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.”
The Latest on: Machine learning predictions
via Google News
The Latest on: Machine learning predictions
- Machine learning-assisted molecular design for high-performance organic photovoltaic materialson November 19, 2019 at 7:15 am
Lingxian Meng et al. Organic and solution-processed tandem solar cells with 17.3% efficiency, Science (2018). DOI: 10.1126/science.aat2612 Wenbo Sun et al. Machine learning–assisted molecular design ...
- I still hate New Year’s cloud predictions, but here are mineon November 19, 2019 at 3:10 am
PR people are sharing their client’s deeply profound predictions for cloud computing in 2020, with the hope that I’ll take note. The large majority are along the lines of “Cloud computing will ...
- Patent Application Titled “Health Insurance Cost Prediction Reporting Via Private Transfer Learning” Published Online (USPTO 20190333155)on November 18, 2019 at 3:13 pm
Health insurance cost data may be noisy and may require advanced analytics like machine learning techniques to make future cost predictions with a reasonable amount of accuracy. Features that ...
- Goldman Sachs’ misguided World Cup predictions could provide clues to the Apple Card controversyon November 15, 2019 at 4:18 pm
For example, in 2018 when Goldman wanted to show off its quantitative prowess by forecasting the winner of the soccer World Cup, its researchers turned to machine learning. They could have used basic ...
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctionson November 15, 2019 at 2:22 am
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate ...
- Machine Learning as a Service (MLaaS) Market : Industry Development Scenario and Forecast to 2023on November 15, 2019 at 12:46 am
Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can ...
- ActiveCampaign Unveils Predictive Content, Leverages Machine Learning to Predict Email Engagementon November 14, 2019 at 5:49 am
To share a press release or news update, please email our Features Editor, Ameya at: [email protected] This is ActiveCampaign’s second Predictive product powered by its machine learning ...
- Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Predictionon November 14, 2019 at 2:58 am
Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. Adding participant data did not improve the algorithms ...
- New AirBnB User Booking Prediction — Using Machine Learningon November 13, 2019 at 2:06 am
Data has became the most valuable commodity in the 21st century. In this excerpt, I want to use machine learning to predict new AirBnb user’s behavior to classify if the user will make a booking ...
via Bing News