Our research team at FOM Institute AMOLF, VU Amsterdam, and the Okinawa Institute of Science and Technology is studying the strategies animals use to navigate the world around them. For a model system, we use roundworms, including the popular genetic model organism C. elegans. These organisms are ~1 mm long with a simple nervous system, and in the case of C. elegans we know its genome, the developmental history of all the cells of its body, and the connectivity of its neurons. Despite this, we don’t understand how C. elegans works.
Generating high-quality quantitative data
Our approach is to generate high-quality quantitative data on the motility of worms, and then use tools from statistical physics to build simple models that accurately describe the worm behavior. By comparing models across many individuals and species, we can then discover what aspects of the behavior are important and how organisms adapt their behavior to different conditions.
Imaging data at high frame rates
This project poses many data problems. We track the worms using high-resolution automated video imaging. Their behavior spans diverse timescales, from repetitive body motions on the timescale of a second, abrupt actions on the tens of seconds scale, and changes in the average behavior over minutes. This requires that we image at high frame rates for long periods of time.
Terabytes of data
Furthermore, the statistical approach requires that we study hundreds of individuals for each experiment. As a result, we accumulate terabytes of imaging data that must be stored, processed, analyzed, and shared among the various members of the research team located at the FOM Institute AMOLF, VU Amsterdam, and the Okinawa Institute of Science and Technology.
EYR facilities will help understanding worm behaviour
We applied to the Enlighten Your Research competition because the services provided would address technical challenges at each of these steps that hold us back from our ultimate scientific goals. The ongoing consultation round has already provided us with useful feedback on managing our data stream and improving the processing pipeline. While this will alleviate some of the issues that prompted us to apply, the computational resources from SARA, connectivity by SURFnet, and advice from the eLife Science Center will be important for fully achieving our goal of using “big data” to understand the worm.
General impact on biology research
Many of the problems we face are becoming more common in biology due to the increasing focus on quantitative data and the development of new data-rich experimental techniques. We hope that the approaches we develop as part of this project will have a more general impact on the way biology research is done.
Postdoctoral Researcher, Systems Biology Group
FOM Institute AMOLF