Leveraging Machine Learning & Automation to Systematically Guide Synthetic Biology
Hector Garcia-Martin | Berkeley National Lab
Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology leverages engineering approaches to produce biological systems to a given specification (e.g., produce x grams of a biofuel). In this effort, new tools are now available that promise to disrupt this field: from CRISPR-enabled genetic editing, to high-throughput omics phenotyping, and exponentially growing DNA synthesis capabilities. However, our inability to predict the behavior of bioengineered systems hampers synthetic biology from reaching its full potential.
We will show how the combination of machine learning and automation enables the creation of a predictive synthetic biology able to tackle current human challenges, such as climate change. We will show examples of recommendations of molecules, pathways, promoters, and media composition, and automation through microfluidics.
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