First off, just want to thank a commenter for providing an interesting and thoughtful response to some of the topics we discussed in day 1. Highly recommended reading.
Day 2 started with Rob trying to stir the pot by placing three bets (the stakes are dinner in Paris at a fancy restaurant, yummy!). First bet was actually with me, or really a bet against pessimism. He claimed that he would be able to explain Hana’s complicated data on transcription in different conditions once we measured the relevant parameters, like, say, transcription factor concentration (wrote about this in the day 1 post). My response was, well, even if you could explain that with all the transcription factor concentrations, that’s not really the problem I have. My problem is that it is impossible to build a simple predictive model of transcription here. The input-output relationship depends on so many other factors that we end up with a mess–there are no well-defined modules. To which Rob rightfully responded by saying that that's moving the goalposts: I said he can't do X, he does X, I say now you have to do Y. Fair enough. I accept the original challenge: I claim that he will not be able to explain the differences in Hana's data using just transcription factor concentration.
Next bet was with Barak. In the day 1 post, I mention the statistical approach vs. the mechanistic approach. Rob and Barak still have to formulate the bet precisely (and I think they actually agree mostly), but basically, it is a bet against the statistical approach. Hmm. Personally, I don't know how I come down on this. I am definitely sympathetic to Rob's point of view, and don't like the overemphasis these days on statistics (my thoughts). But my thoughts are evolving. Rob asked "Would it really have been possible to derive gravitation with a bunch of star charts and machine learning?" To which I responded with something along the lines of "well, we are machines, and we learned it." Sort of silly, but sort of not.
Final bet was with Ido (something about universality of noise scaling laws). Ido also had a bet as well on this point, in this case offering up a bottle of Mezcal for a resolution. More on this some other time. I am going to try and get the bottle!
The talks were again great (I mean really great), if perhaps a bit more topically diffuse than yesterday. Started with evolution. Very cool, with beautiful graphs of clonal sweeps. An interesting point was that experimental evolution arrives at different answers than you expect initially. They are rational (or can be), but not what you expect early on–amazingly even in pathways as well worked out as the metabolic pathways. I'm wondering if we could leverage this to understand pathways better in some way?
On to the "tech development" section, which was only somewhat about tech development, somewhat not. Stirling gave a great talk about human NET-seq. What I really liked about it was that in the end, there was a simple answer to a simple question (is transcription different over exons when they're skipped? exons vs. introns?). I think it's awesome to see that genome-wide data can give such clear results.
So far, everything was about control of the mean levels of transcription. Both Ido and I talked about the variance around that mean, with Ido providing beautiful data on input-output functions. On the Mezcal, Ido shows that there is a strong relationship between the Fano factor and the mean. I am wondering whether this is due to volume variation. Olivia's paper has some data on this. Probably the subject of another blog post at some point in the future.
Theory: great discussion about Hill coefficients with Jeremy! How can you actually get thresholds in transcriptional regulation? Couple ideas. There's conventional cooperativity, and there could also be other mechanisms, like titration via dummy binding sites like in Nick Buchler's work. Surprising that we still have a lot of questions about mechanisms of thresholds after all this time.
Conversation with Jeremy and Harinder: how much do we know about whether sequence fully predicts binding? Thought for an experiment–if you sweep through transcription factor concentrations, what happens to binding as measured by e.g. ChIP-seq? Has anyone done this experiment?
Then, off to the Red Sox vs. the Twins. Biked over there on Hubway with Ron, which was perfect on a really lovely day in Cambridge. The game was super fun! Apparently there were some people playing baseball there, but that didn't distract me too much. Had a great time chatting with various folks, including two really awesome students from Angela's lab, Clarissa Scholes and Ben Vincent, who joined in the fun. Talked with them about the leaky pipeline, which is something I will never, ever discuss online for various reasons. Also crying in lab–someone at the conference told me that they've made everyone in their lab cry, which is so surprising if you know this person. Someone also told me that I'm weird. Like, they said "Arjun, you are weird." Which is true.
Oh, and the Twins won, which made me happy–not because I know the first thing about baseball, but I hate the Red Sox, mostly because of their very annoying fans. Oops, did I say that out loud?
Okay, fireworks are happening here on day 3. More soon!