Summary: C. elegans has a ton of neuropeptides (apparently 115 proneuropeptide genes for up to 250 mature peptides, many of which have been observed experimentally by mass spec). These neuropeptides are generally secreted extrasynaptically, and have been shown to modulate choline/acetylcholine release at neuromuscular junctions in C. elegans. In this paper, they want to know how neuropeptides can control synaptic plasticity.
Summary: This will be my second post I think that actually has nothing to do with neurons.
When worms defecate, they apparently have a very characteristic body pattern consisting first of posterior contraction (both dorsal and ventral), followed by anterior contraction (again, both dorsal and ventral) followed by expulsion. Subdividing this process into these three steps (called pBoc, aBoc and Exp) goes back to at least (Thomas, Genetics, 1990). In this paper, they refer to this as the defecation motor program (DMP).
This process occurs via a calcium wave that starts in the posterior intestinal cells and propagates via innexins to the anterior intestinal cells. I have some questions about this* but I'll hold off on them for now. The calcium influx into these intestinal cells induces pumping protons from the intestinal cells into the pseucoelomic space and acidifying it. Body wall muscles then respond to this acidication via proton-gated ion channels (PBO-5/6) and contract.
When possible, reuse as much of the plot format as possible
For scale bars on images, it doesn't hurt to put text right next to the bar, telling your readers what the scale bar actually means (so that they don't have to go to the legend to find out that bar is 100um).
Putting the sample size directly on your plots is helpful (rather than in legends).
Bar plots with numeric values on the x-axis are almost never the best way to show the information - numeric variables demand quantitative plotting!
Axis labels are essential, and they need to be be as clear as possible about two things: 1) what was measured, and b) what that measurement should be interpreted as.
labels like 'Fraction' or 'Count' are generally not very helpful.
As always, suggestions welcome! I will update this list as I think of more things (and read more papers!).
In this paper, they sought to understand how C. elegans controls its pharyngeal pumping ('feeding') behavior in response to attractive and repulsive chemical stimuli. They use diacetyl as an attractive stimulus, and quinine or high concentrations of isoamyl alcohol as repulsive stimuli.
Title: Tyramine functions independently of octopamine in the Caenorhabditis elegans nervous system
Summary: The year was 2005, and no one knew if tyramine was important in C. elegans. It was known that octopamine was found in C. elegans extracts, and that exogenous octopamine could manipulate C. elegans behavior, but no one had yet found a tyramine hydroxylase gene that could turn tyramine into octopamine in animals. And importantly, no one knew how octopamine and tyramine differed in their roles in worm behavior.
There are lots of optogenetic actuators. And they respond to diverse wavelengths. What if you could systematically profile how an organism or population of cells responds to a precise temporal stimulation profile in 3 colors?
Well, now you can. Because I built a board to do it. Here's an example of random stimulation wellof each well in a 96-well plate with blue and/or green light.
If this sounds like it might be useful to you - awesome, let me know! I'll be putting up the board schematics, bill of materials, and source code shortly so you can build your own.
Where do you get the files? Here! And if you get stuck on building it, hit me up and I'd be happy to help.
Summary: In this paper, they are aiming to make a fast, sensitive and specific optically-controllable protein interaction. At first, they note that there are a number of photoactive proteins that have been engineered for use as photoswitchable actuators, including the LOV2 domain of phototropin 1, Vivid (VVD) and a bunch of others (cryptochrome 2, FKF1, UVR8, EL222). Point blank, I know nothing of any of these so I really can't comment on how this fits in with the field (maybe I'll try to come back to this later).
In this work, they focus on Vivid (VVD), from Neurospora crassa. It's one of the smallest proteins, uses FAD as a chromophore (which is ubiquitous in eukaryotic cells), and homodimerizes when blue light is applied. However, it's got drawbacks.
It homodimerizes. Imagine you want to make gene A active when you apply light. You split gene A in half (A* and A') and make and express two fusion proteins: VVD-A* and VVD-A'. When you shine blue light, you're as likely to match A* with A' (its correct partner) as with A* (not its correct partner).
It's slow. After you stop the blue light, it takes 3-4 hours before the dimers separate back into monomers.
When fitting fails, it fails for basically two reasons
bad initial conditions
bad model (ill-conditioning/multicollinearity)
90% of the time it's the initial conditions. In fitting, finding good initial conditions is usually one of the hardest things to do. You expect the fitter to do all the work but its really not that good - it just refines solutions when you're already in the vicinity of the solution.
As a RULE, always plot these three things overlaid:
Plot the data you want to fit, AFTER any transforms you might apply (for example, if you're going to fit the log-transformed data, PLOT the log-transformed data).
Plot the predictions based on the initial guesses for the parameters.
Plot the predictions based on the fit parameters.
How do we obtain good initial guesses?
One option is to guess some parameters (guided by what they mean in your model), check to see if they yield predictions anywhere in the vicinity of the data they're supposed to fit. This often works okay if you're only fitting one dataset. But what if you've got to fit 100 datasets? Then it's unlikely that a single initial estimate will work for all of them, and we're going to need to come up with initial estimates for each one.
A better option is to use heuristics to get you to ballpark estimates, correct to perhaps an order of magnitude. Examples of heuristics:
If I were fitting a straight line, e.g. y = mx+b (note that this is just a toy example and you'd never use an iterative solver to fit this), i might get an initial guess for the slope parameter m by first sorting my data in order of ascending x, and then using (y_last-y_first)/(x_last - x_first).
If I were fitting a logistic function: y= K / (1+P * exp(-r*x)), I might choose K = max(x), because in my model, K is the maximum value it ever attains. For an initial guess for P, since I know at x=0, then y=K/(1+P), i might see if i have a datapoint around x=0. If I do, call it (x*, y*), then a decent initial guess for P might be P = (K/y* - 1), or max(x)/y*-1.
Fitting on a log or linear scale (and transforms more generally):
While I can fit models many ways, one of the most common issues I've had arise is whether to fit them on a log or linear scale. These do NOT provide the same result - e.g. fitting y=mx+b, or fitting log(y) = log(mx+b). Why? Because when we fit, we're implicitly trying to minimize the overall difference between the left-hand side and the right hand side of the equation. Strictly, most fitters default to minimizing the sum of squared errors between the left- and right-hand sides (observed data and predicted data, respectively). On a linear scale, the difference between 10 and 100 is a lot more than the difference between 1 and 20, whereas on a log-scale, the latter is a much larger discrepancy.
Practically speaking, the way to choose whether to fit your data on a linear or log scale is to ask - do i care about absolute deviations? or relative deviations? Suppose I have some data (y) that span say four orders of magnitude, from 0.01 to 100.
This comes down to - do I believe the errors in my data are additive or multiplicative? In the former, fit the data on a linear scale. If errors are multiplicative, fit in on a log scale.
Instead of using a function that fits formulas, it often helps clarify the problem to formulate it as an optimization (maximization or minimization problem), and forces you to think clearly about what fitting actually means. Typical fitting function (e.g. lm() or nls() in R) use the sum of squared errors between the data and the predictions.