Sarah Harman: With a seemingly limitless capacity for the free exchange of information, ideas, culture, and technology, we have connected the globe in ways that not even yesterday’s science fiction could have accurately predicted.


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Sarah Harman: Yet, the very connections that define our modern lives also highlight one of our greatest vulnerabilities. 

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Sarah Harman: The COVID-19 pandemic serves as a stark reminder that, despite our great achievements, we are still bound by the laws of our own biology.


Sarah Harman: And while SARS-CoV-2, better known as Coronavirus, has been a tragedy on a global scale, it has also pushed the scientific community to new levels of cooperation, coordination, and discovery.


Pat Fitch: The framework, you know, of how to get the labs to work together on a bigger challenge was emerging and the catalyst that that made it urgent was the pandemic.


Sarah Harman: I’m Sarah Harman. You’re listening to Direct Current – the Department of Energy’s Podcast, and in today’s episode we explore pandemics, and how they have pushed our best science even further. 


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Sarah Harman: When we look at the typical, pre-COVID, response to an emergent pathogen, we tend to see a similar chain of events within the scientific community. Here’s Pat Fitch, who you heard in the intro. He’s the Associate Laboratory Director for Chemical, Earth, and Life Sciences at Los Alamos National Laboratory, which is part of the Department of Energy.


Pat Fitch: This was my fifth or so outbreak, I used to run a very large infectious disease lab before I came to Los Alamos. And so I was involved in SARS-1, you know, SARS, several influenza, Ebola, those kinds of outbreak MERS. And you know, what was different in those outbreaks, that is at the beginning, they don't look that different. But at some point, you know, different agencies, whether they're local and regional public health agencies, or whether the US CDC or the World Health Organization, have to start diverting resources because they can, they can look ahead and say, wow, if we if we don't start to roll this off, now, the potential impact will be so much bigger. And all the other examples predating this, by time you get the month three or so the curves are rolling over and coming back down to being a “Okay, well, thank, thank goodness, it looked like it was highly transmissible, but it wasn't as bad as we thought.”


Sarah Harman: So, why not raise the alarm when the risk of a pandemic first presents itself? Why was the scientific community reluctant to act aggressively at the very first signs of a possible pandemic?


Pat Fitch: There's an expression: when you hear hoof beats, you assume a horse, not a zebra, right? Because in general it’s a horse. I mean, at least where I live. And so that's, that's a pretty good rule of thumb, because otherwise, you can become Chicken Little and the sky is falling all the time. And so, you can't be, whether public health or in science, you can't be successful with just chasing after the possible thing.


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Sarah Harman: But it didn’t take long to become clear that this wasn’t a horse…


Pat Fitch: What was different with this pandemic and makes it much more like the 1918 influenza was that those initial responses which would have worked reasonably well for most other outbreaks did not, and some of the reasons were this was much more human to human transmissible. Fortunately, it wasn't as lethal as Ebola was it didn't kill the same percentage higher percent, which is terrific, Thank gosh. But that fact that it kept growing, and we had to leap to more and more aggressive interventions to try to get the roll off, I think, what's the big difference, and so by, by March, I think we'd already cycled through the, you know, maybe we can just use some minimal guidance, you know, make suggestions to the public travel less do this less, do that less to know to, in order to get things to not have the growth rate of infection, and, unfortunately, deaths continue to grow exponentially, we need to be much more aggressive.


Sarah Harman: The scientific community’s predisposition to wait for certainty was at odds with the need for expediency because, the longer you wait, the larger the potential for catastrophe becomes. But there were also practical barriers that made immediate action challenging. 


Pat Fitch: In this environment for the Coronavirus pandemic, time was the enemy. People knew that. But we didn't practice enough of the interfaces to make those time things shrink enough, like we did in practice, you know, what (sic) data would be curated and who would validate it, who would host it? What were the rules for using it.


Sarah Harman: So the need for certainly, while well-intentioned, becomes an impediment to timely action. 

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Pat Fitch: What we hadn't thought through is you need to be in a position to be able to use whatever groups demonstrate their assays work, start using them immediately. You don't want to wait until some normal process, you know, runs its full course to say okay, now that's 100%, curated, understood, diagnostic. And so we lost weeks, if not months on calendar, because our mental model for it was more like preparing for any other kind of simple thing.


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Sarah Harman: It quickly became clear that the old ways of conducting and implementing the science, as it related to SARS-CoV-2, was not going to cut it. A new approach was desperately needed. 


Sarah Harman: At that time, the most pressing concerns were speed and efficiency. But, with the country in lockdown, both of those needs seemed unattainable. The Department of Energy’s answer to this conundrum was the National Virtual Biotechnology Laboratory. 


Sarah Harman: In the simplest terms, the NVBL is a consortium of several DOE National labs, each bringing highly specialized tools to the fight against the COVID-19 pandemic.


Pat Fitch: We, as a team, had a few meetings going back and forth, just to discuss the zoom level, the WebEx level, what do we think is most important that we could do if we were funded to have an impact? Our goal, and this is early to mid-March, was to have projects that started within a few weeks and would end on September 30th. So whatever they were going to do was early phase impact, but still driven by a science gap. This was not let's find a clinical diagnostics lab to do more tests. This was, this was a science mission.


Sarah Harman: With a focus on speed and efficiency, 17 National laboratories sprang into action, closely coordinating on several practical problems requiring immediate, and sometimes complex solutions including testing capabilities, epidemiological and logistical support, supply chain bottlenecks, and manufacturing capabilities. Here’s Dion Antonopoulos, Division Director for Biological Science at Argonne National Laboratory.


Dion Antonopoulos: It's like it's the first time that everybody kind of put their knives down to actually, like, work together. A lot of times science can become quite competitive. And so it was nice to be able to see sort of in this particular circumstance, everybody really rallying together to try to address a pretty major issue at the time.


Pat Fitch: And this is one of the great accomplishments of the NVBL, because it was so urgent, you know, labs self-regulated, there was essentially no, you know, I want my one over Nth share of this money. And I'll tell you what I'm going to do with it kind of thinking it was all, gee, here's an idea. And the team would say, well, great, what will you have done by September 30? And if the person said, well, I'll have the initial data for paper I might be able to write, and people would say, well, that doesn't, that doesn't sound like that meets what we need. On the other hand, they said, well, I think this will impact public health's ability to consider, you know, three different types of alternate ways to collect and execute assays. So for instance, you and I can each be tested, I can run a test on each of ours, or you're not going to each have a slob taken, I could combine them and run one test. And if it's negative, I just save 50% of the cost of the tests and still have another test lab to use on another pair.


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Sarah Harman: And with each lab contributing different sets of tools and capabilities toward addressing the pandemic, the benefits of this new, multidisciplinary approach were immediate and sweeping. Here’s John Hill, Director of the National Synchrotron Light Source at Brookhaven Laboratory.


John Hill: I will add that each block has a different set of capabilities. And that was what was special about the NVBL was them coming together to combine those capabilities. So, some labs have really great light sources, some have superb computing facilities, or manufacturing facilities, capabilities, understanding, you know, advanced manufacturing, so bring those together as what was special in NVBL, kind of working together as a very coherent team to solve these problems with the nation was suddenly facing what was what NVBL was all about.


Dion Antonopoulos: A real key aspect in terms of what this type of coordination and coordinated effort played out was really allowing us within DOE, us within the national lab system to figure out ways to fast track research. It was an opportunity, in other words, not just the bureaucracy or the red tape of fast tracking research. It was really an effort of being able to get teams put together and to be able to actually demonstrate that, hey, there is this agility, there is this flexibility within the national labs system that you can draw upon multiple institutions across the US in a focused, coordinated effort.


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Sarah Harman: The impact of bringing these disparate scientific disciplines seamlessly together within the NVBL can’t be overstated. Here is Deb Gracio, Associate Laboratory Director for National Security at Pacific Northwest National Laboratory.


Deb Gracio: One of the greatest things I think we learned is that if we have collaboration networks set up ahead of time, we have the resources, the people, the data, the compute resources, the synchrotron light sources, the experimental facilities and capabilities, and our people start to know each other and know how to work with each other. That's a critical aspect of being able to address crises like this in the moment, without them already having been set up, bringing people together is very challenging. Being able to look at very difficult problems where we all bring different pieces to the puzzle is a critical, important part of being able to do next generation science to solve problems like this.


Sarah Harman: And that’s the key phrase: Next Generation Science. The pandemic laid bare the relevant shortcomings within the traditional scientific model and methodology, but it also presented an enormous opportunity to redefine the science, and take it to the next, more collaborative level.


Sarah Harman: With a new, collaborative model now in place, the challenge then shifts to efficiently sharing large quantities of data, so it can be most effectively utilized. 


Deb Gracio: Whether it's an experimental infrastructure or computational infrastructure, how do you share the data amongst all of the laboratories, amongst all of the scientific community so that we're growing from each other and the whole is greater than the sum of the parts, rather than each individually siloed answer of how we work on problems traditionally. This is really bringing all of us together to build a more cohesive, collaborative network that can work on these problems.


Sarah Harman: So, what kinds of practical innovations resulted from this new, collaborative method and how are these breakthroughs affecting the lives of average Americans? Pat Fitch again. 


Pat Fitch: We got involved in the ventilator work, we ended up with one of the best 3d models of inhalation breath and how you could change the ventilator to be a therapeutic device. Most people on a ventilator don't survive. It's just so abusive to your lungs. So together with a small company, and some that was three or four labs, and one academic partner, they pretty much built a human lung on a tabletop. 


Sarah Harman: For context, lung biomechanics are very complex, and building a computational model would prove challenging on any timeline, but even more so while under the pressure of an ongoing, global pandemic.


Pat Fitch: I'm not, I'm not easily impressed, because I've had a great career doing a lot of fun things. But when I saw that I was like, that is like the coolest thing I've seen in years. And it involves mechanical engineers, computer scientists, applied mathematicians, biologists of three different flavors, chemist, it was just an insane collection of different talent, all saying, okay, what can we do to make a difference?

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Sarah Harman: The NVBL even worked to ensure the reliability of mass-market COVID tests.


Deb Gracio: So another advancement that happened because of the NVBL were multiple laboratories coming together to validate tests that were coming out on the market to ensure that the tests were valid, that they were not giving false positives or false negatives, and to assure that the tests that were on the drugstore stands for all of the public to utilize were good and accurate tests.


Sarah Harman: From advanced computational modeling to epidemiological and logistical support, the NVBL has proven that, despite unprecedented challenges, we have the necessary means to innovate, even in the face of catastrophe.  


Sarah Harman: And now that the immediacy of the pandemic has waned, the NVBL looks to the future of pandemic detection and management.  


Dion Antonopoulos: It sort of, you know, laid a new foundation, if you will, to be able to, use as a springboard for future efforts. And so now the idea is that, hey, we've been able to come up with a way of surveilling for SARS-CoV-2. Why can't we go about looking at other targets as well? Again, early warning, approaches for being able to see other viruses, pop up as they emerge in populations.


Pat Fitch: We can do surveillance really well. So like our lab, a bunch of other labs, we continue to surveillance for Coronavirus and other viruses, and our sewer system and our wastewater. It actually is a really great predictor, especially since the amount of testing in the community has gone down. But we can, we can see when Coronavirus, influenza and a few other things are going up in the community just by watching what's going on the wastewater. 


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Sarah Harman: Though surveillance methods won’t stop the virus from spreading, early detection is key in public health efforts to stem the tide of infection. And these early warning signs of increased transmission and infection are especially helpful when people are asymptomatic.


Sarah Harman: And while detection is clearly a crucial part of pandemic management, it’s only one piece of the NVBL’s focus moving forward.


Deb Gracio: When the next, whether it's a pandemic, or some other biological event occurs, we have a good understanding what the gaps are, and that from a science perspective, we're already starting to address those gaps.


Deb Gracio: Epidemiological modeling, is a good example of that. Starting to build out the computing infrastructure is another good example of that. And when you start to think about bio challenges, whether it's a pandemic or some other type of a challenge, it's taking that one health agenda and really looking at it from a broad perspective. So is it a something that's happening in the human in the environment, because of climate? You know, what's really bringing the biological challenge? And how is it going to be addressed holistically rather than just thinking about it in that that silo that it exists in?


Sarah Harman: And because this holistic approach includes data from a wide variety of sources, the team must then put it all together to create a coherent picture. 


John Hill: And then the last area was what we call cross cap trying to connect these pieces together. And that comes down again to how do you shared data across these different sources. For some people, sometimes it's data about humans, sometimes it's data about individual cell level, a protein level, or it's about, you know, the environment. And so having ways to share that data at really speed up the feedback loop between if you're trying to discover a drug feedback loop between the predictions and the model, the models and the actual results, just kind of integrate that together in a sort of much more transparent way.


Sarah Harman: And this presents a computational challenge that the Department of Energy is uniquely equipped to handle. 


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Pat Fitch: Take out the word pandemic and make an algorithm that does, it is not that different than the challenges and climate is not that different than stockpile stewardship requirements. And so we are very well positioned as a family of labs to take this problem the computational problem one. And as we know, from stockpile stewardship and climate, once you get better models, you start realizing where the gaps are in your data. So I'd say I look at gaps that DOE has not just an opportunity. So it's not just that no one else is doing it. But they're so strongly positioned to do, I'd say it's this mixture of the computational stuff, which is an easy thing to see how it aligns with other doing missions, then experimental sciences probably need to be customized a bit for the life sciences. And I would include in that the big light sources, structural biology and having, you know, 1000s of more structures in our libraries before the next pandemic.


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Sarah Harman: So, we have a roadmap in place, and we know how the individual pieces can function as part of a larger whole, but where do we go next? How do we arrive at a destination that can effectively address and mitigate the possibility of another global pandemic? Deb Gracio again.


Deb Gracio: So to really implement the solutions we're talking about here requires global cooperation; it's not going to be just what the US can do by themselves, it really has to be a collaboration and cooperation across the whole world. And we're a long ways from that, you know, even starting to address some of the understandings of how to bring new drugs into the marketplace, how to address the legal issues, how to address the IP issues, how to ensure that we have vaccinations around the entire world to support breaking down what the pandemic has created, that will take us a long ways, but it's going to take a lot of work and a lot of time to get there.


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Sarah Harman: And with time being a recurring theme and obstacle within the context of pandemic research, the NVBL is even looking at ways to integrate Artificial Intelligence into the equation.


Dion Antonopoulos: Coming back to this idea of the vast amount of diversity in biological systems, we're challenged by… Only looking at the places where we expect to find things. In other words, where we expect to find new discoveries utilizing AI, utilizing self-driving laboratories, in other words, laboratory automation. Those combined efforts allow us to cover a lot more territory in terms of being able to figure out and winnow down to, hey, this is the type of material that we should be actively working on in the laboratory... The other aspect, too, is that this is also putting us in a position to basically design the laboratory of the future, if you will, where it's not necessarily a case of you're going to be replacing scientists at the bench, it's massively augmenting their capabilities at the bench to be able to again, accelerate discovery along much shorter time frames.


John Hill: Right now, for a typical drug to make it to the market, I think you go through something like 10,000 different experiments, before you get there 10,000 failed drugs before you get to the one that succeeds. So that's why drugs are so expensive, because the pharma industry has to invest so much to get to that one success. If we can short circuit that and go right from the start knowing we're gonna have a success at the end of it, we could just change, change the game. And so I think there's real possibilities there that AI coupled with very fast automated experiments could get us there, on the timescale of five to 10 years, I think that's, that's a reason for real optimism.


Sarah Harman: Even with the extraordinary advancements brought about by the NVBL’s work during the COVID-19 pandemic, we still face many obstacles in prevention, detection, and mitigation. Thankfully, the DOE and its National laboratories are uniquely qualified to meet these challenges, head-on. 


Sarah Harman: And while we may not be fully inoculated from another pandemic, we have mobilized like never before, and have powerful new collaborative tools in our arsenal.

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Sarah Harman: Well, that’s it for another episode of Direct Current! Thank you to our guests, Pat Fitch, Deb Gracio, Dion Antonopoulos and John Hill, for helping us better understand the lessons we’ve learned from the COVID-19 pandemic, and what we’re doing to prepare for the next potential outbreak.


Sarah Harman: If you want to learn more about how DOE and the NVBL have helped to reshape how we look at pandemics, check out our show notes. You can find those, along with our other episodes, at energy.gov/podcast. 


Sarah Harman: Direct Current, and our episode artwork, is produced by me, Sarah Harman. This episode was written by Keith Langsdorf and Matt Dozier. Music and sound editing by Michael Stewart. A special thanks to Gillian King-Cargile and Emily Baker for logistical help at Argonne National Laboratory.

Sarah Harman: This is a production of the U.S. Department of Energy and published from our nation’s capital in Washington, D.C. Thanks for listening!


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