Biotech Company Forges Path to High Performance Healthcare

By Michael Feldman

July 6, 2011

GNS Healthcare is one of those companies that wouldn’t have existed in the 20th century. It promotes itself as “a healthcare IT company that applies technology to optimize patient treatment.” As such, GNS is at the forefront of a new era of drug development and delivery that is moving personalized medicine from theory into practice.

Headquartered in Cambridge, Massachusetts, GNS is the brainchild of Cornell physicists Colin Hill and Iya Khalil, who founded the original company in 2000, under the name Gene Network Sciences. Hill is now the CEO and president of GNS and Khalil is the company’s Executive VP. Their idea was to exploit supercomputing technologies, in the form of “big data” analytics, to identify genetic biomarkers for drug efficacy.

Such an approach requires the ingestion of large volumes of genetic and clinical data, along with lots of data-intensive processing, both of which were expensive propositions a decade ago. In 2000, a modest-sized cluster with a few dozen processors would take a year to analyze a person’s genetic profile.

But technology has caught up to GNS’ aspirations. Thanks to cheaper DNA sequencing technologies to generate the raw data and much more powerful (and less expensive) high performance computing systems to process it, such analytics is now within the reach of commercial firms. Hill believes supercomputing, in particular, will enable advances in drug R&D that would otherwise have been impossible.

Much of that advancement is wrapped around the idea of personalized medicine. One of its principle tenets is to better match drugs to an individual’s genetic makeup in order to make treatments safer and more effective. These compounds work at the molecular level and because even small genetic variations can produce big differences in a person’s physical makeup, drug efficacy can vary significantly from one person to another. In a nutshell, the idea is to correlate these pharmaceuticals with a person’s unique molecular characteristics.

Pharmaceutical companies, healthcare providers and patients all stand to benefit from better targeted drugs since, in theory at least, it drives down costs for everyone and delivers better results. Given the public’s focus on reigning in healthcare expenses and the industry’s concern with producing lawsuit-free drugs that will survive long enough to recoup development investments, a technology that delivers on both fronts would be welcome indeed.

To that end, GNS has developed a software platform that is able to analyze how genes, proteins and drugs interact in a virtual model. Dubbed Reverse Engineering/Forward Simulation (REFS), the software uses HPC clusters, or in some cases bona fide supercomputers, to sift through the data and figure out how all the bio-bits fit together.

In essence, the GNS software delivers a virtual clinical trial. But instead of taking millions of dollars and years to accomplish, the simulated version can be executed for a fraction of the cost in weeks or even just days. No one is ever put at risk, and there are no waivers to sign.

To accomplish this in silico, REFS creates a system interaction model of all the components represented by the data (reverse engineering) and then uses billions of queries (forward simulation) to reveal the most important genes and proteins driving those interactions. Importantly, it can also predict interactions for “what if” scenarios.

The technology was interesting enough to get the attention of DARPA, the US Department of Defense’s research arm, which funded a case study on the GNS work. The effort, in collaboration with the Council on Competiveness, was part of a project to demonstrate the business case for high-end modeling and simulation technologies. This particular case study focused on a recent GNS collaboration with drug R&D specialist Biogen Idec.

The work with Biogen was to build a computational model for identifying novel drugs for rheumatoid arthritis sufferers. Today about a third of arthritis patients do not respond to the most commonly used anti-inflammation therapies (anti-TNF drugs). Since 1 to 2 percent of the world’s population suffers from this condition, there is a lot of interest in developing more effective treatments.

The project with Biogen involved sifting through the genetic data from 70 arthritis patients to look for single nucleotide polymorphisms (SNPs), which are short sequences of DNA in which a nucleotide base in the sequence has been is altered. Gene expression data from the patients’ blood as well as clinical information like pain levels, swollen joints, and other blood markers, were also encapsulated. Models were built from this data, which could then subsequently be used to conduct simulations with different drug compounds.

The data- and compute-intensive nature of the process is hard to fathom. Although only 70 patients were evaluated, it involved correlating hundreds of thousands of genetic variables on top of numerous clinical variables for each patient. Trillions of models were then constructed against each dataset. For example, REFS can simulate the “knock-down” of an individual gene by a certain drug, and then evaluate the result. With so many genes in the mix, the combinations can quickly escalate.

This was the first time a computer model of rheumatoid arthritis was developed that could be used to test new drugs and target pathways for individual patients. And it’s not just that they’ve replace clinical trials with virtual ones. The sheer number of combinations that can be tested, not to mention the ability to virtualize risky drug scenarios means these simulations can go far beyond clinical testing. You just need enough computing horsepower make it work.

From Hill’s perspective, the key technology to move this technology forward is high performance computing. “We have this strong conviction that the major game-changing advances in the biomedical sciences, drug development and patient care will not occur on a short time-scale without the extreme use of supercomputing,” he says.

GNS itself has only a modest HPC setup, but its computational demands are nearly insatiable. Much of the time it uses big machines like IBM Blue Gene supercomputers (on-demand) and larger clusters from its partners. Besides Biogen Idec, Johnson & Johnson and Pfizer have teamed with GNS on other drug R&D projects, and the company is also engaged with a number of academic and non-profit research organizations.

If solutions like that from GNS deliver on their promise, they will have arrived in the nick of time. Skyrocketing labor and drug development costs and aging populations are straining healthcare delivery in much of the developed world. For less economically fortunate nations, 21st century healthcare is simply out of reach. For both rich and poor, the era of personalized medicine can’t happen too soon enough.

Subscribe to HPCwire's Weekly Update!

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

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" process Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This