Episode 40: Exploring the Chan Zuckerberg Initiative – Building, Funding, and Doing Science with Key Opinion Leader Marc Malandro (RE-RELEASE)

In January, the Chan Zuckerberg Initiative announced they were becoming a science-first philanthropy. Join us and listen again as Mark Palandro, now the COO of the entirety of the Chan Zuckerberg Initiative, explains what science means to them and how they intend to impact the world.

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I'm James Zanewicz, the Chief Business Officer for the Tulane School of Medicine.

And I'm Dr. Elaine Hamm, the Executive in Residence for the Tulane School of Medicine.

And this is Bio from the Bayou. Welcome back to Bio from the Bayou. Today's guest is Dr. Mark Malandro. He is the Vice President of Operations for Science at the Chan Zuckerberg Initiative. Mark and I met when we both started in the field of technology transfer, and since we are both so young, we were ingenues at that point, obviously. And we have since pivoted into different roles, but we still get to interact a lot in our current capacities. So it's my extreme pleasure to welcome you to the show, Mark.

I appreciate the invitation. It's great to see you again.

So for our listeners who may not know, the Chan Zuckerberg Initiative was founded in 2015 to help solve some of society's toughest challenges, from eradicating disease and improving education to addressing the needs of our local communities. And inside that mission is embedded a strong commitment to building a more inclusive, just, and healthy future for everyone. And I also know that you guys incorporate a lot of science and technology into everything you do. So can you tell us more about some of the broad goals relating to the future of science and disease research at CZI?

One thing is we're grounded in values. And so the entire organization started with something called the Letter from Max. And it was a letter to Mark and Priscilla's daughter, Max, and basically outlined what they hoped to do in her lifetime. And one of those things was to make sure that luck has as little to do with success as possible. And that's when the overarching goal of a future for everyone happened. And so It is ingrained in all we do. Our wireless password is everyone. It's a guest wireless, so we're all okay. But it's really keeping that in mind. Everyone really does mean everyone. And so in science, we took the mantra of open science as a way of allowing most people who are performing research to be able to have access to the tools we're funding and tools we're developing, regardless of where in the world you happen to be, regardless of the funding you happen to have, that you can access these tools. They're free and they're open to the public. And to allow us as well to ensure the people who the research is going to benefit is as broad as possible. There's a lot of perpetuated bias out there in datasets. And so a lot of the work we do is funding additional datasets or supplementary datasets to make sure that it is as representative as we can possibly have a hand in developing. Those values are through everything we do and through all of our programs. And so science, even being as young as it is, really is doing a whole bunch of different experiments to see where we can have our impact. And our impact, we always call our differentiated impact. We want to do those things that others can't do. We want to fund those things that others can't fund. And so when we fund something and someone else picks it up, that's fantastic. When we can get partnerships, that's fantastic. When other funders will work with us on something that we've started, that's fantastic. And so there's no pride of authorship. There's nothing other than just trying to ensure that as many people as possible can benefit from our work as possible.

Well, that's a great segue to the next question I had for you, because you're one of the key leaders in the area of science, or the pillar of science for CZI. And as I did my research, I saw you have the mantras of build, fund, and do. Can you tell us a bit more about what each of these mean and how our listeners might engage with those?

We originally got started as what you'd call a tech-based philanthropy. And so we realized sort of with our founders that technology and software and data science were going to be wrapped into everything we do. It wasn't without challenges. The Silicon Valley tech infrastructure is a lot different than the academic infrastructure where a lot of the program staff came from. And so it was a time of trying to figure out how we do this all together and how one side can leverage the best from the other side. And so we really got started as a funder, but a funder who was working in the technology space. So we wouldn't just fund generation of data in laboratories, we'd provide software platforms for them to share those data, for them to visualize, to analyze those data. We realized at that point in time, so we have the ability to fund, we have the ability to build software tools, and we were also funding a group which now is sort of part of the ecosystem called the Biohub Network. When the Biohubs first got started, it was a grant, an experiment, if you will, to how you can get regional universities to collaborate together. How can you incent them to work at the Biohub on areas outside of their normal area of interest, but really this high-risk, high-reward type of things, but mostly collaborative. How can you get them together? How can you convene them in a way that makes sense? And we decided when we had some leadership transition in science that that would be a great way of saying, let's bring this ecosystem together to answer questions with all of those levers. Can we figure out how, for any particular problem, what the balance of build, balance of fund, balance of do should be? Building software tools, funding research, and now doing research in our biohubs, one in San Francisco, the newest one in Chicago. And also doing technology development. We have the Chan Zuckerberg Institute for Advanced Biological Imaging, which is doing technology development to allow you to see proteins within cells. From an engagement perspective, the fund side is the easiest side because we put out most of our funding in competitive applications. And so we're always happy to, to have people have that information. On the software development side, All of our software is open source licensed and it's usually developed in the open through GitHub. So anyone can take any of the software products we have, use them as they'd like. And on the do side, it's really traditional academic collaboration. So it's not limited to those 3 regional universities. It's really how can they partner with others and how can we extend the reach of that network? And so we're always willing to work in collaboration. In fact, one of our values as well is collaboration is the key. We need to do this in partnership. We have a bold, audacious goal that says we're going to cure, prevent, or manage all diseases by the end of the century. We can't do that ourselves. We can do it by partnering with people who share the same values, and through open science and accelerating science, hopefully we can get closer to that goal.

And this is why I find CZI just so fascinating and such a great potential partner for so many academic institutions. One thing that you mentioned was the Biohub network, which is touted as empowering scientists to pursue their riskiest, most exciting ideas. And you mentioned a couple of them. Could you go into a little more detail about each of those BioHubs and what they do?

So the first one in San Francisco was really an experiment of just sort of free-thinking science, not necessarily Grand Challenge-focused, but they sort of organized themselves into areas around Steve Quake and Joe DeRisi, the co-presidents. So Joe DeRisi, being an infectious disease researcher, have platforms in infectious disease. Steve Quake, who have been involved in a lot of the Tabula single-cell projects like the Tabula Sapiens and Tabula Muris, single-cell atlases of the human body and of mouse, organized around those. And you need data analysis to be able to look at those. And so data science became a platform. And then out of this, one of the platform leaders they hired really developed some unique imaging capabilities. So you had all of these platforms there. It was a place where you could fund investigators at these institutions, but do it in a way that was different. You weren't necessarily just funding sponsored research. You were giving their laboratories the freedom to partner and figure out what are those next big exciting things? What are those areas you want to work in? You don't have to worry about preliminary data. You don't have to worry about 3 specific aims like in an application to the NIH. You could really put out your blue guy stuff. And perhaps the Biohub networks can empower that, the San Francisco one in particular. When we went to Chicago, what we decided was, is there a way we could focus it a little bit more on a particular scientific area, but still get the sort of best of what happened in San Francisco? So Chicago is focused on using micro and nano fabricated sensors and looking at cells during inflammation. Inflammation is both a good and a bad process in the body. You need it for fighting cancer, but in some cases like arthritis, you want to control inflammation. We have to understand the signals. We have to understand individual cells in their environment. And so within that large sort of area of focus, you're going to have everything from true mechanical and material engineering all the way through to really understanding sort of the ecosystem of cells in those particular environments. But you're going to do that in conjunction with researchers at each of those institutions. And hopefully bringing in with University of Illinois at Urbana-Champaign, bringing in sort of undergraduate computer science to help you with a lot of the data handling issues. As everyone knows, we have too much data now. It's hard to visualize. It's hard to handle. It's hard to move around. But we need to solve this not just for these particular types of problems. I think a lot of the tools to solve data handling are going to be common sort of throughout all types of data. And so trying to get that tech-based thing, which we are so used to, as part of the Biohub for us is exciting as well.

Well, having started my career at UIUC in tech transfer, I love hearing that they're engaged and that's great. And I know that each of the hubs bring together kind of a consortium of local-ish universities. But if there are scientists outside of those areas who want to engage with those hubs, are there ways they can do that?

Yeah, I think they do engage in traditional collaboration. I think the regional focus is simply how their funding works and who they support. Most all of our researchers are interacting with researchers all over the world. And so the funded investigators we have in, in the Bay Area hopefully are going to collaborate with those in Chicago and those well beyond. These are big problems and big challenges. And so we would welcome their collaboration. Each of these investigators that we fund also has their day jobs. So they are collaborating naturally as part of the work they do as faculty members at each of their institutions. And so if we could figure out a way of leveraging all that together, I think that'd be very powerful.

Well, it's talking about big initiatives and big challenges. As I mentioned earlier, CZI has always applied tech to science as a way of advancing things. And you recently announced a key initiative around AI and scientific discovery. And as a board member and officer of the Alliance for AI in Healthcare, or AAIH, which is the industry association in this space, I'm really intrigued by this initiative and where it's going. And I wondered if you could talk a little more about that.

Yeah, absolutely. It's at its most nascent stages, but we always realized that machine learning and natural language processing were always going to be part of what we did. Looking at datasets and finding patterns in those datasets were always gonna be part of what we did, even before all of the discoveries that have happened recently in large language model AI and generative AI. So we were well positioned for this through data science, but what we realized is, There was this large gap. Academic institutions could only afford so much computational power. And there was this huge amount, now in particular, of computational power being developed in industry. So is there space for a philanthropy? And as a tech-based philanthropy, if we couldn't find it, that was a problem we would have.

Mm-hmm.

And so what we decided to do was make an investment in building a large GPU cluster that we would continue to do our work on, We'd also hire additional scientists. Our goal is to make all of the work that comes out of that, whether it's the models, whether it's access to the models, free and open to the public. Those things we're going to work on probably are going to start in the datasets we help generate. So you could imagine the 50 million cells worth of single-cell data that are out there in CellxGene. One may say, okay, can I use those data to predict things like cell state? And once I have cell state, can I use that to do X, Y, and Z? And so Again, in the earliest, most nascent stages, but we are making the investment and hopefully more to come. That is so great to hear.

I love the engagement, the investment, the commitment to moving things forward. And finally, as I mentioned earlier, you came out of the realm of academia, and so you have a really unique insight into our operations, our strengths, and let's be honest, our quirks. Do you see ways that academic medical centers or universities can work better with CZI or provide you with information that enables you to leverage our strengths more efficiently?

One of the things we're good at is understanding sort of those people who are leaders in their field. In order for us to even start programs, we do a lot of workshopping. And so we ask people in the field, not just themselves, what do you think the field needs, but who else should be at the table? Who else should we talk to? So the fact that Tulane and others have great people working at their institutions, we're going to know about those people. We also want to ensure that we're setting up relationships where people know about those types of things we're interested in funding. Most all of our work is competitive funding, and so those are open calls to whomever wants to participate. And so we want to make sure that people know and understand that. Understanding that we are high touch, so we're not the type to fund people and then walk away and come back in 3 years and see what happens. They have to convene. They have to work collaboratively, not just with their own cohort of people, but with the other cohorts of people at CZI. There's advantages to that in that once we establish gaps, we can do supplementary funding to figure out how we can further support. But we are high touch. The other thing is to, to have institutions who are willing to work in the open science value space that we are. Everything we do is free and open to the public. And so it has to be people who— Who share those values. So there's certain things in funding basic biomedical research that we fund that are different than funding drug discovery and drug development. And so understanding the areas and spaces that we focus in and sharing those values of making sure that those data are out there for other scientists to use are good ways of aligning values, let's say. And at the end of the day, we're all trying to make progress. And so ensuring that all of our measures of those progress are well aligned For us is really important. We're very transparent. We try to be as transparent as possible, and we're just there to support and help because we can't do this work alone.

So I think if we boil it down to something very simple and an analogy, and you knew I was going to do this with something at some point, CZI is not into casual dating in your collaborations. You want a long-term relationship.

Absolutely.

Absolutely. Well, Mark, I want to thank you once again for coming on. But before we close, I want to ask, is there anything else you feel our listeners need to know? ways they should approach you? Like, what's the last thought you want to leave us with today?

We are learning. We're really new to this. We don't have all the answers, and we need people to teach us. So the more people who can work with us, the more people we can learn from, the better we're going to be. As I said, we're looking to fund those things that people don't fund. We're looking to make a differentiated impact where, though we can do those things we call CZI-shaped problems, And the only way we're going to know what those are if people tell us. So we're always willing to learn.

Well, Mark, I want to thank you once again for coming on the podcast, and we look forward to following CZI so much more in the future, especially as we have our first Tulane collaboration with you ever. And as always, we'll have Dr. Melandro's contact info and his informational links on CZI posted in our show notes. So make sure you check them out to learn more. Mark, thanks so much once again.

Appreciate it. Thank you.

This episode of BIO From the Bayou was recorded at BIO On the Bayou, our annual event showcasing academic science, biotech, and startups from the entire Gulf South region. Save the date for 2024, October 29th and 30th. Check out bioonthabayou.com for more information.

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