Making Trust and Collaboration a Unified Force in Science

Try to recall all the superhero movies you have watched. Many of us would agree that the films which are most captivating are those where superheroes collaborate as a team to defeat a near invincible villain – like in The Avengers. When there is collaboration, there is trust. Dr. Douglas Fridsma, President and CEO of the AMIA (American Medical Informatics Association) mentioned a phrase in a panel discussion we were on in 2012 that stuck with me. “Information moves at the speed of trust.” And, trust is at the heart of any collaboration. New forms of trust and collaboration networks have been forming since 2008, and the “bitcoin” is a great example of this. The “BlockChain” method behind bitcoin, discussed in an article published by The Economist and illustrated in the figure below is a new approach to trust and collaboration.

This figure illustrates the "BlockChain" method behind bitcoin.

Scientists are our Modern-day X-Men

Taking this parallel and comparing it to the sphere of scientific research – notably in biomedical sciences, this is an area where breakthroughs can deliver better health outcomes for mankind – like superheroes do, but only if scientists have the means of working together. All humans are continuously mutating; and I’d like to think that scientists are our modern day X-Men (and Women)!

The two most exciting disruptions in science recently are Synthetic Biology advances and the CRISPR gene editing enzyme system. Both of these innovations have enormous implications for biomedical sciences and the future of healthcare advancements.

Recently, a young girl in London with Leukemia was treated with Gene Editing Therapy. This case used a gene editing enzyme system called TALEN. This is but one of the many leaps that have been made possible through collaborative scientific research.

Fueling and Steering Scientific Research

Research is fueled by data. Discoveries are steered by the management of data. Even superheroes like Iron Man and The Incredible Hulk need some form of cognitive direction to focus their superhuman powers in order to achieve a common desired outcome. To build trust and collaboration frameworks, we need a single logical container of data. And this is why EMC has the Data Lake concept: a multi-user, multi-protocol, multi-application container for data which is geo-aware and secure.

We know that research is ultra-data intensive. To implement Precision Medicine at population health scale, there are two pivots: Collaboration and Asia. The Malaysia Genome Institute (MGI) engages in national and international collaborative projects in comparative genomics and genetics, structural and synthetic biology, computational and systems biology, and metabolic engineering. When MGI does DNA sequencing, whole genome sequencing, whole transcriptome sequencing, and targeted sequencing, a single run generates 13 terabytes of data. That’s equivalent to over 2.6 million songs in your iPod.

Being able to discover insights through large chunks of data is what differentiates progress from stalemate for the institution and its partners. MGI had a problem. As MGI increased its storage capacity to cope with the influx of research data, data processing speed decreased, which slowed down analysis work.

That was before MGI adopted EMC Isilon’s scalable on demand storage solution with its fast next-generation sequencing architecture. With the added benefit of having data access provided directly to users, this has also curbed the problem of bottlenecks within workflows and ensured ease of collaboration.

Read the MGI Case Study to learn more.

Tools for Teamwork in Research

Singapore’s Agency for Science, Technology and Research (A*STAR) is a single agency that oversees 14 biomedical sciences, physical sciences, and engineering institutes as well as six consortia and centers.

So how does A*STAR encourage collaboration amongst scientists housed in different institutions?

There were two key issues A*STAR needed to address. One, sharing of data between institutions was done manually by researchers, who had to make a copy to transfer it to another party. It was both time consuming and wasteful in terms of storage due to the duplication of data within localized machines.

Two, long procurement periods – three to nine months – meant A*STAR didn’t have the means to scale up storage when the demand called for it. The opportunity cost was great.

Following the deployment of a comprehensive EMC Isilon platform, all that changed. Atop the increase in usable capacity with an option to scale on demand, researchers could now assign their data to a central storage, which could be shared within and across research institutes.

Says Lai Loong Fong, Director, Computational Resource Centre at A*STAR. “Users have been receptive to the new model. They are looking forward to the new features we can offer them to provide greater flexibility in accessing research data through their mobiles or laptops when they are working and meeting outside of the labs. It’s another way we can support innovation and collaboration across all of our research disciplines.”

Read the A*STAR Case Study to learn more.

Subject Data Protection

According to DOE Human Subjects Resources, the use of humans as research subjects has aided significant scientific discoveries such as the Human Genome Project. That being said, given that one’s genome contains personal health and other privy information, there needs to be measures in place to protect each subject’s privacy and prevent the loss of information. There are Ethical, Legal and Social Implication (ELSI) issues which can be resolved by trust and collaboration, as published by the Genome Law Review.

Looking at A*STAR as an example again, the agency has incorporated EMC Isilon SnapshotIQ into their platform which offers data protection through secure inbox snapshots and access to near-immediate, on-demand snapshot restores.

The sum of many great minds can achieve much greater things than the sum of one. And even greater still, scalable data storage on the cloud now makes it possible for great minds to work together, regardless of where they are. We can only begin to imagine what our modern-day science X-Men would be able to actualize in these new dynamic and secure collaborative environments.

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About the Author: Sanjay Joshi

Sanjay Joshi is Industry CTO Healthcare at the Dell Global CTO Office. Based in Seattle, he has spanned the gamut of life-sciences from clinical and biotechnology research to healthcare informatics to medical devices. A "skunkworks" engineer and informaticist, he defines himself as a "non-reductionist" with a "systems view of the world.” His current focus is a systems-level understanding of Healthcare, Genomics, Proteomics, Microbiomics, Imaging and IoT processes, and data infrastructures. Recent experience has included AI platforms, data management and instruments for Electronic Medical Records; Proteomics and Flow Cytometry; FDA and HIPAA verification and validation; Lab Information Management Systems (LIMS); Translational Genomics research and Imaging. Sanjay holds a patent in multi-dimensional flow cytometry analytics. He began his career developing and building X-Ray machines. Sanjay was the recipient of a National Institutes of Health (NIH) Small Business Innovation Research (SBIR) grant and has been a consultant or co-Principal-Investigator on several NIH grants. He is actively involved in non-profit biotech networking and educational organizations in the Seattle area and beyond. Sanjay holds a Master of Biomedical Engineering from the University of New South Wales, Sydney and a Bachelor of Instrumentation Technology from Bangalore University. He completed several medical school and PhD level courses (in Sydney and Seattle). A list of selected recent invited talks and panels: • Next Generation Bioinformatics & Biotech Conf, Oct 2019: Mumbai India, Keynote, “Time Series, Machine Learning and the Microbiome: A summary” • GratiFi Summit, Jul 2019, Seattle WA, Panelist, “AI in Biotechnology.” • 601 Club, Jun 2019, Seattle WA, Moderator, “Artificial Intelligence and the Future of Health.” • Bio2Device & Silicon Vikings, Apr 2019, Palo Alto CA, Panelist, “Digital Health.” • BioIT World West, Mar 2019, San Francisco CA, Chair and Speaker, “Streamed Postcards from the Edge: Medical Device Architectures.” • Data Day Texas, Jan 2019: Austin TX, “Morals from a Type 2 Diabetes dataset analytics journey.” • Global AI Conference, Jan 2019: Santa Clara, CA, “Medical Device Architectures: Machine Learning on Streams” • Next Generation Bioinformatics & Biotech Conf, Oct 2018: Jaipur India, Keynote, “A Machine Learning Operational Analytics Story” • EPPICGlobal conference, Oct 2018, Burlingame CA, “Digital Health Keynote Panel” • AI in Healthcare Summit, Jun 2018: San Francisco CA, Chair and Panelist, “Executive Physician Roundtable” • BioIT World, May 2018, Boston MA, Chair and Speaker, Machine Learning and Data Science track • Medical Imaging in Clinical Research, Feb 2018 San Francisco CA; Speaker “Operational Imaging in Clinical Trials.” • AI in Healthcare Summit, Jan 2018 Boston MA: Keynote Panel and Genomics AI moderator • Kaiser Permanente Machine Learning Day, Dec 2017 Oakland CA: Panelist on AI in Healthcare • Interface Summit, Oct 2017, Vancouver Canada; Speaker “Pain: can AI shine a light on it?” • MinneAnalytics HALICON; Oct 2017, Minneapolis MN; Speaker “Two use-cases and a summary: Diabetes and Communicable Disease.” • mHealth Israel; Sep 2017, Jerusalem, Israel; Speaker “AI in Health: Hope or Hype?”