Ari Berman, CEO of BioTeam, has a diverse background in science and technology. In a recent Data Unchained podcast interview with host Molly Presley, Berman shared his views on the ways data supports scientific discovery as well as the challenges – and opportunities – stemming from the increasing diversity of data. He also discussed the next steps in global data storage.
Berman is a doctor of molecular biology and conducted 15 years of laboratory research on addiction, Alzheimer’s, and Parkinson’s disease. He also spent another five years performing computational research, using gene targets in model organisms to analyze the same targets in humans. In addition, Berman has been a designer of supercomputers for the past 30 years in support of scientific research. He continues this work at Bio Team, which he joined in 2012.
BioTeam’s mission is to increase the speed of scientific discovery by applying data strategies. Considering we’re all in a world where the population’s health is at the forefront with COVID, how does data inform driving cures forward, as well as influence things like mask mandates?
Data Technologies Become More Diverse, and More Complicated
“What’s happened in the last 15 or so years in life sciences and biomedical research is that the laboratory technology used to do research and analyze disease conditions or disease-like conditions has really taken off,” Berman explains.
Data technology is also becoming more diverse in scientific research due to increasing use of computationally-intensive tasks like simulations, genomics sequencers and viral analysis, explains Berman. COVID research is using new technologies like cryo-electron microscopy, a type of electron microscopy. Supercomputing uses metrics known as hero numbers that serve as benchmarks for a particular data flow. Researchers use a concept called extract, transform and load (ETL) to transform data into a format that allows them to analyze and gain knowledge from it. However, there are about 40 standard formats, which creates a problem when trying to look at data in a larger context than just a specific research project.
“You really need to spend a lot of time harmonizing that data. And this is a big problem across everything. There’s lots of different sequencer manufacturers, and they all have their different file formats and they all have their different sample prep formats,” said Berman.
Geographical and political policies further complicate the environment. The biotechnology space needs to make data globally available, so people can base decisions on it. However, the way data are generated and the way data are treated by, say, governments, are very different across the world. Are there standards that are needed? What needs to happen with data to take this to the next level?
Global Data Availability Presents Opportunities to Drive Science Forward
Global data availability requires data generators to identify the tools that support a solution for a particular problem they’re trying to solve. Those tools could include a cloud platform, supercomputer or custom software specifically designed to solve that problem. Each question in science can be answered in multiple ways, depending on how scientific computing supports that problem. Scientists should therefore approach a solution based on outcomes, even if it’s necessary to build a new tool.
The cultural part of that is significant, said Berman. “I think one of the things we focus on that is unusual is we talk about the technology, we talk about the science. The reality is that all of it is done by people. So culture is a huge part of the problem and a huge part of what needs to be changed here.”
“It’s got to be incentivized. And you really have to pay attention to the people and creating a culture around it. That’s really where BioTeam spends its time; our expertise is in all of those spaces so that we can drive it forward. Our bigger goal is simply to really drive science forward in a way that is supported by technology so that humanity can continue to mature.”
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