montpellier to detect prostate cancer biomarkers. Further information on the project can be found at:


  1. Pandi A, Koch M, Voyvodic PL, Soudier P, Bonnet J, Kushwaha M, Faulon JL. Metabolic Perceptrons for Neural Computing in Biological Systems. Nature Communications, 10: 3880, 2019. | doi: 1038/s41467-019-11889-0
  2. Castaño-Cerezo S, Fournié M, Urban P, Faulon JL, Truan G. Development of a Biosensor for Detection of Benzoic Acid Derivatives in Saccharomyces cerevisiae. Frontiers in Bioengineering and Biotechnology, 7: 372, 2020. | doi: 10.3389/fbioe.2019.00372 | PMID: 31970152
  3. Armetta J, Berthome R, Cros A, Pophillat C, Colombo B, Pandi A, Grigoras I. Biosensor-based enzyme engineering approach applied to psicose biosynthesis. Synthetic Biology, 4(1): ysz028, 2019. | doi: 10.1093/synbio/ysz028
  4. Pandi A, Grigoras I, Borkowski O*, Faulon JL*. Optimizing Cell-Free Biosensors to Monitor Enzymatic Production. ACS Synth Biol. 2019 Aug 16;8(8):1952-1957. | doi: 1021/acssynbio.9b00160 | PMID: 31335131
  5. Voyvodic PL, Pandi A, Koch M, Conejero I, Valjent E, Courtet P, Renard E, Faulon JL*, Bonnet J*. Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors. Nature Communications, 10(1):1697, 2019. | doi: 1038/s41467-019-09722-9| PMID: 30979906
  6. Koch M, Pandi A, Borkowski O, Cardoso Batista A, Faulon JL*. Custom-made transcriptional biosensors for metabolic engineering. Current Opinion in Biotechnology, 59:78-84, 2019. | doi: 1016/j.copbio.2019.02.016 | PMID: 30921678
  7. Koch M, Faulon JL*, Borkowski O*. Models for Cell-Free Synthetic Biology: Make Prototyping Easier, Better, and Faster. Frontiers in Bioengineering and Biotechnology, 6: 182, 2018. | doi: 3389/fbioe.2018.00182| PMID: 30555825
  8. Koch M, Pandi A, Delépine B, Faulon JL*. A dataset of small molecules triggering transcriptional and translational cellular responses. Data in Brief, 17: 1374-1378, 2018. | doi: 1016/j.dib.2018.02.061 | PMID: 29556520
  9. Trabelsi H, Koch M, Faulon JL*. Building a minimal and generalizable model of transcription-factor based biosensors: showcasing flavonoids. Biotechnology and Bioengineering, 115(9): 2292-2304, 2018. | doi: 1002/bit.26726| PMID: 29733444
  10. Delépine B, Duigou T, Carbonell P, Faulon JL*. 0: A retrosynthesis workflow for metabolic engineers. Metabolic Engineering, 45: 158-170, 2018. | doi: 10.1016/j.ymben.2017.12.002 | PMID: 29233745
  11. Libis V, Delépine B, Faulon JL*. Sensing new chemicals with bacterial transcription factors. Current opinion in microbiology, 33: 105-112, 2016. | doi: 1016/j.mib.2016.07.006 | PMID: 27472026
  12. Delépine B, Libis V, Carbonell P, Faulon JL*. SensiPath: computer-aided design of sensing-enabling metabolic pathways. Nucleic Acids Research, 44: W226-231, 2016. | doi: 1093/nar/gkw305 | PMID: 27106061
  13. Libis V, Delépine B, Faulon JL*. Expanding biosensing abilities through computer-aided design of metabolic pathways. ACS Synthetic Bioliology, 5(10): 1076-1085, 2016. | doi: 1021/acssynbio.5b00225 | PMID: 27028723
  14. Fehér T, Libis V, Carbonell P, Faulon JL*. A sense of balance: experimental investigation and modeling of a malonyl-CoA sensor in Escherichia coli. Frontiers in Bioengineering and Biotechnology, 3: 46, 2015. | doi: 3389/fbioe.2015.00046 | PMID: 25905101