HPC Unlocks Secret to Drought-Resistant Crops

April 20, 2017

This network shows the cross-species co-expression relationships between genes in Arabidopsis and Agave. Dark green nodes represent Agave genes, light green nodes represent Arabidopsis genes, blue edges represent positive co-expression relationships, and red edges represent negative co-expression relationships. The co-expression network was used in the paper to investigate the co-expression relationships of genes within the same gene family.

OAK RIDGE, Tenn., April 20, 2017 — A multi-institution research team has used supercomputing to understand processes leading to increased drought resistance in food and fuel crops.

Photosynthesis, the method plants use to convert energy from the sun into food, is a ubiquitous process many people learn about in elementary school. Almost all plants use photosynthesis to gather energy and stay alive.

Not all photosynthetic processes are the same, though. In recent years, researchers have grown increasingly interested in desert plants’ preferred method of photosynthesis—crassulacean acid metabolism (CAM), a process named after the Crassulaceae family of plants, which include succulents like friendship plants, pig’s ears, and hens and chicks.

These plants caught researchers’ attention because of their seemingly opposite photosynthetic schedule, and understanding this process may be the genetic key to helping plants of all kinds conserve water. With a more fundamental understanding of CAM, scientists aim to help the plants upon which society relies for food and fuel become more drought resistant, thereby expanding the area where crops can grow and thrive.

“One of the benefits of CAM photosynthesis is water efficiency,” said Oak Ridge National Laboratory (ORNL) computational biologist Dan Jacobson, who is part of a multi-institutional team that recently published a CAM study in Nature Plants. “When you think of bioenergy and food crops, you want them to be able to tolerate drought stress or grow in areas that aren’t currently arable land. That means they have to be able to withstand some kind of environmental stress, most commonly drought stress. CAM species are very good at this.”

To that end, Jacobson works with a large group of experimentalists and computational scientists to more fully understand the CAM process. This cross-omics team (combining expertise in metabolomics, proteomics, and genomics) uses computing resources at the Oak Ridge Leadership Computing Facility (OLCF)—a US Department of Energy Office of Science User Facility located at ORNL—to catalog how plants’ CAM processes vary and ultimately uncover how CAM processes may be genetically engineered into feed stock, food crops, and crops for bioenergy applications.

Shining a light on photosynthesis

When most people think of photosynthesis, they are actually thinking of a specific form, called C3 photosynthesis. This process follows the Calvin Cycle, in which plants capture light energy during the day and convert it into energy-bearing adenosine triphosphate (ATP).

ATP helps plants split water atoms into their hydrogen and oxygen constituent particles. Meanwhile, a C3 photosynthetic plant opens up small pores—called stomata—to absorb carbon dioxide from the atmosphere. Then at night, the newly freed hydrogen particles combine with carbon dioxide absorbed during the day to create the carbohydrates plants use to live and grow.

CAM photosynthesis works the same way, but stomata open for respiration at night and stay tightly closed during the day, allowing plants to conserve more water. This helps plants like cactus and Agave survive in climates where water is scarce.

Less than 10 percent of known plant species use this specialized form of photosynthesis, but researchers hope that by understanding how CAM works, they can apply this water-saving method to other plants. To do that, though, researchers need to understand how molecules interact during CAM photosynthesis and how metabolites and proteins change over time.

Data-intensive design

In addition to simulating processes too dangerous or complex for experiments, supercomputers also help scientists make connections in vast amounts of data. For this project, researchers from ORNL, the University of Tennessee, Newcastle University in the United Kingdom, and the University of Nevada, Reno gathered photosynthesis data from Agave (a CAM plant) and compared it with the Arabidopsis genus of plants (C3 plants). To conduct a study between Agave and a C3 plant, the team selected the Arabidopsis genus plant thale cress, one of the first plants to have its genome sequenced and a good candidate for plant studies.

The team then studied what gene expressions control stomata opening and closing in both CAM and C3 plants and how proteins regulated this process. Collecting this data in both a common CAM and a C3 species allowed the team to distinguish traits ubiquitous to CAM plants from species-specific traits. However, finding these connections required a machine capable of comparing large data sets against themselves.

Jacobson and his collaborators used the OLCF’s Eos analysis cluster to run “all-versus-all” comparisons of the team’s data sets. These comparisons scan large data sets and compare each individual plant’s data with all others. This helps the team form relationships between the metabolic processes underpinning CAM in individual Agave specimens as well as the differences between Agave’s CAM properties from thale cress’s C3 properties.

“These all-against-all vector comparisons for correlation networks allowed us to look for different types of patterns and different times of day where the [gene expression] transcripts are correlated with each other, where they were correlated to proteins or metabolites, or times of the day where they shift dramatically,” Jacobson said.

The team members gained access to OLCF resources through the OLCF’s Director’s Discretionary program, and after familiarizing themselves with Titan’s hybrid architecture, they plan to expand research into other CAM species, comparing larger data sets and more fully cataloging CAM processes. “As we gain more knowledge from these various approaches, we hope to tease apart the underlying mechanisms for CAM and how it is regulated,” Jacobson said. “That starts to build toward having enough knowledge to deploy CAM in a new species.”

Jacobson also indicated that without access to high-performance computing, the team would not have been able to find these meaningful connections in a timely manner. “This is the first study looking at a cross-omics, time-course experiment to try and explore CAM at this molecular detail,” he said. “I think the ability to use supercomputing infrastructure enabled things that wouldn’t have been possible otherwise. We were able to have a pretty big impact on the analysis of this work because of those resources.”

Related Publication: P. Abraham, H. Yin, A. Borland, D. Weighill, et al., “Transcript, Protein, and Metabolite Temporal Dynamics in the CAM Plant Agave.” Nature Plants 12, no. 2 (2016): 1–10, doi:10.1038/nplants.2016.178.

About Oak Ridge National Laboratory

Oak Ridge National Laboratory is supported by the US Department of Energy’s Office of Science. The single largest supporter of basic research in the physical sciences in the United States, the Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.


Source: ORNL

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire