A conversation with Adam Marblestone about his new project - Focused Research Organizations.

Focused Research Organizations (FROs) are a new initiative that Adam is working on to address gaps in current institutional structures. You can read more about them in this white paper that Adam released with Sam Rodriques.

Links

FRO Whitepaper

Adam on Twitter

Adam's Website

Transcript

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In this conversation, I talked to Adam marble stone about focused research organizations. What are focused research organizations you may ask. It's a good question. Because as of this recording, they don't exist yet. There are new initiatives that Adam is working on to address gaps. In current institutional structures, you can read more about them in the white paper that Adam released recently with San Brad regens.

I'll put them in the show notes. Uh, [00:01:00 ] just a housekeeping note. We talk about F borrows a lot, and that's just the abbreviation for focus, research organizations.

just to start off, in case listeners have created a grave error and not yet read the white paper to explain what an fro is. Sure. so an fro is stands for focus research organization. the idea is, is really fundamentally, very simple and maybe we'll get into it. On this chat of why, why it sounds so trivial.

And yet isn't completely trivial in our current, system of research structures, but an fro is simply a special purpose organization to pursue a problem defined problem over us over a finite period of time. Irrespective of, any financial gain, like in a startup and, and separate from any existing, academic structure or existing national lab or things [00:02:00 ] like that.

It's just a special purpose organization to solve, a research and development problem. Got it. And so the, you go much more depth in the paper, so I encourage everybody to go read that. I'm actually also really interested in what's what's sort of the backstory that led to this initiative. Yeah. it's kind of, there's kind of a long story, I think for each of us.

And I would be curious your, a backstory of how, how you got involved in, in thinking about this as well. And, but I can tell you in my personal experience, I had been spending a number of years, working on neuroscience and technologies related to neuroscience. And the brain is sort of a particularly hard a technology problem in a number of ways.

where I think I ran up against our existing research structures. in addition to just my own abilities and [00:03:00 ] everything, but, but I think, I think I ran up against some structural issues too, in, in dealing with, the brain. So, so basically one thing we want to do, is to map is make a map of the brain.

and to do that in a, in a scalable high-speed. Way w what does it mean to have a map of the brain? Like what, what would, what would I see if I was looking at this map? Yeah, well, we could, we could take this example of a mouse brain, for example. just, just, just for instance, so that there's a few things you want to know.

You want to know how the individual neurons are connected to each other often through synopsis, but also through some other types of connections called gap junctions. And there are many different kinds of synopsis. and there are many different kinds of neurons and, There's also this incredibly multi-scale nature of this problem where a neuron, you know, it's, it's axon, it's wire that it sends out can shrink down to like a hundred nanometers in [00:04:00 ] thickness or less.

but it can also go over maybe centimeter long, or, you know, if you're talking about, you know, the neurons that go down your spinal cord could be meter long, neurons. so this incredibly multi-scale it poses. Even if irrespective of other problems like brain, computer interfacing or real time communication or so on, it just poses really severe technological challenges, to be able to make the neurons visible and distinguishable.

and to do it in a way where, you can use microscopy, two image at a high speed while still preserving all of that information that you need, like which molecules are aware in which neuron are we even looking at right now? So I think, there's a few different ways to approach that technologically one, one is with.

The more mature technology is called the electron microscope, electromicroscopy approach, where basically you look at just the membranes of the neurons at any given pixel sort of black or white [00:05:00 ] or gray scale, you know, is there a membrane present here or not? and then you have to stitch together images.

Across this very large volume. but you have to, because you're just able to see which, which, which pixels have membrane or not. you have to image it very fine resolution to be able to then stitch that together later into a three D reconstruction and you're potentially missing some information about where the molecules are.

And then there's some other more, less mature technologies that use optical microscopes and they use other technologies like DNA based barcoding or protein based barcoding to label the neurons. Lots of fancy, but no matter how you do this, This is not about the problem that I think can be addressed by a small group of students and postdocs, let's say working in an academic lab, we can go a little bit into why.

Yeah, why not? They can certainly make big contributions and have to, to being able to do this. But I think ultimately if we're talking about something like mapping a mouse brain, it's not [00:06:00 ] going to be, just a, a single investigator science, Well, so it depends on how you think about it. One, one, one way to think about it is if you're just talking about scaling up, quote, unquote, just talking about scaling up the existing, technologies, which in itself entails a lot of challenges.

there's a lot of work that isn't academically novel necessarily. It's things like, you know, making sure that, Improving the reliability with which you can make slices of the brain, into, into tiny slices are making sure that they can be loaded, onto, onto the microscope in an automated fast way.

those are sort of more engineering problems and technology or process optimization problems. That's one issue. And just like, so Y Y Can't like, why, why couldn't you just sort of have like, isn't that what grad students are for like, you know, it's like pipetting things and, doing, doing graduate work.

So like why, why couldn't that be done in the lab? That's not why [00:07:00 ] they're ultimately there. Although I, you know, I was, I was a grad student, did a lot of pipetting also, but, But ultimately they're grad student. So are there in order to distinguish themselves as, as scientists and publish their own papers and, and really generate a unique academic sort of brand really for their work.

Got it. So there's, there's both problems that are lower hanging fruit in order to. in order to generate that type of academic brand, but don't necessarily fit into a systems engineering problem of, of putting together a ConnectTo mapping, system. There's also the fact that grad students in, you know, in neuroscience, you know, may not be professional grade engineers, that, for example, know how to deal with the data handling or computation here, where you would need to be, be paying people much higher salaries, to actually do, you know, the kind of industrial grade, data, data piping, and, and, and many other [00:08:00 ] aspects.

But I think the fundamental thing that I sort of realized that I think San Rodriquez, my coauthor on this white paper also realized it through particularly working on problems that are as hard as, as clinic Comix and as multifaceted as a system building problem. I th I think that's, that's the key is that there's, there's certain classes of problems that are hard to address in academia because they're system building problems in the sense that maybe you need five or six different.

activities to be happening simultaneously. And if any, one of them. Doesn't follow through completely. you're sort of, you don't have something that's novel and exciting unless you have all the pieces putting, you know, put together. So I don't have something individually. That's that exciting on my own as a paper, Unless you, and also three other people, separately do very expert level, work, which is itself not academically that interesting.

Now having the connectome is academically [00:09:00 ] interesting to say the least. but yes, not only my incentives. but also everybody else's incentives are to, to maybe spend say 60% of their time doing some academically novel things for their thesis and only spend 40% of their time on, on building the connectome system.

Then it's sort of, the probability of the whole thing fitting together. And then. We see everyone can perceive that. And so, you know, they basically, the incentives don't align well, for, for what you would think of as sort of team science or team engineering or systems engineering. yeah. And so I'm like, I think, I think everybody knows that I'm actually like very much in fav