Thursday, September 3, 2015

A partial list of things you can talk to a senior grad student about.

The chances are that you know a grad student. Those wobbly, skittish creatures that you find slinking around a lab or office, easily mistaken for stray grasshoppers. You may, on occasion, attempt to engage these entities in casual conversation. But be warned: under the smile scrawled upon their faces may lurk - unbelievably - a slew of actual human emotions. Uncertainty, anxiety, hope, frustration, hilarity, sadness. As outlandish as it may sound, students are capable of feelings, which only become heightened as they near the inevitable departure from studenthood and the accompanying life changes that can be equal parts welcome and unwelcome.

It's a transition that needs to be talked about - and that's for them to do with their research mentor(s), and it hopefully catalyzes some empowering, encouraging, insightful discussions.

But if you're not mentoring the student in question, what are you to do? What on earth could there be to ask a grad student about, other than the date of their thesis defense? Don't they relish having a neon red countdown clock affixed to their head?

The answers may surprise you: grad students don't actually want to talk about being a grad student in every single conversation. Rest assured, they know that they need to defend their thesis, find a job, clear out their apartment, move away from their close friends, etc. It can be exciting and stressful and painful - and honestly, you're better off not picking at that tangled web and triggering thoughts that they might be trying to shelve away for a little while.

So if you're not going to ask a senior grad student about their end date, are there any conversation topics that are appropriate, or should you just shun their company? To answer that question, let me ask you some other questions:

  1. What was the last book you read or film you watched?
  2. What's your favorite dinosaur?
  3. What's your least favorite song? 
  4. What are you passionate about? 
  5. If you could have any superpower, what would it be?
  6. Would you rather have a free lifetime supply of coffee but never have chocolate, or a lifetime supply of chocolate but no coffee?
  7. Do you like hiking? Bird watching? Bungee jumping?
  8. Have you ever solved a Rubik's cube?
  9. Who in history would you most like to spend a day as?
All of these questions, and others, could potentially be employed during conversation with graduate students! But please keep in mind the personality and interests of the student that you're talking. I'm assuming that if you're talking to them, you have some interest in getting to know them better and aren't simply emitting words to pass time.  

Are there non-work things you wish (or wished) people would talk to you about during the years that you're working on your PhD? Or do you disagree, and enjoy talking about the logistics of finishing up? Comment below! 

Thursday, February 5, 2015

Good hard vs. bad hard: What type of research challenges are you experiencing?

In a laboratory setting, researchers, postdocs, and graduate students can find themselves alone and lacking confidence in the face of some common challenges. Those difficulties are often lumped together as an inherent part of pursuing a research career, but we think they could be divided into two types—challenges that are hard in a good way or in a bad way.

Good-hard challenges include rigorous tasks that lead to scientific discovery, and can be surmounted with discipline and focus, while bad-hard challenges are those that are extraneous to the research process and can lead to debilitating personal stress, poor self-image, and stagnation in the work.

We’ve created a partial list of both types to help researchers differentiate between the two. Read more at The Chronicle of Higher Education.

Friday, August 22, 2014

Elucidating missing links of the TCR signaling network

Just published:
Phosphorylation site dynamics of early T-cell receptor signaling. LA Chylek, V Akimov, J Dengjel,  KTG Rigbolt, WS Hlavacek, B Blagoev. PLOS ONE 9, e104240

Stimulation of the T-cell receptor (TCR) can trigger a cascade of biochemical signaling events with far-reaching consequences for the T cell, including changes in gene regulation and remodeling of the actin cytoskeleton. A driving force in the initiation of signaling is phosphorylation and dephosphorylation of signaling proteins. This process has been difficult to characterize in detail because phosphorylation takes place rapidly, on the timescale of seconds, which can confound efforts to decode the order in which events occur. In addition, multiple residues in a protein may be phosphorylated, each involved in distinct regulatory mechanisms, necessitating analysis of individual sites.

To characterize the dynamics of site-specific phosphorylation in the first 60 seconds of TCR signaling, we stimulated cells for precise lengths of time using a quench-flow system and quantified changes in phosphorylation using mass spectrometry-based phosphoproteomics. We developed a computational model that reproduced experimental measurements and generated predictions that were validated experimentally. We found that the phosphatase SHP-1, previously characterized primarily as a negative regulator, plays a positive role in signal initiation by dephosphorylating negative regulatory sites in other proteins. We also found that the actin regulator WASP is rapidly activated via a shortcut pathway, distinct from the longer pathway previously considered to be the main route for WASP recruitment. Through iterative experimentation and model-based analysis, we have found that early signaling may be driven by transient mechanisms that are likely to be overlooked if only later timepoints are considered.

Wednesday, August 13, 2014

Pre-game announcements!

Greetings, loyal readers! You might remember that a few months ago we showed you the q-bingo game card that we brought to the last q-bio conference, and asked for your ideas on what terms are popular (or perhaps overused) in systems biology so that we could use them in future games. I can now announce that we have used your ideas in the set of playing cards for this year's conference!

If you're at the conference and want to play, come to Poster Session 1 tomorrow (Thursday) and stop by poster #11 (hint: it's very violet) to pick up your card AND to learn about some exciting research that will be coming out in just a few days. See you there!

If you're not coming to the conference (or even if you are), you can still join the fun by following us on (our new) Twitter: @qbiology.

Thursday, August 7, 2014

Thanks, but no thanks

I am posting from the q-bio Summer School, where we are enjoying many discussions about modeling. Several lecturers have advised the junior modelers attending the school, who are mostly graduate students and postdocs, to find an experimental collaborator. I appreciate the advice and the benefits of having an experimental collaborator, but I am usually quite irked by the reasons stated for seeking out opportunities to collaborate with an experimentalist. One reason I've heard many times is that modelers need an experimentalist to explain the biology to them and to help them read papers critically. It certainly could be useful to have a more experienced researcher aid in formulating a model, but that person might as well be a modeler familiar with the relevant biology. I don't subscribe to the idea that modelers need a collaborator to evaluate the soundness of a paper. To suggest so seems insulting to me. Modelers do need to consult experts from time to time to understand the nuances of an unfamiliar experimental technique, for example, but so do experimentalists. I am probably more annoyed by the popular sentiment that a collaborator is essential for getting predictions tested. If I were an experimentalist, I might be insulted by this idea. It's unrealistic to think that experimentalists are lacking for ideas about which experiment to do next. If your prediction is only appealing to your experimental collaborator, then maybe it's not such an interesting prediction? Modelers should be more willing to report their predictions and let the scientific community follow up however they may, partly because it's unlikely that your collaborator is going to be the most qualified experimentalist to test each and every prediction you will ever make. I think the real reason to collaborate with an experimentalist is shared goals and interests and complementary expertise. Finding such a colleague is wonderful, but it shouldn't be forced, and the absence of a collaborator shouldn't be an impediment to progress. If you have a good prediction, you should report it, and if you want to model a system, you should pursue that. Eventually, you will know the system as well as the experimentalists studying it, if not better. After all, it's your role as a modeler to integrate data and insights, to elucidate the logical consequences of accepted understanding and plausible assumptions, and to suggest compelling experiments. Finally, I want to speak to the notion that modelers should do their own experiments. I think that's a good idea if you want to be an experimentalist. If you want to be a modeler, be a modeler.

Tuesday, July 29, 2014

Resource post: Tools for better colors

Looking through my list of bookmarks, it’s apparent that something I like to read about (other than food) is design. The reason? I believe that well-made, attractive visuals are worth the effort because they can help make a point clearer and more memorable. In a sea of plots and diagrams (or in a room full of posters), you want people to notice, understand, and remember yours. One aspect of this process is finding the right color scheme to get your message across, which can involve several questions: How much (if any) color is necessary? What do I want these colors to convey? Will viewers be able to distinguish every color? When answering these questions, I've found a couple of links to be useful.

Getting inspired/informed: 
  • Dribbble: Although (or perhaps because) this design gallery isn't science-centric, browsing it can trigger new ideas for how to use color and other design elements. 
  • Color Universal Design: How to make figures that are colorblind-friendly, with examples. 
  • The subtleties of color: An introduction to using color in data visualization. 
  • Adobe Kuler: Generate color schemes based on color rules or images, browse schemes created by other users, or put together your own. This is nifty for seeing how different colors will look when they are side by side. 
  • Colorbrewer: A popular tool among scientists in part because it offers different color schemes for different data types. 
  • Colorzilla: A browser plug-in that makes it easy to pick and analyze color from webpages, for when you see a color that you really want to use. 
Do you have any favorite tools or approaches for using color? Or is it something that you'd rather not emphasize?

Sunday, July 20, 2014

Being (and keeping) a collaborator

Recently, a paper of ours was accepted for publication (stay tuned for more about that!). It grew out of a long, trans-atlantic collaboration. It was the first collaboration that I was part of, and I was "spoiled" by the experience because of how productive and fun it was (and continues to be). I remember the first time that my side of project yielded a useful clue. Much to my surprise and delight, our collaborators took that clue to their lab and followed up on it right away. 

Collaborations can be awesome. They're also becoming increasingly prevalent as connections grow between different fields. There are lots of potential benefits for everyone involved: you get to learn about techniques outside your own specialization, your can develop a unique new perspective, and you may find yourself having some friends to visit in faraway places. 
Good memories of great science-friends in Odense, Denmark.

However, I've noticed since then, through observation and experience, that not all collaborations reach their best potential. So I have been thinking about what qualities are possessed by a good collaborator so that I know what to look for and what I should try to be. 
  1. Finding a problem that you can tackle together. It goes without saying, but it's key to pick a problem that all participants care about and can actually work on. Bonus points if it's a problem that can only be addressed by combining the complementary skills of everyone involved. (Otherwise, are you collaborating just for show?)
  2. Reliability and communication. When you and your collaborator work in different offices (or countries), it can be easy to fall off each other's radar and let the project fizzle out. To avoid this outcome, demonstrate that you're serious about the project (even if you don't have spectacular results yet) and that you want to interact with them occasionally. 
  3. Openness to feedback. A big part of collaboration is giving each other feedback. When the person giving you feedback is not in your field, it may feel like they're impinging on your space. When this happens, pause for a minute - they might be giving you a fresh, valid perspective. Or, they might just need you to better clarify/justify what you're doing, which can be a preview of how an outside audience might respond. 
  4. Understanding capabilities and limitations. Everyone has some things (experiments, simulations, etc) that they can do routinely, other things that take more time/money/pain, and some things that would be desirable but are unfeasible. These things may be obvious to someone in your field, but you and your collaborator may need to discuss them to ensure that you both have a realistic picture of what the other can do. 
Have you been, or do you want to be, part of a collaboration? What did you get (or want to get) from the experience?