Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide …

Synthetic biology, like artificial intelligence (AI) machine learning, is a relatively modern field that applies emerging technologies to achieve innovation. Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) in California merged the two fields by creating a machine learning algorithm for synthetic biology called ART (Automated Recommendation Tool), and published their study a few weeks ago in Nature Communications. Synthetic biology includes the design and formation of novel biological systems or components, and the redesign and production of natural biological systems. Many industries benefit from advances in synthetic biology. Examples include cosmetics, pharmaceutical drugs, vaccines, food and beverage, consumer products, agriculture, delivery plasmids, BioBrick parts, synthetic cells, bioinformatics, DNA synthesis, gene editing, oligonucleotides, chemicals, synthetic genes, and health care.

Tijana Radivojevic (left) and Hector Garcia Martin working on mechanical and statistical modeling, data visualizations, and metabolic maps at the Agile BioFoundry last year. If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine – both products that are "grown" in the lab – then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically.

If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine--both products that are "grown" in the lab--then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell's DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc non systematic engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool ( ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated and real data sets and discuss possible difficulties in achieving satisfactory predictive power. 2 Introduction Metabolic engineering 1 enables us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels 2,3 or anticancer drugs.