by Andre on 23 March 2016
Cross posted from Worm Watch Lab blog.
tl;dr: Using the data collected so far, we’ve already identified some mutants that look like they have different egg laying rates than the reference strain. These could point to previously unknown roles for the mutated genes in nervous system function. See the plot below for a summary of some of the results.
Following the dedicated work of so many worm watchers, we got classifications for all of the data that we initially uploaded to the site (thanks again!). Since then, we’ve had a chance to look at some of the results.
Remember that the goal of this project is to find genes that play roles in behaviour. The way we do that is by looking at mutant worms with known genetic changes to see if they behave differently. If they do, then that’s a hint that the altered gene affects behaviour. Many of the genes we are focussing on are likely to function in nerve cells so these results can provide new starting points for learning how brains work at the molecular level.
With a project like Worm Watch Lab, each video gets classified by multiple people. We can compare different people’s annotations to help filter out mistakes and refine the timing of egg laying events. Bertie Gyenes, a graduate student in my lab, has followed up on the work Vicky talked about in her blog post.
At this point, we’ve just done some preliminary analysis of the average egg laying rate (how many eggs worms lay per video). The plot summarises this data. Each bar shows the range of egg laying rates observed for a given strain (the red crosses are individual outlying worms) and the dashed orange line shows the rate typically observed for the laboratory reference strain N2. The plot only shows data for the mutants with the lowest and highest egg laying rates. As you can see, many of the worms with low rates are called egl. That’s because these genes were initially discovered by screening for mutants with abnormal egg laying. These are expected hits. What you can also see are worms with unusually high egg laying rates. Many of these, including the current record holder in our dataset dnc-1 were not previously known to have abnormal egg laying rates, which means we already have hints of new discoveries in the data that’s been categorised so far!
If you’re interested in learning more about any of these genes, just go to http://wormbase.org/ and put the gene name into the search bar. Sometimes you can learn interesting things about the genes. For example, mutations in the human version of dnc-1 can lead to Charcot-Marie-Tooth disease.
We’re getting ready to upload some new videos to Worm Watch Lab soon. Please consider contributing some more annotations for the next batch of data. It will be exciting to see what else we’ll find.
by Andre on 20 November 2014
Physical Biology has published a nice collection of perspective articles (and they’re freely available!) from scientists who have come to biology from various flavours of physics. They cover a range of topics and are written in a range of styles. I found the contributions from Bob Laughlin and Geoff West interesting. Bonnie Bassler and Ned Wingreen write about how biologists and physicists can work together. They both present their very different perspectives, which I thought worked really well in the article. It’s probably also the most practical of all the perspectives in actually giving some advice on how the two cultures can work together productively. Finally, Bob Austin’s contribution is good, but it’s really just a reprise of his earlier polemic in response to “Harness the Hubris”, which we’ve talked about before.
by Andre on 13 November 2014
The OpenWorm team is trying to simulate an entire animal. It’s a big goal, but C. elegans is the right place to start. I had the pleasure of meeting a good portion of the team last week in London at their meeting and I’m definitely impressed with what they’ve managed to do so far. You can read their latest (and I believe first!) update in Frontiers in Computational Neuroscience here.
You can find out more on the page of their (extremely successful) Kickstarter campaign. The emphasis there is on something concrete that they can give back to contributors, but the longer term goal remains the development of a flexible platform for worm simulations. The most exciting aspect for me is the behavioural validation. Basically, to check whether the simulation is working and to optimize parameters, they will compare the output to known worm behaviour.
In principle, that’s not much different from comparing a mutant worm to the wild type, which I spend a lot of my time thinking about. We’ve recently sent them the data from Ev’s behavioural database paper and they’ve kindly agreed to serve the data, including the videos, which we are currently only doing for the segmented videos via our YouTube channel.
Our next generation tracker is going to generate some even better data both for our purposes in behavioural genetics, but also for OpenWorm’s simulation because we’re going to include a few stimuli rather than just looking at spontaneous behaviour. That kind of input-output data will be more useful for model optimization. Of course, what they really want is comprehensive optogenetics and imaging data coupled with quantitative behavioural data. That’s not quite available yet, but some groups are getting very close.
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