By day, computational biologist Brato Chakrabarti studies cilia and other microscopic appendages adorning the surface of biological cells. By night, he can be found playing the tabla and sarod or listening to Hindustani classical music. Despite outward appearances, these two interests aren’t so dissimilar. By playing in a group, says Chakrabarti, musicians create a melody larger than they could alone, just as cilia work together to create movement bigger than themselves.
However, unlike musicians, cilia appear to do this spontaneously. In his work, Chakrabarti is using physics and mathematical computer models to figure out how cilia are able to operate collectively. By studying how cilia work together, he hopes to understand the basic physics that underlies a whole host of concepts in evolution, as well as developmental and functional biology.
Chakrabarti is a Research Fellow in the Flatiron Institute’s Center for Computational Biology (CCB) in the Biophysical Modeling Group. After completing a bachelor’s degree in mechanical engineering at Jadavpur University, he went on to earn a master’s degree in engineering mechanics at Virginia Polytechnic Institute and a doctorate in applied mechanics at the University of California at San Diego.
Chakrabarti recently spoke with the Simons Foundation about his work and its impact.
What do you study?
I use physics tools to probe questions that involve self-organization and emergent behavior in biological phenomena. By emergent behavior, I am referring to the behavior that occurs when multiple objects move together and create movement that no single object could make alone. Think of a wave created by fans in a football stadium or ripples rolling across a wheat field on a windy day.
I study emergent behaviors that are self-organized, meaning ones that are created by internal forces. So, instead of wind creating ripples in a wheat field, these emergent behaviors are driven by themselves. An example of this is the behavior of cilia, which are tiny micron-sized cellular appendages. Cilia are ubiquitous and are often found coating surfaces of cells in our brain, lungs and the reproductive tract. A single motile cilium in isolation typically beats spontaneously in a whip-like fashion. However, in collective setting, like in our ciliated cells, their oscillations are converted into an emergent behavior of large-scale traveling waves. I’m trying to understand how cilia and other biological entities spontaneously self-organize and synchronize.
I also study synchronization in physics problems that are inspired by biological observations, like the waves in cilia or a neuron’s electrical pulses. Since there are examples of synchronization both in physics and biology, understanding them from one discipline can help drive advances in the other.
Why are emergent behaviors and synchrony important?
Deciphering these phenomena helps us get insights into development, evolution, and more. For example, the tiny movements of cilia have big consequences. In our lungs, cilia generate waves that help us fight against pathogens and irritants we breathe in by transporting particles out of the lungs. Inside our brains, cilia coating certain types of brain cells transport cerebrospinal fluid in order to regulate blood circulation in the brain. Cilia are also found in microorganisms, where they are used for locomotion.
In addition to cilia, I look at self-organizing flows inside cells, particularly the egg cells of fruit flies. Towards the end of an egg cell’s development, there are large flows circulating around the entire cell. These flows are driven by hair-like objects that are similar to cilia, which coat the interior walls of the egg cell. They are essential for the fly to develop properly — with wings and legs in the right places. By studying their movement, we can understand how an egg cell develops. Similarly, if we want to learn about immune response or evolutionary processes, it’s important that we have a good understanding of the physics of self-organization and emergent behaviors.
How do you study self-organizing behaviors?
We take a ‘bottom-up’ approach. We start by asking: How does a single object, such as a single cilium, perform a task like waving? Once we figure that out, we can figure out how a bunch of objects move together.
Often, we start with what has already been done by looking at previous experiments and scientific papers to create a basic theoretical model for what we hypothesize. The models we create are mathematical — they employ both differential and partial differential equations that can explain a behavior. By comparing our equations to more experiments, we see if they match. If they don’t, then we make changes to the equations or add more complex equations, until, eventually, the model matches the behavior seen in the experiments. It’s an iterative process of experimentation and modeling.
When we have a working model for a single filament, we can figure out how multiple filaments work in concert. If we want to look at the specifics of how tens of thousands of filaments interact, we use largescale computer simulations. Alternatively, we can look at the overall behavior of just the individual actions of each filament. This approach is called continuum theory, and it’s what I use to model the way fluid flows in fruit fly eggs. Instead of modeling all the tiny hairs in the egg, I assume the carpet of hairs acts as one continuous field. This allows me to make a simpler model with just a few differential equations that can describe the whole system.
To write these equations, I have to draw on concepts from fluid mechanics, statistical mechanics and non-equilibrium physics. Since the filaments are in a liquid, we need to understand how their movements generate flows in the liquid, which in turn affect the movements of the filaments.
In this process, I work closely with experimental collaborators here in the CCB, such as Jasmin Imran Alsous. One project we are working on is studying how sperm is stored in male fruit flies. Jasmin has collected incredible images of sperm cells, and I’m trying to build quantitative theories that can help us understand the emergent collective dynamics of sperm. I will make a prediction or a theory, and she will bring in knowledge from experiments on what works or what does not. We engage in a continuous process of iteration and collaboration as we strive towards finding a solution. This synergistic exchange of ideas and perspectives fuels our progress, which I cannot accomplish alone.