Difference between revisions of "Team:Heidelberg/Software"

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                 {{Heidelberg/panelelement|DeeProtein|https://static.igem.org/mediawiki/2017/2/2b/T--Heidelberg--2017_phage-titer-logo.png|https://2017.igem.org/Team:Heidelberg/Software/DeeProtein|
 
                 {{Heidelberg/panelelement|DeeProtein|https://static.igem.org/mediawiki/2017/2/2b/T--Heidelberg--2017_phage-titer-logo.png|https://2017.igem.org/Team:Heidelberg/Software/DeeProtein|
 
                 We apply deep learning models to harness the complex sequence to function relation in proteins. |Explore
 
                 We apply deep learning models to harness the complex sequence to function relation in proteins. |Explore

Revision as of 09:26, 1 November 2017


AiGEM
Artificial intelligence for Genetic Evolution Mimicking
abstract
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DeeProtein

We apply deep learning models to harness the complex sequence to function relation in proteins.

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GAIA

By interfacing our trained models with a genetic algorithm we developed an in silico evolution tool.

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SafteyNet

To perform self-checks, and prevent misuse of directed evolution techniques, we developed SafetyNet a sensitive tool for the detection of harmful traits in sequences.

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Validation

Deploying GAIA, we fully in silico evolved a beta lactamase and reprogrammed the E. Coli beta glucuronidase towards beta galactosidase function.

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MAWS 2.0

Description