Scientists use technique to automatically predict the amount of biofuel produced by microbes
Scientists from the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel.
Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells.
The new approach is much faster than the current way to predict the behavior of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.
The research was published May 29 in the journal Nature Systems Biology and Applications.
In biology, a pathway is a series of chemical reactions in a cell that produce a specific compound. Researchers are exploring ways to re-engineer pathways, and import them from one microbe to another, to harness nature’s toolkit to improve medicine, energy, manufacturing, and agriculture. And thanks to new synthetic biology capabilities, such as the gene-editing tool CRISPR-Cas9, scientists can conduct this research at a precision like never before.
“But there’s a significant bottleneck in the development process,” said Hector Garcia Martin, group lead at the DOE Agile BioFoundry and director of Quantitative Metabolic Modeling at the Joint BioEnergy Institute (JBEI), a DOE Bioenergy Research Center funded by DOE’s Office of Science and led by Berkeley Lab. The research was performed by Zak Costello (also with the Agile BioFoundry and JBEI) under the direction of Garcia Martin. Both researchers are also in Berkeley Lab’s Biological Systems and Engineering Division.
“It’s very difficult to predict how a pathway will behave when it’s re-engineered. Trouble-shooting takes up 99% of our time. Our approach could significantly shorten this step and become a new way to guide bioengineering efforts,” Garcia Martin added.
The current way to predict a pathway’s dynamics requires a maze of differential equations that describe how the components in the system change over time. Subject-area experts develop these “kinetic models” over several months, and the resulting predictions don’t always match experimental results.
Machine learning, however, uses data to train a computer algorithm to make predictions. The algorithm learns a system’s behavior by analyzing data from related systems. This allows scientists to quickly predict the function of a pathway even if its mechanisms are poorly understood — as long as there are enough data to work with.
The scientists tested their technique on pathways added to E. coli cells. One pathway is designed to produce a bio-based jet fuel called limonene; the other produces a gasoline replacement called isopentenol. Previous experiments at JBEI yielded a trove of data related to how different versions of the pathways function in various E. coli strains. Some of the strains have a pathway that produces small amounts of either limonene or isopentenol, while other strains have a version that produces large amounts of the biofuels.
The researchers fed this data into their algorithm. Then machine learning took over: The algorithm taught itself how the concentrations of metabolites in these pathways change over time, and how much biofuel the pathways produce. It learned these dynamics by analyzing data from the two experimentally known pathways that produce small and large amounts of biofuels.
The algorithm used this knowledge to predict the behavior of a third set of “mystery” pathways the algorithm had never seen before. It accurately predicted the biofuel-production profiles for the mystery pathways, including that the pathways produce a medium amount of fuel. In addition, the machine learning-derived prediction outperformed kinetic models.
“And the more data we added, the more accurate the predictions became,” said Garcia Martin. “This approach could expedite the time it takes to design new biomolecules. A project that today takes ten years and a team of experts could someday be handled by a summer student.”
Receive an email update when we add a new MACHINE LEARNING PREDICTIONS article.
The Latest on: Machine learning predictions
via Google News
The Latest on: Machine learning predictions
- Take A Shot Every Time One Of Our Business Trend Predictions For 2019 Comes True on December 16, 2018 at 8:07 pm
Some of these predictions are already en route to being fulfilled ... A.I. To Enter The Mainstream As the world becomes more automated, our reliance on machine learning will continue to skyrocket. All ... […]
- AI In 2019 According To Recent Surveys And Analysts' Predictions on December 15, 2018 at 6:00 pm
Artificial Intelligence (AI) is the talk of the world and it features prominently in predictions for 2019 (see here and ... Only 18% of senior leaders plan to increase investment in AI and machine lea... […]
- SoftServe Achieves Machine Learning Specialization in Google Cloud Partner Program on December 14, 2018 at 4:00 am
Specifically, the Machine Learning Specialization demonstrates SoftServe’s expertise in data exploration, preprocessing, model training, model evaluation, model deployment, online prediction ... […]
- AI and Machine Learning: 9 Predictions for 2019 on December 13, 2018 at 11:52 am
In 2018, artificial intelligence and machine learning took center stage and in 2019, the emphasis is expected to increase on cognitive computing technologies that can analyze data in ways previously u... […]
- Juniper Research: Top Ten Tech Predictions for 2019: Adversarial Learning, AI at the EDGE and Banking-as-a-Service Head List on December 13, 2018 at 1:25 am
with each prediction explained in more detail in the free report and slide set available to download from the Juniper Research website, while a commentary on the slides can also be accessed here. 1. A... […]
- Predictions 2019: How AI, Machine Learning Continue to Impact Us on December 12, 2018 at 10:18 am
PREDICTIONS 2019: With the new year only weeks away, we present some ideas from various industry executives about what new impacts they believe AI and machine learning will be making on the IT busines... […]
- Machine Learning, EHR Data Predict High-Risk Surgical Patients on December 12, 2018 at 6:39 am
In addition to outperforming human experts in predicting high-risk patients, the machine learning models surpassed the National Surgical Quality Improvement Program (NSQIP) calculator, which is curren... […]
- 3 Big Mobile Data Predictions For 2019 Worth Watching on December 12, 2018 at 2:32 am
Still, if predictions pan out, big data and analytics revenues ... Now, Google will integrate your web data with the Maps app. For You employs a machine learning function called “Your Match,” which an... […]
- Machine learning adoption thwarted by lack of human skills on December 11, 2018 at 3:35 pm
We’ve all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; ... […]
via Bing News