Our group is interested in synthetic biology in whole-cell and cell-free systems. We develop computational and wet lab protocols to search, design, and engineer biological pathways and networks. Other activities include structure-activity, sequence-function relationships, retrosynthesis, and the design of experiments using active and reinforcement machine learning methods. The applications of our work include synthetic metabolic pathways and genetic circuits engineering for bioproduction, biosensing, and biocomputing.
BioRetroSynth
Our group is interested in synthetic biology and systems metabolic engineering in whole-cell and cell-free systems.
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active_learning_cell_free
active_learning_cell_free PublicScripts to perform active learning as described in the referenced article.
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RetroPath2-wrapper
RetroPath2-wrapper PublicPython wrapper for Retropath2.0 Knime workflow
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Repositories
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- icfree-feedstock Public Forked from conda-forge/icfree-feedstock
A conda-smithy repository for icfree.
brsynth/icfree-feedstock’s past year of commit activity - bioconda-recipes Public Forked from bioconda/bioconda-recipes
Conda recipes for the bioconda channel.
brsynth/bioconda-recipes’s past year of commit activity - extractor_brendapy Public
brsynth/extractor_brendapy’s past year of commit activity
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