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.
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BioRetroSynth
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- amn_release
amn_release Public - 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 PublicPython wrapper for Retropath2.0 Knime workflow
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brsynth/morganrxn's past year of commit activity - bioconda-recipes Public Forked from bioconda/bioconda-recipes
Conda recipes for the bioconda channel.
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brsynth/rplibs's past year of commit activity - rpScore Public
Computes a global score for a heterologous pathway. The score is calculated from a learning process based on reaction rules score, FBA and thermodynamics metrics, and the number of reactions in the pathway.
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brsynth/rpScore's past year of commit activity
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