Machine learning platform for predictive bioengineering
Theme:
Poster
What:
Poster
Part of:
When:
5:00 PM, Saturday 15 Oct 2022
(1 hour 30 minutes)
Breaks:
Break: Tours of Concordia Genome Foundry 05:00 PM to 07:00 PM (2 hours)
Dinner 06:30 PM to 08:30 PM (2 hours)
Dinner 06:30 PM to 08:30 PM (2 hours)
Where:
RF building - Loyola Jesuit Hall and Conference Centre (ground floor)
- RF building - Loyola Jesuit Hall and Conference Centre
Virtual session
This session is in the past.
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Enter virtual roomThrough advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems.
Mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We used a genome-scale model to pinpoint engineering targets, efficient library construction using synthetic biology, and high-throughput biosensor-enabled screening for training machine learning algorithms to enable successful forward engineering of yeast metabolism1.
Now, we have enabled these techniques via a robust implementation platform to allow efficient closed-loop optimization of biological molecules.
