We believe that scientists need to be provided with tools for metabolic engineering that enable them to apply their creativity, and simulate their unique ideas with a strong backing of scientific literature.
Galaxy SynBioCAD is a platform to power the metabolic engineering revolution. It is an open access Galaxy workflow environment to design and build different metabolic routes for the production of a compound of interest. We combine the proven efficiency of RetroPath2.0 – an automated open-source tool based on generalized reaction rules that generate retrosynthesis networks linking the target compound(s) (called the source) to the metabolites of the chassis strain (called the sink); with a downstream analysis method. This provides a user with a user-defined ranking function to extract the most efficient pathways from the generated metabolic pathways.
The intuitive, user-friendly platform of the Galaxy SynBioCAD Portal:
- offers a versatile one-stop destination to access multiple, connected tools in one place.
- enables to construct customized workflows for generating and analyzing pathways, pathway ranking, designing genetic parts, etc that can be seamlessly integrated with your data files.
- provides a graphical user interface allows for rapid test and prototyping, even for users with little to no knowledge in programing.
These features make this tool a valuable addition to a biological engineer’s bench desk, thus accelerating development of metabolic engineering technology and its applications that can have a huge impact in our lives.
Galaxy SynBioCAD tools publications
- Koch M, Duigou T, Faulon JL. Reinforcement Learning for Bioretrosynthesis. ACS Synthetic Biology, 9(1): 15, 2020. | doi: 10.1021/acssynbio.9b00447 | PMID: 31841626
- Carbonell, P. Faulon JL, Breitling, R. Efficient learning in metabolic designs through optimal assembling. IFAC-PapersOnLine, 52(26): 7-22 , 2019. | doi: 10.1016/j.ifacol.2019.12.228
- Duigou T, du Lac M, Carbonell P, Faulon JL*. RetroRules: a database of reaction rules for engineering biology. Nucleic Acids Research, 47(D1): D1229-1235, 2019. | doi: 10.1093/nar/gky940 | PMID: 30321422
- Carbonell P*, Wong J, Swainston N, Takano E, Turner NJ, Scrutton NS, Kell DB, Breitling R, Faulon JL*. Selenzyme: enzyme selection tool for pathway design. Bioinformatics, 34(12): 2153-2154, 2018. | doi: 10.1093/bioinformatics/bty065
- Delépine B, Duigou T, Carbonell P, Faulon JL*. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metabolic Engineering, 45: 158-170, 2018. | doi: 10.1016/j.ymben.2017.12.002 | PMID: 29233745
- Swainston N, Dunstan M, Jervis AJ, Robinson CJ, Carbonell P, Williams AR, Faulon JL, Scrutton NS, Kell DB. PartsGenie: an integrated tool for optimising and sharing synthetic biology parts. Bioinformatics, 34(13): 2327-2329, 2018. | doi: 10.1093/bioinformatics/bty105
- Fehér T, Planson AG, Carbonell P, Fernández-Castané A, Grigoras I, Dariy E, Perret A, Faulon JL*. Validation of RetroPath, a computer-aided design tool for metabolic pathway engineering. Biotechnology Journal, 9(11): 1446-1457, 2014. | doi: 10.1002/biot.201400055 | PMID: 25224453
- Carbonell P, Parutto P, Baudier C, Junot C, Faulon JL*. Retropath: automated pipeline for embedded metabolic circuits. ACS Synthetic Biology, 3(8): 565-577, 2014. | doi: 10.1021/sb4001273 | PMID: 24131345
- Carbonell P, Parutto P, Herisson J, Pandit S, Faulon JL*. XTMS: pathway design in an eXTended metabolic space. Nucleic Acids Research, 42: W389-394, 2014. | doi: 10.1093/nar/gku362 | PMID: 24792156
- Carbonell P, Fichera D, Pandit SB, Faulon JL*. Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms. BMC Systems Biology, 6: 10, 2012. | doi: 10.1186/1752-0509-6-10 | PMID: 22309974
- Carbonell P, Planson AG, Fichera D, Faulon JL*. A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Systems Biology, 5: 122, 2011. | doi: 10.1186/1752-0509-5-122 | PMID: 21819595