Daniel Colón-Ramos: Networks and the Nervous System

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00:00:06.19 My name is Daniel Colon-Ramos, I am an assistant
00:00:09.18 professor at Yale University and my lab is interested
00:00:12.22 in understanding how the architecture of structures
00:00:16.11 like the brain forms. And I spend most of my time thinking about
00:00:22.21 these questions, but today I wanted to do something a little bit
00:00:25.26 different. It actually doesn't have to do directly with research,
00:00:30.10 but I wanted to use the brain and you know, some general
00:00:34.25 musings, to basically to think about what can we learn
00:00:39.01 about the brain with regards to how we learn, how we generate
00:00:44.25 knowledge, and also how science works. So what I'm going to be doing
00:00:48.29 today, I'm going to jump into different topics. And again, this is not
00:00:52.27 a science talk, it's more like a general thought talk.
00:00:55.22 But I'm going to illustrate how we think about science and
00:01:00.15 through collaborations, through creating networks of knowledge,
00:01:04.29 we basically, as scientists, generate new knowledge. And in that,
00:01:08.10 I'm going to illustrate the importance of cooperativity and the importance
00:01:12.16 of collaboration, which is a topic that's seldom discussed
00:01:14.28 in the sciences. But is essential for creating new knowledge
00:01:19.17 and for furthering the sciences, in general.
00:01:23.17 So, the brain is made out of cells that are called neurons.
00:01:30.00 And there are more neurons in a single human brain
00:01:36.07 than there are stars in the whole Milky Way. So there are about
00:01:40.28 a hundred billion neurons. So if there are some out there that are
00:01:43.21 watching this talk, next time that you are sitting down and looking at
00:01:46.02 the sky, think that there are more neurons in your brain than there
00:01:49.14 are stars you are seeing in the skies. And basically, there are a lot
00:01:54.27 of them. And they connect in a very specific way. And they actually
00:01:59.11 underlay human behaviors, they underlay the capacity to communicate
00:02:05.06 for you to understand the stock, and the organization is critical in that
00:02:10.13 aspect. So we're interested in this question, in how we
00:02:14.23 said that the architecture of the brain develops. And my lab
00:02:19.09 in particular, pursues this question by using the nematode, C elegans,
00:02:23.13 as a model organism. C. elegans is a tiny, tiny worm. It's about the size
00:02:27.25 of a comma in a sentence. So some of you might be asking me, how
00:02:31.06 is it that you can claim that you understand how the human brain,
00:02:34.09 which is so complex, works by looking at such a tiny worm?
00:02:37.05 And I want to explain a little bit how we do that, because
00:02:40.14 this is actually how -- the way that we do it, and the way that
00:02:43.12 other colleagues in the field that use C. elegans or use other
00:02:46.10 model organisms do it, actually highlights how science is done and how
00:02:51.10 knowledge is generated. So, C. elegans as I mentioned is really
00:02:56.11 small. And the reason that we use it is because scientists have
00:03:00.16 created tools that allow us to examine questions that will otherwise
00:03:05.16 be very, very complicated to examine. Questions like how is it that
00:03:09.01 you deconstruct the architecture of a structure like the brain.
00:03:13.10 And in particular, we use C. elegans because it's the only animal
00:03:17.19 for which we know the wiring diagram of the whole nervous system.
00:03:21.00 So, somebody sat down and mapped out all of the neurons, all of the
00:03:25.17 cells that form part of the nervous system, and so we know who they're
00:03:29.20 connecting to, where they're positioned, and for many of them,
00:03:32.23 we know what behaviors they're important for. And all that knowledge
00:03:35.13 is actually very important, because we use that knowledge
00:03:38.04 to generate and create new knowledge. So why is that knowledge
00:03:41.16 important? And the reason that that knowledge is important is
00:03:43.11 because of evolution. So it's thanks to evolution that a lot of
00:03:46.15 the concepts that we find in, for example, C. elegans, they're actually
00:03:50.28 applicable to other organisms, to other animals, including ourselves.
00:03:55.06 And so much so, that in the past 10 years, 6 Nobel prizes
00:03:59.25 have been awarded to people working in C. elegans that have
00:04:02.17 made fundamental discoveries in C. elegans, which were then
00:04:05.12 widely applicable to other systems, which furthered our general
00:04:11.21 knowledge of how biology works. And they were recognized with
00:04:15.21 Nobel prizes. But the important part beyond the prize is that
00:04:18.25 because of evolution, we can use model organisms, be it
00:04:21.18 C. elegans or yeast or flies or mice, to be able to generate fundamental
00:04:26.10 knowledge that then can benefit humanity. So, that knowledge has
00:04:34.03 told us something fundamental, very important, about how the nervous
00:04:37.26 system works. Be it C. elegans or our own brains, neurons connect
00:04:43.27 to each other and cooperate to form networks. And that cooperativity
00:04:48.27 between these individual cells is what basically underlies the
00:04:52.21 power of the nervous system, the power of our own brains.
00:04:55.19 So neuron isolation is not that powerful, but in combination with
00:04:59.23 other neurons, it's capable of creating all these circuits which then
00:05:02.19 are capable of doing amazing things, like creating a concept of
00:05:06.20 reality and allowing communication. So, but one important aspect
00:05:12.25 is that the number of neurons is important, but what is more critical
00:05:19.07 than the number of the neurons is how they're organized.
00:05:22.00 So, much like having a bunch of cables does not make a computer,
00:05:26.18 having a bunch of neurons that are disorganized does not make
00:05:30.05 a nervous system. They have to be organized in a particular
00:05:32.23 way. And again, I want to bring as an example, C. elegans.
00:05:35.03 C. elegans only has 302 neurons, so that's far fewer neurons
00:05:40.05 than 100 billion neurons that you find in a single human brain.
00:05:42.19 But those 302 neurons are organized in such a way that allows
00:05:47.01 the animal to create a representation of its world to find mates,
00:05:50.18 to avoid predators, to find food. And that's what we're interested
00:05:55.22 in. We're interested in understanding how is it that those neurons
00:05:58.18 get organized, and how that organization then underlies the behavior
00:06:01.18 of the nematode. The human brain is obviously organized in a different
00:06:08.17 way, because we don't have the same pressures. The same evolutionary
00:06:11.28 pressures or the same needs as C. elegans. So we are -- our brains
00:06:15.00 are wired in a different way than the C. elegans brain, thankfully.
00:06:18.25 And one critical aspect of our brain, which is fascinating, at least to me,
00:06:23.21 is that our brains are actually wired to be wired. And what I mean
00:06:27.10 by that is that when C. elegans are born, or when reptiles are born,
00:06:32.19 they know what to do. It's almost like their behaviors are wired
00:06:36.05 and they know where to go. But when we're born, a new baby is born,
00:06:40.21 it's actually -- it looks like what Aristotle used to call a tabula rasa.
00:06:46.10 It looks like the mind is empty. It's not really empty, it's wired to achieve
00:06:51.16 something very important. And that something very important is to connect
00:06:54.22 to other humans, and to learn from other humans. That's what we
00:06:56.27 are connected to. And that capacity of our brain to connect to
00:07:01.20 other people, to be able to learn from others, to be able to
00:07:04.07 build on knowledge that was generated by others before us,
00:07:07.02 is fundamental for the sciences. So, knowledge to a certain extent,
00:07:12.29 can be considered a network of networks. Because our brain is
00:07:17.06 a network, and the knowledge that we're generating by connecting our
00:07:20.17 brains to other people is a network of networks. So, let me give you
00:07:25.08 an example of how this works in the real science world.
00:07:28.08 So, I became interested in understanding how the nervous system
00:07:33.07 of the nematode develops in embryos. But I didn't have the tools to
00:07:38.05 be able to do that, so I collaborated -- I linked my brain to
00:07:43.00 other experts that had tools that were very beneficial to my
00:07:46.29 research, and my research was beneficial to theirs.
00:07:49.12 One of them is Zhirong Bao, who is at Sloan Kettering.
00:07:52.18 He's a computer scientist turned developmental biologist.
00:07:56.16 And he developed a computer that he trained to be able to
00:08:00.10 observe, basically here you have a nematode embryo, where the nuclei
00:08:04.22 in the cells, the center of the cells, are labeled in green. And the computer
00:08:08.29 is going to basically visualize this embryo as it's developing
00:08:12.24 and it's going to keep track of every single cell, and the lineage
00:08:15.23 of every cell. So at any given time, the computer -- because we
00:08:18.27 for C. elegans, we know the lineage of every single cell, it knows
00:08:22.11 what each cell is and what it's going to become. So, you have this
00:08:26.13 tool on one hand, and on the other hand, we also collaborated,
00:08:29.22 Zhirong and I, with another scientist, he's a microscopist at the
00:08:33.18 NIH called Hari Shroff. And Hari developed a microscope that was much more
00:08:37.20 powerful than any microscope that existed at the time.
00:08:40.00 And allowed us to image the embryo continuously and seamlessly
00:08:45.01 for long periods of time without damaging the embryo. That work was
00:08:48.26 very important, and in combination with these computer tools,
00:08:51.11 allowed us to do things that we couldn't do before. So here's the
00:08:54.06 microscope at work, looking also at the development of this
00:08:57.10 embryo, but you can see it's happening much faster because
00:08:59.15 this microscope that Hari developed is 30 times faster than the
00:09:03.12 microscopes that we had before. So that all of a sudden, by linking
00:09:06.15 our brains, we were able to do more than we could have each
00:09:08.29 done individually. And this is actually very important because this
00:09:13.00 is how science is done at all levels, even if you were claiming
00:09:15.28 to be a scientist and work completely alone, you're actually
00:09:18.27 -- you're receiving knowledge from other colleagues that are
00:09:23.25 actually colleagues who could be contemporaries, or it could
00:09:26.12 be colleagues that passed away, that generated knowledge before
00:09:29.15 you came along, and you're building on that knowledge. So
00:09:32.17 in that sense, science is actually a network of networks, and it's
00:09:36.06 a community where we're continuously building on other people's
00:09:39.16 knowledge. And in our case here, we were able to then link
00:09:44.00 what Zhirong was doing, what I was doing, and what Hari was
00:09:46.28 doing, to then generate videos like the one that I'm showing you
00:09:50.04 here, of an embryo that is developing in real time, and you can see
00:09:55.09 the neurons in green, the nuclei in red. And all of a sudden,
00:09:59.23 when this embryo starts moving, you can see that we can actually
00:10:02.19 keep track of the individual cells because the microscope that Hari
00:10:06.10 developed is so efficient and so fast, and we can observe neural
00:10:11.06 developmental events that were previously inaccessible.
00:10:14.05 And in that way, answer questions that we couldn't answer
00:10:17.08 before. So I hope that with these examples, I have illustrated
00:10:21.12 to you a little bit how science works, how collaborations work,
00:10:24.15 the importance of collaborations in sciences, which is
00:10:27.14 fundamental but seldom discussed. And if you're interested
00:10:30.22 in these topics and you want to hear more, I invite you to
00:10:33.02 do a Google search of my last name with "ted blog,"
00:10:36.15 I have a blog on this topic, and I also have a Ted-x talk
00:10:39.09 that discusses in more depth, the importance of basic
00:10:43.03 research in biomedicine. Thank you.

This Talk

Speaker: Daniel Colon Ramos

Audience:

Student
Researcher

Recorded: February 2014

Watch in:

Spanish

Talk Overview

Can a single human brain ever understand how a human brain works? Colón-Ramos argues not – but a network of brains might! Networks are an essential part of science, whether it is at the level of single neurons connecting to form a brain, or networked brains forming a scientific community. Colón-Ramos studies neural networks in the nematode C. elegans to uncover how neurons organize and communicate with each other to form the nervous system. Using the nervous system as a metaphor, he explains how our own brains, similar to neurons, are “wired to be wired”, and how scientists connect their brains to weave new knowledge.