This article was originally written by Avanade alumn Michael Isaac.

When Ernest Fraenkel, professor of biological engineering at MIT, stepped to the front of the room, he was addressing his peers, some of the top medical researchers and data analysts in the world.

“I want to be sure everyone understands why we are here today,” he said. “We are here to cure ALS. Everything we do is about eradicating this disease.”

So began five hours of intense dialogue within the framework of a design-led workshop for one specific purpose: to create a technical solution to simplify and accelerate research collaboration into one of the cruelest diseases known to humankind.

Amyotrophic lateral sclerosis, commonly known as ALS or Lou Gehrig’s disease, is a progressive motor neuron disease that slowly robs patients of their ability to move, speak and even, in the end, breathe. Estimates are that as many as 400,000 people worldwide are living with ALS. There are no effective treatment protocols and very little is known about its cause.

Workshop attendees were participants in Answer ALS, the world’s largest coordinated and collaborative ALS research project. The one-of-a-kind consortium of medical research centers, leading technology companies and 1,000 ALS patients has one goal: leverage cloud computing, machine learning, an enormous amount of patient data, and a powerful, interactive data repository to determine what causes ALS, identify subgroups and develop customized treatment protocols that would allow patients to live vibrant, full lives.

Search for answers begins
Our Avanade team had convened the workshop to determine how we could best help Answer ALS accomplish its first-phase technology goal: create a Microsoft® Azure-based system that would be used to hold, synthesize and analyze the trillions of bits of information the research project would amass. What we didn’t realize at the time was that this system may become a template for future research into other motor neuron diseases, such as Alzheimer’s, Parkinson’s, multiple system atrophy and more.

The first thing I wanted to do that day was break down the walls between technology and academics, so I asked these world-renowned scientists what they did for fun. I admit, I saw a few eye rolls. But it is a pretty typical ice breaker, and it seemed to get things started.

Then I brought out the sticky notes.

Over the next four or five hours, we used the principles of design-led thinking, a way of identifying, defining and prioritizing objectives, to create a roadmap for how Avanade could best use its expertise in Microsoft Azure, digital transformation, data analytics and AI to create the system that would power the researchers’ work.

The results surprised everyone, especially me.

Building an engine that could: answers in hours, not weeks
The Avanade team went into the workshop thinking we would build a place for Answer ALS researchers to protect and access both raw and processed sequencing files that we then could put into a format that could be shared. It turns out, that’s not what they needed most. Participants said they could build the repository. What they needed was a QBD engine.

A “query by data engine” would allow researchers to submit a query to the database and rapidly receive an accurate answer back.

“Rapidly” is key. Typically, researchers submit a query and then theoretically could go on vacation, because it would take four or five days to process. Answer ALS researchers wanted to create an interface that would answer a query in hours, not days or weeks.

Using the workshop’s prioritized outputs and an agile methodology, we created that engine and provided the Answer ALS team the ability to sift through large volumes of clinical and genomic data to find patterns that we all hope will translate to a new understanding of the disease and new possibilities for drug targets and therapies.

“Where they might take weeks to process in the past, these scripts now process data efficiently using powerful cloud resources and parallel Azure Batch computing,” says Barry Landin, an Answer ALS solutions architect. “The solution Avanade delivered removes nearly all of the complexity of cloud technologies so that Answer ALS teams can focus on science.”

Now researchers can comprehensively evaluate genetic information from each participating ALS patient and perform the most comprehensive biological analytics ever, combining those data with RNA, protein and epigenomic analysis of the patients’ cells to yield a personalized database of thousands of petabytes of new ALS-specific information.

Mining data for paths to breakthroughs
Powered by machine learning and big data informatics, this wealth of biological data will be mined to uncover ALS causes, subtypes, pathways gone awry and drug targets. Researchers hope it will serve as the foundation for new clinical trials, suggest new ways to subgroup patients to better discover successful drugs, and find drug-responsive biomarkers or diagnostics. Importantly, the data will be publicly available for ALS researchers around the world to access, interpret and mine for valuable insight.

It all started with a mission focused on one objective: identify treatment protocols for a devastating disease. The possibility that Answer ALS has also created a new way of conducting medical research that can serve as a template for mastering other diseases is a welcome byproduct.

Having met and worked with this community of researchers, the patients and patient advocates, I speak for the entire Avanade team: This mission energizes us. Answer ALS is making a difference, and we are proud to be part of it.

Click on over to read the Answer ALS client story and the interview with Emily Baxi, Ph.D., executive director of the Robert Packard Center for ALS Research at Johns Hopkins.