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! 

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.

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.

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? 

Trophy papers

Getting a paper into certain journals is good for one's career. These papers usually represent impressive and important work. It seems that many more such manuscripts are produced than the number that can be published in high-profile journals, such as Nature. It's probably not a bad thing to submit a manuscript to a high-profile journal if you think you have a chance there, but these attempts often generate considerable frustration, for reasons ranging from peculiar formatting requirements to rejection without peer review. Some researchers believe in a piece of work so much that they are not deterred by these frustrations and keep submitting to one high-profile journal after another. This enthusiasm is admirable, but if repeated attempts fail, then the level of frustration can become rather high because of the wasted effort. I wonder how others handle this sort of situation. Do you put more work into the project? Do you submit to an open-access journal? Do you move on to the next desirable target journal and take on the significant non-scientific work, such as figure layout and reference formatting, which a manuscript revision can sometimes entail? Do you wonder if the manuscript is fatally flawed because of the initial attempt to present the findings in a highly concise format? Please share your thoughts and experiences. Should we even be trying to do more than simply sharing our findings?

Etymology. (Not to be confused with entomology.)

It's time I explained where the name of the blog, "q-bingo", comes from.

It started last year at the q-bio conference, which is a conference focused on quantum quixotic quantitative biology. Like all fields, quantitative biology involves a certain amount of jargon and buzzwords, and certain words crop up more often than they would in everyday conversation.

And where would you hear those words most often? Conferences, of course. In fact, you might start keeping track of how many times certain words come up, and wonder if anyone else is keeping track too...

And thus, q-bingo was born. Simply cover a square whenever you hear a word used in a talk, and when you fill a straight line shout "q-bingo" straight away. Yes,  right there during the talk. [Disclaimer #1: I made this suggestion fully aware that my own talk would be punctuated by a few "bingo"s. Disclaimer #2: There are other examples of such games.] Conference organizers and attendees seemed to love the idea. Sadly, the game didn't quite get off the ground due to the issue of having to print 200+ of these things for everyone at the conference. 

On the other hand... At an immunology meeting, I wouldn't necessarily find it noteworthy or funny that people use specialized words like "clonotype" and "Fab fragment". So why did these words jump out at me?

• I think part of the reason is that some of these words are used to create a certain impression rather than to communicate information. For example, the word "complexity" is often to used to throw a veil of sophistication over something, without explaining what makes the topic complex. Same with "network" and "circuit", to some extent. 
• Other words, like "incoherent" (as in incoherent feed-forward, which is a simple pattern of interactions/influences) can mean vastly different things to other scientists and to the general public. 
• A few words aren't actually objectionable or amusing - they capture ideas that people are excited about at a particular time. There were several talks about the importance of "single-cell" measurements because of cellular "heterogeneity". 

I want to hear your feedback. Are these just buzzwords, and should we try to use them less? Or are they signs of a young-ish field finding its own language? And of course... if you have ideas for q-bingo words, let me know in the comments because we might need them again this year. 

How to make yourself understood across field boundaries

It seems like everyone these days is excited about "interdisciplinary science", which is much like regular science but with a longer list of affiliations. Working together means talking together, which includes making presentations that appeal to people in different fields. Is there anything to keep in mind beyond generic advice about giving a talk (develop an outline, make eye contact, don't mumble, etc)?

Here are two pitfalls that I've noticed when people speak for "interdisciplinary" groups:

• Tunnel vision. A speaker ignores the diverse backgrounds of audience members and assumes they all share his or her knowledge and interests. As a result, the speaker doesn't provide enough basic information for audience members to understand the talk, or to appreciate why the talk matters. 
• Self-effacement. A speaker goes too far in catering to an audience and loses their own point of view as a result. I once heard a talk from a bioinformatics researcher. They seemed to think their audience contained only chemists and, furthermore, that no one would want to learn anything about biology. As a result, the speaker tried to avoid touching any biological details. The result? A deluge of vagueness and abstraction. 

What can we do to avoid these extremes so that speakers and audiences can meet in the middle? 

1. Scope out the audience beforehand. Learn about your potential listeners. Think about what they're likely to know or not know, what they might feel strongly about, and why your talk is relevant to them. They might have more in common with you than you think. People are usually eager to latch onto something that connects to their interests, even tangentially. It's usually a good thing, but beware... 
2. Stay in the driver's seat.  A handful of times, "I know about what the speaker is talking about" can devolve into "I need to prove that I know more about it than she does, especially since I consider myself more of a specialist".  As a speaker, it's your job to address questions/comments thoughtfully. However, if someone tries to derail you - and you'll know it when you see it! - it's also your job to stay on track and remind them that you're the one giving a talk, which is different from a one-on-one meeting. 
3. Make good use of pictures and examples, which can help make ideas more concrete. A well-made diagram will make descriptions/equations/algorithms more approachable, especially to someone new to the subject. 
4. Appeal to shared problem-solving tendencies. If you're talking to scientists - or to humans, generally* - it's likely that even if you have different backgrounds, you share an instinct for solving problems. Try to give your audience the basic information needed to answer a question. Then give them a chance to work out an answer before showing them your results. No need to demand a verbal response (which can get awkward), but you want their brains to work while they listen. It'll keep their attention and make your research process more relatable. 
*If you're talking to non-humans, I would love to hear about that.
5. Have some faith in your listeners. I've come across many blanket statements, like that biologists always quail at the sight of equations, or that one must never utter gene names in the presence of a physicist. Although these statements may allude to general preferences, we need to remember that people aren't defined by what subject their degree is in and, related to point #4, people like to learn. Try to gently and non-patronizingly lead people out from their comfort zone. If you can show them that something they thought was incomprehensible is actually not so bad, you'll help them feel smarter rather than dumber, which is a big step towards bringing together people with diverse backgrounds. 

What are your thoughts? Have you had any interesting experiences when presenting your work to others?  

When (specialized parts of) two heads are better than one...

A recent review highlighted the small army of databases that has sprung up to help keep track of what we're learning about cells. Many of these databases focus on a particular feature of cell signaling (like protein-protein interactions or post-translational modifications), with a few databases combining information across multiple features to help build a more complete picture. A question that remains is how these collections of information can be used to help us achieve practical goals - identifying drug targets or predicting the physiological effects of mutations.

Computational modeling could have a role to play by turning descriptions of interactions into quantitative predictions. As databases tend to be managed by groups of people, one might expect that large-scale modeling projects could also benefit from a community-driven approach. However, modeling tends to be carried out by individuals or small groups. Are there ways to turn modeling into a community activity?

A first step is probably to put models into a format that is easy to navigate and that encourages interactions among people. One such format is a wiki, and there are actually a few examples of wikis being used to simultaneously annotate models and to consolidate information about a signaling pathway - a little like an interactive literature review that you can simulate on a computer. I think this is a cool concept, although it seems like these wikis tend to stop being updated soon after their accompanying paper is published. There have also been some efforts to establish databases for models, which would in principle make it easier for people to build on past work. But in practice, so far, it seems that these databases are not very active either.

  

[Review]

The issues involved in community-based modeling is also something I thought about when I read (the verbose yet interesting) "Reinventing Discovery" by Michael Nielsen, a book that advocates for "open science": a culture in which data and ideas are shared freely, with the goal of facilitating large-scale collaborations among people with diverse backgrounds. The underlying motivation is that progress can be accelerated if problems are broken down into modular, specialized tasks that can be tackled by experts in a particular area. I can see how such an approach would be beneficial in modeling and understanding cell signaling - a topic that can encompass everything from ligand-receptor interactions to transcriptional regulation to trafficking, each of which are complicated fields in their own right. So, how can experts in these fields be encouraged to pool their knowledge?

Nielsen's book has many examples of where collaborative strategies in science have succeeded and failed. As it turns out, creating wikis just for the sake of it is not always a good idea, because scientists often have little incentive to contribute. They would (understandably) prefer to be writing their own papers rather than spending time contributing to nebulous community goals. It seems like in most examples of where "collective intelligence" has succeeded, specific rewards have been in participants' minds. There's Foldit, the online game where players compete at predicting protein structures. And perhaps the most famous example is Kasparov vs. The World. (It's noteworthy that in both these examples, many participants are not trained professionals in the activity that they are participating in - structural biology and chess, respectively.)

I wonder what the field of cell signaling can learn from these examples. Does there need to be a better incentive for people to help with wikis/databases? One might imagine a database where an experimentalist can contribute a piece of information about a protein-protein interaction, which would automatically gain a citation any time it was used in a model. Or, can some part of the modeling process be turned into a game or other activity that many people would want to participate in? It seems like there are a lot of possibly risky, but also possibly rewarding, paths that could be tried.