Leveraging machine learning and automation to make synthetic biology predictable

Hector García Martín

doi:10.1117/12.2571482

20 August 2020 Leveraging machine learning and automation to make synthetic biology predictable

Hector García Martín

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691D (2020) https://doi.org/10.1117/12.2571482
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

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 medical drug or invade this type of cancer cell). 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 for the benefit of society.

Conference Presentation

Citation Download Citation

Hector García Martín "Leveraging machine learning and automation to make synthetic biology predictable", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691D (20 August 2020); https://doi.org/10.1117/12.2571482

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available