- Harvard University, Cambridge, MA, U.S., PhD in Biophysics, 2000.
- Princeton University, Princeton, NJ, U.S., AB in Physics, 1994.
- Department of Physics, University of Toronto.
- Department of Cellular and Systems Biology, University of Toronto.
MY RESEARCH OVERVIEW (GO TO SCIENTIFIC OVERVIEW)
How do simple organisms make measurements of their environment and compute appropriate responses?
Sensing and responding to changes in the environment is a hallmark of living systems, and a major challenge of systems biology is to understand the biological networks that connect sensory information to behavioral responses.
We are interested in how biochemical and neuronal networks encode and process sensory information to produce adaptive locomotory behavior, and how this behavior benefits the organism in their natural environment. In our work, we study the sensory behavior of two model organisms, the bacterium E. coli and the nematode worm C. elegans. These organisms can sense the world as we do. They use their sense of smell to find their way to food (chemotaxis) or use temperature measurements to move to cooler areas when things get too hot (thermotaxis).
We study E. coli because it is a model of how biochemical networks process sensory information in single cells. Discovering how bacteria measure and process this information will likely reveal design principles of the networks in all cells. C. elegans is a nematode that gets through life with a surprisingly small number of neurons (~ 300), and so it is an excellent model of how small neuronal networks process sensory information. Its compact neuronal network may allow us to understand complete sensory pathways from sensory neuron to motor output. Using the worm system, we hope to understand the processing of “complex” stimuli like thermal pain.
Our work is interdisciplinary. We study the sensory processing of cells and simple organisms using a combination of techniques that span the areas of genetics, physiology, photonics, and biological physics. We also work hard to develop new instrumentation and computational tools. This makes the Donnelly Center a great place to work since the culture here crosses the boundaries of traditional disciplines and embraces technological innovation.
SCIENTIFIC RESEARCH OVERVIEW
All cells and organisms make analog measurements of their environment and process these measurements to produce adaptive responses. We are interested in determining what computations are involved in this processing: i.e. what is the software and how is it implemented? What is the hardware? Our main approach is to develop novel instrumentation and methods to measure and analyze behavioural responses at the motor output level. This systems-level approach helps us detail the molecular, cellular, and neuronal components involved in these pathways, and also allow us to ask questions that span a number of traditional scientific boundaries such as sensory biology, systems neuroscience, theoretical neuroscience, and sensory ecology. Some of our projects are described below.
1. Sensorimotor analysis of thermotaxis and thermal nociception
The nematode, C. elegans is thermotactic and prefers the temperature at which it was cultivated. When placed on agar plates in thermal gradients, worms will migrate toward their preferred temperature and near this temperature they will track isotherms with surprising accuracy (within 0.1C). The thermal preference of worms is plastic. If placed at a different temperature in the presence of food, worms will acquire a new thermal preference in about 4 hours. By applying defined thermal stimuli to single worms using an IR laser and then following the behaviour of the worm in time with the worm tracker, we are studying various components of the thermosensory behaviour of C. elegans, including the impulse response, isothermal behaviour, and general computational strategy.
2. C. elegans foraging
What is the statistical foraging strategy of C. elegans? A common model to describe the foraging movements of organisms is the random walk, in which the duration and direction of the forward movement of the organism is chosen randomly. A variation on this strategy allows for taxis. For example, an organism can perform chemotaxis - biasing its motion along a chemical gradient towards an attractant or away from a repellant - by correlating the duration of forward movements with the changes in sensory input. In the absence of any such sensory stimuli, an important question is what statistical strategy will be the most efficient? Or more specifically, from what distribution should the organism choose the duration of its forward movements? It has been suggested that for randomly distributed targets it is more efficient to perform a Levy walk than a Brownian walk. A Levy walk is a random walk in which the run lengths have a power-law distribution( P(l)~l^(-μ) with 1 < μ < 3 ), and a Brownian walk is a random walk in which the run lengths have an normal distribution (μ > 3). More specifically it has been shown that for sparsely distributed targets, the optimal value of μ is 2. It is known that the turning frequency of C. elegans decreases as a function of time away from food. In order to quantify this behaviour we are using the tracking microscope to measure trajectories of worms freely crawling on agar plates. We can show that C. elegans in the absence of food performs a Brownian walk initially (μ > 3) and shifts to a Levy-type walk (μ ~ 2) after a period of about 15 minutes. Through Monte Carlo simulations, we show that this behaviour is in fact more efficient than either a Brownian walk or Levy walk alone. We also show that the statistical strategy is under genetic control. Dopamine receptor mutants, dop-2, show Brownian behaviour at early and late times, while dop-3 mutants show Levy walk behaviour at early and late times. We are continuing to explore how this statistical control of strategy is controlled through hormonal neurotransmitter systems of the worm.
3. Measuring and perturbing neurons in C. elegans
To study the neuronal correlates of behaviour, we use a variety of techniques to measure and perturb the activity of C. elegans neurons. Neurons can be ablated (e.g. killed) using pulsed laser light from a dye laser or a Ti:Sapphire laser. Signals from neurons can be measured using fluorescence microscopy using genetically encoded calcium indicators like GCamP or Cameleon. Neurons can be activated directly using channelrhodopsin. Neurons can also be developmentally ablated or functionally silenced using a variety of genetic techniques. The goal here is to correlate these neuronal perturbations with our behaviour measurements to understand how these behaviours are implemented at the neuronal level.
4. E. coli thermotaxis and colony behaviour
In addition to being a model organism for understanding chemosensation, E. coli is also thermotactic. Using a tethered cell assay where the bacterium is tethered to a glass coverslip by a single flagellar motor, we can measure the behavioural state of single bacteria in time. By applying well-defined thermal stimuli we can measure the thermal responses of single bacteria. What is the thermotaxis strategy of E. coli? What does the thermosensory system of E. coli tell us about the evolution of early thermal sensation? How does the this system remain robust across such a wide range of temperatures? We study the behavioural response of single E. coli to programmed thermal gradients. In addition to single cell analysis we are also interested in the physical interactions of bacterial colonies as they interact at the boundary between two colonies.
- Genetics of Intraspecies Variation in Avoidance Behavior Induced by a Thermal Stimulus in Caenorhabditis elegans. Ghosh R, Bloom JS, Mohammadi A, Schumer ME, Andolfatto P, Ryu W, Kruglyak L. Genetics. 2015 Aug;200(4):1327-39.
- Direct measurements of drag forces in C. elegans crawling locomotion. Rabets Y, Backholm M, Dalnoki-Veress K, Ryu WS. Biophys J. 2014 Oct 21;107(8):1980-7.
Mechanistic analysis of the search behaviour of Caenorhabditis elegans. Salvador LC1, Bartumeus F, Levin SA, Ryu WS. J R Soc Interface. 2014 Jan 15.
View Pubmed search of Dr. Ryu's full list of publications.