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Primeur weekly 2018-06-04

Focus

ECMWF's scalability programme and co-design effort result in proposal for "Extreme Earth" Flagship Initiative ...

Exascale supercomputing

Black holes from an exacomputer ...

Quantum computing

Paradoxically, environmental noise helps preserve the coherence of a quantum system ...

The right squeeze for quantum computing ...

Time crystals may hold secret to coherence in quantum computing ...

Focus on Europe

Lithuania to join EuroHPC ...

ENEA has inaugurated in Portici the most powerful supercomputer in the South of Italy ...

Slovenian-Chinese Supercomputing Lab launched in Ljubljana ...

Hardware

NVIDIA Isaac launches next-gen robotic systems to be enabled by Jetson Xavier computer and Isaac robotics software ...

NVIDIA introduces HGX-2, fusing HPC and AI computing into unified architecture ...

Acer announces new servers powered by NVIDIA Tesla GPUs at GTC Taiwan 2018 ...

Supercomputer astronomy: the next generation ...

Novel insulators with conducting edges ...

Building nanomaterials for next-generation computing ...

Supermicro shows industry's first scale-up AI and machine learning systems based on the latest generation CPUs and NVIDIA Tesla V100 with NVLink GPUs for superior performance and density ...

Applications

RevolutionInSimulation.Org public web portal launched to support simulation for everyone ...

NVIDIA and Taiwan's Ministry of Science and Technology unveil collaboration to supercharge AI efforts ...

High school students to have STEM-focused summer at OSC ...

SDSC's Comet provides computing power to model mechanism critical to maintain stability of DNA ...

Second open call for Application Experiments - Up to 60K euro funding, technical and business coaching available to support European companies to develop smart applications ...

New machine learning approach could accelerate bioengineering ...

Supercomputers provide new window into the life and death of a neutron ...

The Cloud

EU ministers endorse Commission's plans for European Open Science Cloud ...

Mellanox launches ground-breaking open Hyper-scalable Enterprise Framework ...

Supermicro unveils 2 PetaFLOPS SuperServer based on new NVIDIA HGX-2, the world's most powerful Cloud server platform for AI and HPC ...

New machine learning approach could accelerate bioengineering


A new approach developed by Zak Costello (left) and Hector Garcia Martin brings the the speed and analytic power of machine learning to bioengineering. Credit: Marilyn Chung at Berkeley Lab.
1 Jun 2018 Berkeley - Scientists from the Department of Energy's Lawrence Berkeley National Laboratory 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 toE. colibacterial 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 is published May 29 in the journal npj 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", stated Hector Garcia Martin, group lead at the DOE Agile BioFoundry and director of Quantitative Metabolic Modelling 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", Hector 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 behaviour 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 toE. colicells. 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 variousE. colistrains. 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", stated Hector 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 work was part of the DOE Agile BioFoundry, supported by DOE's Office of Energy Efficiency and Renewable Energy, and the Joint BioEnergy Institute, supported by DOE's Office of Science.

Source: DOE/Lawrence Berkeley National Laboratory

Back to Table of contents

Primeur weekly 2018-06-04

Focus

ECMWF's scalability programme and co-design effort result in proposal for "Extreme Earth" Flagship Initiative ...

Exascale supercomputing

Black holes from an exacomputer ...

Quantum computing

Paradoxically, environmental noise helps preserve the coherence of a quantum system ...

The right squeeze for quantum computing ...

Time crystals may hold secret to coherence in quantum computing ...

Focus on Europe

Lithuania to join EuroHPC ...

ENEA has inaugurated in Portici the most powerful supercomputer in the South of Italy ...

Slovenian-Chinese Supercomputing Lab launched in Ljubljana ...

Hardware

NVIDIA Isaac launches next-gen robotic systems to be enabled by Jetson Xavier computer and Isaac robotics software ...

NVIDIA introduces HGX-2, fusing HPC and AI computing into unified architecture ...

Acer announces new servers powered by NVIDIA Tesla GPUs at GTC Taiwan 2018 ...

Supercomputer astronomy: the next generation ...

Novel insulators with conducting edges ...

Building nanomaterials for next-generation computing ...

Supermicro shows industry's first scale-up AI and machine learning systems based on the latest generation CPUs and NVIDIA Tesla V100 with NVLink GPUs for superior performance and density ...

Applications

RevolutionInSimulation.Org public web portal launched to support simulation for everyone ...

NVIDIA and Taiwan's Ministry of Science and Technology unveil collaboration to supercharge AI efforts ...

High school students to have STEM-focused summer at OSC ...

SDSC's Comet provides computing power to model mechanism critical to maintain stability of DNA ...

Second open call for Application Experiments - Up to 60K euro funding, technical and business coaching available to support European companies to develop smart applications ...

New machine learning approach could accelerate bioengineering ...

Supercomputers provide new window into the life and death of a neutron ...

The Cloud

EU ministers endorse Commission's plans for European Open Science Cloud ...

Mellanox launches ground-breaking open Hyper-scalable Enterprise Framework ...

Supermicro unveils 2 PetaFLOPS SuperServer based on new NVIDIA HGX-2, the world's most powerful Cloud server platform for AI and HPC ...