Data Science and Modeling

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Hector Garcia Martin

Director of Data Science and Modeling

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Tyler Backman

Deputy Director for Data Science and Modeling

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Apostolos Zournas

Post Doctoral Researcher

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Patrick Kinnunen

Post Doctoral Researcher

Despite synthetic biology’s remarkable potential to tackle important societal problems (e.g. produce renewable biofuels), its full potential is held back by our inability to engineer biological systems predictably. While novel tools allow us to edit a cell’s genome more efficiently than ever, the effect of these edits on cell biology are challenging to predict, highlighting the need for new design strategies. 

In the Data Science and Modeling group, we use a combination of machine learning and mechanistic modeling to guide metabolic engineering and make synthetic biology a predictable endeavor. Our tools allow researchers to e.g. choose promoter combinations, genetic edits, DNA parts, and media to optimize production and enable commercially viable biofuels. We work very closely with other teams in JBEI to merge our predictive capabilities with new molecular biology tools and automation capabilities, such as microfluidics.


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