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New AI Speeds Discovery in Synthetic Biology
Berkeley Lab’s machine learning algorithm accelerates metabolic engineering.
Posted October 3, 2020 Reviewed by Kaja Perina
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.
Applied synthetic biology is used to create plant-based meat substitutes. Silicon Valley-based Impossible Foods with USD 1.4 billion in funding from a myriad of venture capitalists and private investors (including Google Ventures, Serena Williams, Bill Gates, Khosla Ventures, Katy Perry, and others per Crunchbase), is a startup that uses synthetic biology to create its plant-based burgers, pork, and sausages. Specifically, scientists at Impossible Foods extracted the DNA from soy plants and placed it into genetically engineered yeast to brew up leghemoglobin, the soy protein with similarities to heme. Heme is the key molecule found in the protein myoglobin that gives meat its unique meaty flavor essence.
Artificial intelligence (AI) is flourishing anew mostly due to deep learning which is a subset of machine learning with architecture that is somewhat inspired by the biological brain. AI is gaining momentum across a wide range of purposes such as electric vehicle batteries, extreme weather predictions, flavor and fragrances, anti-vaping and bullying detectors for schools, robotics, predicting future events, and even in e-sports.
Training AI often requires big data, and machine learning is well-suited for recognizing patterns in complex data for medical and health care industries. Uses include medical imaging, epilepsy, breath analyzer disease diagnosis, in vitro fertilization, cancer detection, precision oncology, traumatic brain injury detection, breast cancer early detection, chemotherapy, brain cancer diagnostics, opioid management in hospitals, and brain aneurysms detection.
AI machine learning is also being used in psychology and neuroscience for brain-computer interfaces, neuroprosthetics, voice-based depression detection, post-traumatic stress syndrome (PTSD), detecting anxiety and depression in children, and even preventing smartphone addiction.
In both the biotechnology and pharmaceutical industries, artificial intelligence is a tool for antibiotic discovery, antifungals drug discovery, biomarker development, and drug discovery. Given the wide adoption rate of AI machine learning, it was only a matter of time before the two fields intersect.
In the Berkeley Lab study, the research team of Hector Garcia Martin, Kenneth Workman, Zak Costello and Tijana Radivojević created a patent-pending algorithm called ART (Automated Recommendation Tool) to accelerate development through guided bioengineering, quantification of uncertainty, and access to AI machine learning techniques.
ART enables researchers to better predict outcomes by using training instances made of a set of vectors of measurements and associated system responses. The algorithm brings together various machine learning models from the scikit-learn library using a Bayesian ensemble approach in order to predict the output’s probability distribution.
As proof-of-concept, the researchers then collaborated with other scientists at the Novo Nordisk Foundation Center for Biosustainability at the Technical University of Denmark, TeselaGen Biotechnology, and their global research colleagues. Together the researchers used ART to manage the metabolic engineering process in efforts to increase tryptophan production using baker’s yeast (Saccharomyces cerevisiae) and published the joint study on September 25 in Nature Communications.
Tryptophan is an essential amino acid used for making and managing neurotransmitters, muscles, enzymes, and proteins. Tryptophan is required for normal growth in infants and is not produced by the body.
First the Danish researchers and their colleagues created a combinatorial library, a collection of chemicals or molecules synthesized by combinatorial chemistry and set up a large phenotypic dataset. Combinatorial chemistry is the chemical synthetic method that enables to produce large quantities of compounds in a single process.
"To construct a combinatorial library targeting equal representation of 30 promoters expressing five target genes, we harnessed high-fidelity homologous recombination in yeast together with the targetability of CRISPR/Cas9 genome engineering for a one-pot assembly of a maximum of 7,776 (65) different combinatorial designs," wrote the researchers.
The researchers trained ART to associate certain amino acid production with gene expression using experimental data on a small percentage, just 250 genotypes, out of the 7,776 possible combinations of biological pathways of five target genes as the input training dataset. ART extrapolated how the remaining thousands of combinations would impact tryptophan production, then produced designs to improve high tryptophan production ranked in priority.
“From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training,” wrote the researchers.
The researchers demonstrated the capabilities of machine learning to accelerate metabolic engineering. The worldwide synthetic biology market is expected to reach USD 18.9 billion with a compound annual growth rate (CAGR) of 28.8 percent during the period of 2019-2024 according to BCC Research. Artificial intelligence and synthetic biology are innovative technologies where the intersection amplifies the potential benefit to humanity in the future.
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