Searching for Food and Better Data Science at the Same Time

Two presentations to announce, both of which are happening live on 12/2. The first is the latest OpenWorm Journal Club, happening via YouTube live stream. The title is "The Search For Food", and is a survey of a recently-published paper on food search behaviors in C. elegans [1].

While the live-stream will be available in near-term perpetuity [2] on YouTube, the talk will begin at 12:45 EST [3]. The abstract is here:

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms.

The other presentation is one that I will give at the Champaign-Urbana Data Science Users' Group. This will be a bit more informal (20 minutes long), and part of the monthly meeting. The meeting will be live (12 noon CST) at the Enterprise Works building in the University Research Park. The archived slides are located here. The title is "Open Data Science and Theory", and the abstract is here:

Over the past few years, I have been working to develop a way to use secondary data and Open Science practices and standards for the purpose of establishing new systems-level discoveries as well as confirming theoretical propositions. While much of this work has been done in the field of comparative biology, many of the things I will be highlighting will apply to other disciplines. Of particular interest is in how the merger of data science and Open Science principles will facilitate interdisciplinary science.

NOTES:
[1] Subtitle: To boldly go where no worm has gone before. Yup, Star Trek pun. Full reference: Roberts, W. et.al   A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans. eLife, 2016; 5: e12572.

[2] for as long as YouTube exists.

[3] Click here for UTC conversion.