Post tenure, my answer is to just be me

I have no clue how many you will feel this way or find this post helpful, but I try to be true to myself in everything that I do as a scientist and part of that is wearing some thoughts on my sleeve right out there in the open for all to see.

About two years ago I received tremendously awesome news. The University of Arizona was going to grant me tenure. Literally the one thing in my career that I had struggled towards and worried about and I made it!

I had read about what some had discussed in terms of a "post-tenure depression". There are a ton of posts out there describing such feelings. A lot of the ideas seem to boil down to the idea that once you attain what you've been working for, there's a period where you question what else there is. I had some other things going on in my life (see my previous post about the circumstances around my son being born),  but I can honestly say that I experienced a post-tenure lull in my career. Never thought it would happen to me, but there it was.

Don't get me wrong, I love my job and still love my job. However, every lab and every department and every University have their own little weaknesses that can become problems. When you start to look around and evaluate your career,  you really notice these things and it can wear on you. It certainly did for me. I have never worried about coming up with new experiments and ideas, my greatest worry is finding the money to carry out these experiments. Writing grants is hard. It takes A LOT of emotional energy to pull together a new grant and it is completely devastating when your grant is rejected. Given funding rates in the US, chances are pretty good that you're going to see many more grant rejections than funded grants. It wears on you, the grind of writing and rejection and rewriting and rejection and rewriting and government shutdowns and rewriting and now that funding program doesn't exist anymore, etc etc etc...At the same time you start to look around and compare yourself to other scientists, it's inevitable. It's human nature to wonder things like how did that project get funded? How come that paper was accepted to that journal? Why does this set of experiments keep failing? On and on, all of these thoughts and doubts pile on to each other. "But you're tenured, you should be happier" people say. It's easy to lose perspective for a bit. We're all human, recognition feels really good when it comes and it's super difficult not to feel jealous about others' good fortune.

For me, the key was to double down on what got me here. Focus on my own happiness and the rest will come (or it won't, that's OK too...).

It's taken me a good two years, but I definitely feel much better about my career than in the couple of months post tenure. I've decided to keep being  me, to do science that I love and am truly interested in and to not worry about the small stuff. I'm going to teach a bit more, and  maybe that buys me some karma from my bosses in case grants don't come through. I realized that one of the best things about science is the friends you make along the way, and I absolutely love talking shop with other scientists. I've made some pilgrimages back to the places and the people that mean a lot to me and have helped me along the way and that's certainly helped to bring me out of whatever funk was there. I've made a lot more time for my family and for enjoying life outside of science. I've tried to really celebrate others' good fortune and give them kudos when possible. At some point, we all are just trying to hang on and survive, and those little kudos go a long way. 

I have no clue what the future holds, but I'm going to try and keep sciencing until retirement. There  are going to be ups and downs, but it's still incredible to walk into the lab and see that an experiment worked. It's incredible to feel like you've just discovered something completely new about the universe. That's enough for now, even if the government ever opens back up and my grants are rejected.

Taking Stock 2/n How much should I work vs. how much did I work

Six years ago (nearly to the day) I started my lab at the University of Arizona. There have been numerous ups and downs in science and outside of science, but I'm still here. My tenure package was submitted last August (I've gotten good news but nothing official yet), we've just undergone about the third dramatic change in lab personnel, and there's plenty on the plate for the future. I figured it was a good enough time to sit down and begin to reflect on these last 6 years and dream about the next 30 or so. Not sure how many parts this is going to be, hence the /n in the title, but for this second post I'm going to at least provide my own slanted perspective on work/life balance. 1/n here: http://mychrobialromance.blogspot.com/2017/01/taking-stock-1n.html

Every once in a while the same arguments pop up on the tweets...they go something like this:

Passionate about science is great. But if we don't respect science as a normal 40-hour-per-week job, then we are hostile to diversification.

— Terry McGlynn (@hormiga) February 18, 2017

followed by

Disagree- if you are a GS and aren't living and breathing your research, do something else. https://t.co/S9IFWT9nPO

— Andrew Kern (@pastramimachine) February 18, 2017

and it escalates after that...

For clarity- I'm advocating that everyone sell their organs to fund their labs. https://t.co/HnMfl6Fkzs

— Andrew Kern (@pastramimachine) February 19, 2017

and so on and so forth.... Side note, for a great thread(s) check out what followed this tweet:

Okay, wading into something here and heading to bed....*taps mic*

— Needhi Bhalla 💅🏽 (@NeedhiBhalla) February 19, 2017

 Healthy discussion is great, sharing different viewpoints is great. They are all valid points, and much of the required nuance is trashed by the 140 character limit and difficulty in relaying tone because twitter.

I fall squarely in the camp that every single one of us has different work schedules and different requirements for how much to work in order to be "productive". Terry McGlynn hit the nail on the head exactly when he mentioned that one of the main problems with discussions about work/life balance in academia is that we inherently (ed: often not openly) disagree about what constitutes "success". Some want to win a Nobel prize. Some want to make a difference in the lives of those who have no one else to encourage them. In many ways, these are equally difficult challenges. There are many variations on what "success" is, and they're different for everyone.

One of the greatest challenges I've seen with being a PI is that, in the US system at least, we become used to judging our own worth compared to other people's metrics. Did I get an A in the class? Where do I rank with my GPA? This doesn't ever stop. Did I get into the program I wanted? That person in my cohort just published a paper, why haven't I published a paper? That person works more than me? and so on and so forth. At some point (I'd argue in grad school) we lose the ability to have definitive metrics to compare ourselves against and this is completely unsettling when your career is firmly placed in the context of data gathering. That first day you step into your empty lab space, you try and grab onto any foothold you can to try and gauge whether you are doing *enough*. Ideally, you have great mentors that can guide you along the way and offer advice, and ideally you are getting feedback from those on the tenure committee and during annual reviews, but there's no magical metric to tell you whether you're doing *enough*. Don't even get me started "how many grants and papers you need for tenure" because it's different for different people even in the same department. Allow me an (American) football metaphor for research...some teams try to score a touchdown every single play. Some teams are content to move the ball 3 yards forward every single play. You can be successful using both strategies and mixes of both but both require you to just move the ball forward by the end of the game. I'm not sure where defense plays into this, but I was on a roll so there you go.

There have been weeks when I've worked 80 hours and those that I've worked 0. There have been days when I've worked 24 hours (usually before a grant deadline). There have been many days that I haven't worked because (at least for me), I need to take at least one day fully off every week to stay sane. I tell the people in my lab that my metric for judging whether things are on the right track is that we should set up mutually agreed upon goals and work towards making constant progress towards these goals, and yes, I count failed experiments as constant progress. Aside from having to OK hours on a timesheet, I don't really keep track of how much or how little people work. I figure that everyone has their own rhythms and effective times and encourage them to make the most effective use of their time. I try to set an example through my own actions, but I don't expect them to exactly copy my work habits. Have goals, keep your eye on them, work enough to give yourself a fair chance at accomplishing them. Reevaluate the goals frequently because contexts change. Perhaps the second greatest challenge I've had as a PI is learning to have empathy for other people's working and learning styles. It's not easy, but it's incredibly important. That said, I understand and respect the other side of the argument but it's just not for me. We often have the opportunity to choose what we want in the labs we join, and I think the best we can do is represent how we view work/life balance and *success* so that those that are actively choosing can make an informed decision.

This is getting long, so one last observation about what "work" is. I've learned that I do my best writing and thinking when I'm running or otherwise being active. I've written a lot of my papers while not actually writing. I've written a lot of my papers and grants while not "at work", while in the shower, sometimes in dreams (for real...just kind of wake up sometimes and write stuff down and it's coherent). Does this kind of *work* play by a 9-5 schedule? Is it actually work? I have not clue...but it's what I've done and it's what works for me. I think this was Kern's point above before the nuance was lost to the twitter Gods.

Teaching Microbiology in the Era of #Fakenews

Wanted to briefly post about an interesting situation I'm encountering right now in my Microbial Genetics class. We just had a quiz exercise where I effectively asked this True/False question:

True/False During bacterial transcription, RHO-DEPENDENT terminators utilize a hairpin loop

To me, and my understanding of transcriptional termination, the answer is clearly false as far as science knows right now. I went over factor dependent and independent terminators and focused on how factor independent terminators can be identified by hairpin loops in the DNA/RNA sequence (usually followed by something like a run of Poly-Us) and contrasted this with how there was only a sketchy signal for rut sites in terms of rho-dependent termination.

It sucks to be wrong, and especially to make mistakes when lecturing to a class of undergrads, but there is a part of me that loves it when I get challenged by what I've said with actual data. It's a learning opportunity and I enjoy when students go above and beyond to find other sources of info.
My standing agreement with students is that if they can present me with primary literature that demonstrates their argument, I'll give them points regardless of how the quiz was originally marked. I think that's only fair...

However, a couple of times I've run into a situation this semester where students cite non-primary lit sources to demonstrate their point. In the case of the quiz question above, they've cited a couple of YouTube videos about Rho-dependent terminators (here and here) that AFAIK incorrectly state that rho uses hairpins.

I see this as a very small battle in the larger world of #fakenews where we are constantly barraged with other peoples digested opinions and views rather than read the original undigested words. Regardless it's troubling. I'm going to mention this in class example in class today, and point out that "when in doubt find a primary source and look at the original data" is a great go to for deciding what's "real" in these situations.

Taking Stock 1/n

Six years ago (nearly to the day) I started my lab at the University of Arizona. There have been numerous ups and downs in science and outside of science, but I'm still here. My tenure package was submitted last August (I haven't heard back yet but expect to soonish), we've just undergone about the third dramatic change in lab personnel, and there's plenty on the plate for the future. I figured it was a good enough time to sit down and begin to reflect on these last 6 years and dream about the next 30 or so. Not sure how many parts this is going to be, hence the /n in the title, but for this first post I'm going to try and be open about self care needs and the internal struggles that I'm guessing a lot of us face. 

I've always been of the mindset that it's not about how much you work, but whether you make progress towards goals in that time. I've tried to encourage my lab members to take care of themselves mentally, to take breaks when necessary and enjoy the flexibility that comes with working in a research lab. I'm well aware that that line of thinking works for me, but it doesn't work for everyone and so please don't take this as a prescription for what to do but as an example of what's been done. For five years I thought I had everything under control. Sure, research and publishing and funding is always a struggle but it's been a manageable struggle. That all changed seven months ago and I'm still recovering.

My wife's pregnancy had been pretty standard up until early June. At that point she was about 29 weeks pregnant, but was also self employed as an equine vet and still going out to calls. She had been around horses all her life and has cultivated an intuition around these animals that's second to none. Was I worried about her job and my unborn son, deep down yeah. However, I trusted my wife to take all the necessary precautions and be careful, and by all measurements she did and was. I still remember that call in early June because it's one of those moments where time stands still. She had been kicked square in the stomach by a horse, it had thrown her back into a wall and there was a deep cut in her head. Maybe a couple of broken/chipped elbows (never did figure that one out, it was the least of our worries). She was being rushed to the hospital, no clue of how bad anyone's injuries really were. Not going to show you the pic, but there was literally a horse-shoe shaped bruise right on her pregnant stomach.

These are the kinds of situations where your mind tries to prioritize everything into what's essential and what's non-essential. Essential was me getting to the hospital ASAP bc my wife was in and out of consciousness and they didn't know how bad the head trauma was. They didn't know what condition my son was in, there was really no way to know if he had enough oxygen, how badly the internal damage was. It's a unique method of torture, to be a researcher and be incapable of truly assessing how bad the situation was because the tools simply don't exist.

Fast forward and my son was emergency C-sectioned at 29 weeks. Somehow his head was pointing down so that if the horse landed any blow it was a glancing one on his legs. We still don't know if he lost oxygen in the womb at all, but the placenta was damaged. My wife pretty much stayed in the NICU for two months straight (plenty of stories about that for other times), while I watched my three year old daughter at home during that time. It's a testament to the flexibility of our jobs as researchers that it was possible for me to do that. Somehow I managed to get my tenure packet submitted. Somehow I managed to write a couple of papers. Somehow I managed to make the slightest of progress in the lab during that time. Do I remember much of it, not really. I was just focused on getting by day to day one step at a time. There was a lot of stress in directions that I wasn't ready for, but I grew numb to it all for the sake of getting to the next day. It wasn't really easier when Kyle came home from the NICU, but my wife is a superhero and I'll leave it at that.

Why do I mention all of this? Part of it is catharsis. Part of it is that I haven't had the time to properly thank everyone for their help during this time, and so please take these lines as a thank you. All of the support meant so much and made everything easier to cope with. I mean that. I'm also writing this to say that, it has to be OK for us as researchers to be given time to get through difficult situations. Give your people the space they need (within reason). Give your people the help they need whether it be mental or physical. Whether you are an undergrad, grad student, postdoc, PI...life can be extremely challenging. I picked this career because of the flexibility and this summer solidified that. Everyone deserves the support that I received regardless of station.

I'm also writing this to describe that I'm not completely back yet. I've tried a couple of times to sit and write grants like I did before, to just pound them out, and I can honestly say that I don't have that gear back (hopefully it comes back...having a 7 month old doesn't help with sleep patterns). I'm getting back to how I felt pre-summer of 2016, but it's happening more slowly than I'd like. I would love to just flip a switch and have everything feel like it did before, but it doesn't work that way. I can't help but think that those months of emotional numbness that it took just to survive have left a bit of an emotional hangover. I've had more science energy lately and I feel better and more energetic every day about my research. I don't know how the story ends, but I do love both my family and my job. Sometimes you don't get to choose how to devote your emotional energy. With help, it's possible to muddle through to a more stable place. I've been way luckier than many and I'm looking forward to what the future has to hold. Still taking things one step at a time though, and that has to be OK too. 

mSphere Direct and Peer Review

I guess I've gone through the inevitable "slowdown in blog posts" phase of the blog. To be honest, it's not that I haven't had things to say, it's just been quite hectic as many of you know. More on that later...It's taken some time, but I've found my footing again so it's time to blog some more.

What's drawn me out today is the unveiling of a new take on peer review and publication by one of the societies to which I belong, the American Society for Microbiology (ASM) (see descriptions of mSphere direct here and here). ASM hosts many different journals under its umbrella and I try to publish in ASM journals when appropriate. From my perspective, it's a great society consistently headed by some forward thinking people. Despite some internal disagreements about direction across the membership, the divisions of which are readily apparent if you've ever attended the ASM Microbe conference, I'm going to still be involved and publish there because it's important to support your societies.

A year or so ago ASM started two new journals, mSphere and mSystems. My tl;dr take on mSphere is that it's a useful addition to the cannon which seeks to minimize waiting time to publish manuscripts while filling the niche of a slightly more microbe-centered PLoS ONE. A lot of good papers thus far there, with less emphasis on flashiness of the story and more emphasis on supporting good work. As far as I can tell, mSphere direct will be a new track for publication into mSphere with the goal of relaying "quality" and peer reviewed work as fast as possible.

The mechanism by which mSphere direct is going to minimize waiting times in peer review is that they will now allow you (the author) to seek out and suggest your own reviewers. You will solicit these reviews, and then work with the reviewers to come to agreement on a publication quality manuscript. In some respects this is kind of what we all hopefully do with co-authors anyway (in the perfect theoretical Platonic world where everyone has infinite time to review papers that your name is on with a fine-toothed comb) except that the reviewers will not be co-authors. Instead, the reviewers names will appear along with the paper as a method of accountability. When you submit to mSphere direct, you will submit these reviews and review history and editors at the journal will give you a thumbs up/thumbs down within a few days. No revisions after that, just yes or no. If yes, it's published. Now, this is the natural marriage of two different ideas that ASM/mSphere have implemented in the past: 1) allowing ASM fellows to solicit reviewers along a "fellows track" and 2) mSphere allowing you to include previous reviews from other journals along with your submission to speed the process. I'm guessing that both of these previous ideas have worked reasonably well, to the point of mSphere starting mSphere direct.

I'm guessing that a lot of your own opinions about mSphere direct are going to depend on what you view as main problems with current peer review. There are plenty of problems to choose from, but we all have our own focuses of priority on problems that "really matter" compared to those that are just "burdens" on the system. It's not my place to tell you what to think about this, we all have our own priorities, but I am going to tell you what I think....

mSphere direct's goal is to reduce waiting times to the publication of peer-reviewed manuscripts. Full stop. In my opinion, the advent of preprints have somewhat blunted this problem (obviously depending on how your own scientific discipline has embraced preprints and scientific priority). To me, you post a preprint and so long as the story is scientifically valid that establishes priority. Others might think priority is only established after peer review...that's their prerogative. I'm not a zero-sum worldview person and so I think both can exist at the same time and both work, but given multiple thought lines, this is inevitably going to lead to disagreements that screw over people. I don't know a way around this, suffice it to say that problems of scientific priority have existed even in the "old days"...note how many Nobel Prizes have been awarded to those working in animal systems for phenomena that have been previously described in plants. C'est la vie. I also think that a hybrid system utilizing thorough comments on preprints followed by submission to mSphere direct should also be a valid track and that's up to the editors to decide (I would definitely strongly support such efforts though).

What I and many others that are hesitant about mSphere direct worry about, is that we prioritize faithfulness of the review process over publication times (again, my worldview is heavily biased by being a preep-er and thinking that preprints establish priority). I can also speak to my fears growing up in a scientific world where a similar publication track at PNAS led to <multiple facepalm> levels of crappy papers. I worry that the review process of mSphere direct will enable more crony-ism in science where you pick your buddies as reviewers (or heaven forbid actually offer payments for good reviews) and then submit to mSphere direct. Right now it's on the editors to ensure that the review process is thorough (I'll put my faith in ya'll so long as quality papers come out of this track) as well as firm commitments to avoid reviewer COIs. I can speak as a researcher that avoidance of COIs, while a good first step, will not eliminate crony-ism int the review process. Our best reviewers are those that disagree with us but which we can convince that our story is legit. This type of review process will inevitably soften the review process (for better or worse) because the author will know who the reviewer is and because we all have our scientific friends and enemies. If everyone published with mSphere direct, there would be less worry about "reviewer 3" but...and although it sucks when you get harsh reviews...so long as the reviews are fair they make the manuscript better. Naming reviewers helps, but it doesn't eliminate the problem.

Overall, I'm cautiously hopeful about mSphere direct but I think they can make one change that will solidify my faith in the journal. Aside from publishing reviewer names, publish the reviews and responses of each paper along side the paper. Let us as critiques see which of certain valid points about the paper were brought up by reviewers and dealt with by authors. Give the reviewers that participate more of an incentive to do a great job reviewing (other than just having their names associated with paper review). I suggest this out of hope, and out of wanting to see my scientific society survive, thrive, and revolutionize.

Reconciling the Hologenome

There have been a couple of rounds of back and forth in print and online (here, here, here, here, and some shameless self promotion here and here...among multiple other places over the years). There is apparently an mBio special series starting today that teases a "heated discussion" akin to discussion about the neutral theory.

I don't have skin in this game (yet), and have tried to see and understand the points from both "sides". I lean towards the view in the Douglas and Werren paper, but I also see value in ascribing names to concepts. Yes, there are actually valid points on both sides. However, I'm starting to get frustrated by the whole discussion simply because I can't see a reasonable goal to work towards, and to me it seems as though both sides are starting to talk past one another. I won't go too much into it in this post, but I'll try and describe how I see the crux of the disagreement. It makes sense in my own head, but apologies in advance for what I get wrong.

It seems to me that many on the "anti-hologenome" side (for lack of a better word) seem to argue that we have all the models we need right now to get to the heart of interactions between hosts and microbiomes. They point towards GxE, GxGxE, multi-level selection, etc...and cite papers showing that these topics can be dealt with using the tools available. Why invent another term? I feel the frustration that these researchers have because (disclaimer, again this is IMO right now and can change if I'm presented with a great argument or explanation) the holobiont/hologenome concept has some important internal inherent disagreements. To illustrate, I've seen multiple "pro-hologenome" folks say that the utility of the hologenome lies in being able to describe natural selection acting at the level of the host+microbiome. That there is some value added by considering these two entities together. That's all well and good, but if you define the hologenome in terms of selection than there isn't any good way to fit neutral microbes into this worldview (because selection won't affect them). It's an internal inconsistency that Seth Bordenstein has already tried to convince me doesn't exist. All I'm saying, you can't point towards being able to describe selection better as a holobiont (and have this be the main example of why the concept is useful) and then include subsets of the microbiome that are neutral.

To me, the "anti-hologenome" side wants to see the hologenome concept as something that can be a predictive tool for modeling species interactions. The concept is certainly not there yet and I'm not sure if that's really where it's meant to go (see below). The holobiont definition gets a bit fuzzy when you push the limits as many on the "anti" side seem to want to do. Where does it end? What microbes are included? What about non-microbes that are vertically transmitted? The answers may be available, but I haven't seen a clear articulation of this anywhere (sorry Seth). I *think* much of the "anti" side frustration stems from not having clear/crisp enough definitions of the terms involved or the situations where the hologenome concept may apply. Not clear enough for predictive modeling purposes anyway.

Like I said above though, there is value in the hologenome concept writ large! If you squint your eyes and take the 30,000ft view, it's great to have a term that describes natural selection acting at levels greater than single organisms. We can use these terms for public communication of why microbes and microbiomes are important and not to be feared. We can use these terms to capture the imagination of burgeoning scientists or those outside of the fields that we work in. The utility of the holobiont concept is about communication and you don't need to get deep in the details for this to be true. We do actually need another term and another way to explain things like GxGxE and multilevel selection because the older terms aren't cutting it. We don't need a holobiont concept to create new ways to model multi-level selection, but I don't think that's ultimately what it's for.

That's how I see it, and I'm firmly trying to straddle both sides.

Trying to build the cheapest computer that can run an Oxford Nanopore MinION Part I

One of the most difficult things to do in life is to admit that you don't know something. Along those lines, it also very difficult to stumble through learning things (and to make mistakes) in full view of the public. I'm about to do both, but hopefully this process is useful to someone out there. Also, feel free to chime in and tell me where I'm making huge mistakes.

Like many of you I've been following the sequencing developments from Oxford Nanopore pretty closely, and the results have been awesome so far. I'm excited to get a MinION and start playing around with collecting data. However, the first step in the process is finding a computer that actually meets the minimum specs to run a MinION (found here). The most important parts of this (at the moment) are that the machine must run Windows 7 or above, have a solid state hard drive that's big enough to handle the data coming off the machine, have 8Gb of RAM, have an i7 chip, and have a USB 3.0 port. Well, the only windows machine that my family owns is my wife's work computer (it's got the specs but there's no way in hell I'm messing with anything on that machine, out of fear). There are also a bunch of other folks who have reconfigured their other non-Windows machines to run windows, and while I could do that I wanted to take a different tack. So, my goal was to try and build a machine that meets the MinION specs while spending the least amount of money possible. Here goes....

I have hardly ever opened up the case on any of my computers before, and I would equate (at the start) my knowledge of what's inside a computer to "6 year old that is curious about how to make their gameplay better". Luckily, there are a ton of different forums, videos, and websites to guide you through every step of the way. Just know that no matter how much you learn, there are many 8+ year olds out there that can run circles around your computer building skills. I'm cool with that, I just want to build something that can sequence DNA using a USB stick.

First step was to survey the field and figure out what I needed to do. It turns out that Dell and HP build a lot of machines with decent processing power for business purposes that don't really have all the bells and whistles that other computers have. Since (I'm guessing) that these are all bought by businesses and then sold after a few years, it turns out that there are A LOT of these machines that you can buy refurbished. I didn't want to build a computer from scratch (too much of a leap), so I thought it might be good to find a refurbished tower that I could upgrade in other ways to meet the minimum specs. Things I looked for:

1) PCI express slot so that I could add in a USB 3.0 port
2) i7 chip
3) Running Windows 7

Turns out I found a machine that had all of this, and that also has a lot of space for expansion, for 279$ (HP Elite 8200 SFF). There are various flavors of "HP Elite 8200" machines, but I stayed away from the Ultra Slim model just because there wasn't a lot of capability for expansion. Likewise, I stayed away from the regular desktop because it was more $$$.

Here's what the inside looks like:

Next step was to buy a USB 3.0 upgrade, and there are a variety of different versions. If you go with the machine above, you want to be able to fit it in an SFF case, and so you want one that has a bracket for "Low profile" machines. Found one on Newegg for 10$. This comes with the driver you need to load it onto the machine (and which apparently worked the first time), and fits right into the PCIexpress 1x slot. One problem that I didn't anticipate, is that you need to provide power to the USB3.0 ports outside of the PCIexpress slot (on the version I bought this is supplied through a 15 pin SATA connection). There apparently are various ways to wire this, but the refurbished computer already had a splitter going to the DVD drive so that all I needed to buy was a 15 pin Male to 15 pin Female SATA connector (here) for 5$, and Voila! Not sure if this works yet, still waiting for delivery.

Last couple of steps are upgrading RAM and the solid state drive. The RAM is easy, the HP machine I bought has four slots for RAM, and you can buy this on Crucial.com (Crucial will even scan your machine beforehand to make sure the RAM fits). I didn't wan't to go overboard, so I bought 2xGb of RAM from them for 83$. I *think* that RAM works better when it's paired, so that's why I bought 16Gb total, but you might be able to get away with 8Gb if you're going minimum specs. Those basically just snap into the RAM ports on the machine. Easy Cheesy.

Last thing was to buy a solid state drive. Again, I went to Crucial.com and ordered a 500Gb drive (you can supposedly get away with 250Gb with the MinION, but I was advised that bigger is better in this case). Same deal, you can scan your system and make sure you're getting the right part and then order. That drive was 150$ total. When the drive came, I detached the SATA wires from the DVD port and connected them to the drive itself. Then I used this handy step by step guide to clone the original Windows 7 running hard drive to this new solid state drive, then unplugged both hard drives and swapped connections (and took the old hard drive out). Last step was to start the machine up again and, DAMN, it felt good when that machine booted itself up after I swapped hard drives. The SSD doesn't quite fit well in the case, although as long as I don't move it it should be OK for now. Looking for solutions for that in the future.

So there you go, a dedicated machine that can run an Oxford Nanopore MinION (in theory) for about 500$. I'll let you know in the next post whether it passes the basic spec tests before I actually burn in my MinION.

Coevolution and the Microbiome

Coevolution is defined as cases where two (or more) species RECIPROCALLY affect each other's evolutionary dynamics. Reciprocally is highlighted in every way that I possibly could in the preceding sentence because this aspect really is key. It's a difficult concept to get a grasp on and an even more difficult concept to actually test in nature. I don't want to spend too much space going over the ins and outs of coevolution because numerous people that are smarter than I am have done that in accessible ways...My favorite being Dan Janzen's "When is Coevolution". (For other good lists that are slightly longer and more intense, see Scott Nuismer's or John Thompson's Google Scholar page). I'm missing a bunch of other stuff out there, but it's late so please forgive me and add any other great links in the comments.

I mention this because there is a lot of work being published on microbiomes right now, and I'm sensing a pretty strong tendency across manuscripts and presentations to state that microbiomes and their hosts have "coevolved". In some cases this is certainly true (best examples I can think of off the top of my head are nutritional symbionts in insects, some nodulating Rhizobia in legumes, Plasmodium in humans, and Vibrio-Bobtail Squid). Here's where it gets fuzzy, and I'm largely focusing on human microbiome studies here because they often get all the press...in many cases where researchers claim that microbiomes and hosts have "coevolved" there is absolutely no evidence that this has happened. Sure, there are trends and correlations that make it seem likely that coevolution has taken place. However, read the Janzen piece above again and find me a study where researchers have reciprocally tracked genetic changes in the microbiome and have seen direct evolutionary (read:heritable) changes in human host populations. It's nearly impossible (let alone ethically challenging) to track fitness in human populations over time and cleanly and directly relate those to changes in the microbiome. Codiversification != coevolution, so any story that mentions Helicobacter pylori and humans is pretty much right out (h/t to Jonathan Klassen for that one). Moreover, health != fitness. By definition, obese people are unhealthy yet they can still have kids at a decent clip (note, I'm unaware of studies actually measuring the fitness affects of obesity in humans but would love to hear about them if you know of any). Likewise, much has been made about H. pylori affecting human health negatively through gastritis/gastric cancers and positively through asthma/GERD prevention. I'd love to hear how these things directly affect fitness in human populations, but there's absolutely no data to these points. Proto-humans might well have had gastric problems from H. pylori, but I'm betting that there were many other things that directly impacted their lifespans and fitness with greater magnitude. Please don't get me wrong, I'm not saying that it's impossible that humans and their microbiomes have coevolved, I'm saying that there is no direct evidence that directly addresses the hypothesis of coevolution outside of a handful of pathogens.

All of this is a roundabout way of saying that studying (and demonstrating) coevolution is really really really difficult. You need to actually measure how species 1 directly influences evolution in species 2 AND how species 2 then directly affects evolution of species 1. Reciprocality is key. There are many examples where species 1 influences some trait on species 2, but that doesn't mean that evolutionary dynamics in species 2 will be affected. Likewise, it doesn't mean that species 1 will then be reciprocally affected by this trait change.  Moreover, we don't have good models of host-microbiome coevolution because the math is difficult/complex so we therefore don't have very clear ideas about what parameters matter most to drive coevolutionary dynamics. One of my near to mid term goals in science is to try and change this, so as a first step I'm trying (fingers crossed) to organize a workshop at NimBios (probably in Fall 2016) to bring 35ish smart people from across the globe together to talk about how we begin to frame questions about host-microbiome coevolution. If you're interested please fill out the form at the following link: http://goo.gl/forms/G79CwnhYc8

I'll be submitting a workshop proposal by March 1st, and will keep you up to date on what's happening. If you're curious about NimBios and workshops, overview can be found here: http://www.nimbios.org/workshops/

We tend to be harshest those we love...

Last year a paper was published investigating links between natural transformation and type VI secretion mediated killing, and I cobbled together a blog post with some select thoughts about this paper (here). It's recently come to my attention (should have written this earlier, it's my fault that I dropped the ball) that that particular post could be viewed in a couple of lights depending on your the context. I want to make it completely 100% crystal clear, that it is never my intention in writing in this space to include ad hominems in posts (I'm not Dan Grauer...)*. I write quickly and don't ever really sit on posts, but really I'm writing here because I love what I do and I care for certain topics in science. I'd like to see these topics understood as clearly as possible. For those that "know me", I really enjoy critical discussions about science and sometimes my words get ahead of my inner filter. Suffice it to say that I've made a conscious decision to try and not use this space to trash people and papers personally, but to be critical about science involving topics that are near and dear to my heart. Sometimes when I do this my inner New Yorker comes out, but know that my intention is not to critique the person but to focus on the science. If I do this, let me know and I'll directly tackle/ammend/try to fix the issue in public.

F'ing Bulls**t MT“@WvSchaik: 'Host Demise as a Beneficial Function of Indigenous Microbiota in Human Hosts' paper http://t.co/aHifo7Rx2y”

— David Baltrus (@surt_lab) December 16, 2014

Figure 1. An example of me responding a little quickly in public to a recently published paper

This being said, I'm writing this post because another paper was published recently that linked together natural transformation and microbe-microbe killing. Honestly, I haven't had the chance to read the paper yet because I was sitting in airplanes all day yesterday. I'm guessing that I would probably have similar thoughts as I did about the type VI secretion paper last year. These thoughts come out in public sometimes:

And the hand waving evolutionary explanations for linked regulation of competence and "trait X" continue. https://t.co/hCd1hXgehe

— David Baltrus (@surt_lab) February 3, 2016

Figure 2. An example of me responding to a recently published paper after sitting on airplanes all day and without reading the paper

All right, so what's my beef with these studies? Let me be completely clear, I respect the authors and inevitably I have no problem with the experimental design or the actual reported science. In the type VI secretion case, and probably with this new paper, I have absolutely no problem with the experimental design or with the genetics. I don't think the papers are wrong science-wise. I'm writing about these papers because I spent the better part of 5 years in graduate school huddled in the fetal position thinking about the evolutionary effects of natural transformation in bacterial populations. My problems with a lot of these papers are usually directed solely at the evolutionary interpretations and spin within discussion sections and press releases.

There's a historical legacy that surrounds researchers of natural transformation in bacteria, where there are a couple of entrenched camps that tend to argue past each other. These fights usually flare up around disagreements that conflate questions about original evolutionary benefits of natural transformation and benefits of natural transformation that are measurable today (after these systems originally evolved). After many years of thinking about this, I'm actually agnostic when it comes to the original evolutionary scenarios for natural transformation. Rosie Redfield is one hell of a thinker and I defer to her about such things (so I guess that firmly places me within one camp). I tend to be more interested in wondering about how strong the selective forces on natural transformation are within present day bacterial populations and on trying to figure out realistic parameters that could affect our evolutionary interpretation of gene exchange in bacteria.

Like I said above, I think the genetics and molecular biology within these papers are tip top and have absolutely no quarrel with those. I get caught up when the discussions start to extrapolate from  the controlled conditions of the lab environment into natural populations. Natural transformation certainly leads to gene exchange in natural populations of bacteria. However, suggesting that pathways are linked in regulation because of evolutionary benefits of natural transformation is a leap of faith that no paper out there has been able to tackle as of yet. What leads to the disconnect? There is a long standing tradition across many, many, many papers that describe evolutionary "just so" stories whereby we witness results in the lab or under certain conditions and think/assume that natural selection must act that way across many different conditions or environments. My comments about "hand waving" are usually directed at such extrapolations.

This is getting long, so I'll save the nitty gritty for another time. However, long story short, these extrapolations hinge on critical parameters of these experiments being similar in the lab and under natural conditions. There are no natural populations of bacteria for which we have realistic estimates of things like A) the DNA pool available for natural transformation B) natural selection pressures over space and time within and between bacterial populations that are exchanging DNA C) the repeatability and direction of these selection pressures D) how often cells encounter other cells that they can kill in nature E) having killed these cells in nature, how often these cells take up DNA F) evolutionary costs of natural transformation in nature G) I'm missing something because it's early in the morning but there are other parameters. When one sits down to write mathematical models that account for all of these above parameters, it ends up being REALLY difficult to find parameter space whereby natural transformation is GENERALLY beneficial. That's not to say that gene exchange doesn't matter within natural populations (as it certainly does) but it's hard to find situations where there are clear results where natural transformation is beneficial even a majority of the time. Under laboratory experiments like the ones in the type VI secretion paper (and probably the bacteriocin paper, again, haven't read yet) all of these parameters are actually controlled for pretty cleanly:

A) the DNA pool is controlled by the experimenters so that there is no contaminating DNA from other strains/species that could compete with genes of interest for uptake

B) Natural selection is really strong because these experiments are typically selecting for antibiotic resistance where cells pick up the relevant DNA survive and those that don't die. The same would be true if we experiments were set up to investigate phage predation, etc...

C) Typically in these lab experiments, there is only one direction for selection to act and the environment doesn't change over time (i.e. there is only one antibiotic that the cells need to become resistant to, and therefore one locus that they need to pick up through natural transformation)

D) lab experiments are usually biased so that cells are encountering cells that they can kill at pretty much optimal frequencies (50/50).

E) lab experiments are done under conditions whereby cells are highly competent for natural transformation

F) there are few costs for natural transformation systems in these lab experiments because the experiments themselves usually occur in relatively cush situations for bacteria (media containing abundant nutrients, controlled temperatures, etc...) and only take place under limited amounts of time. In fact, for most bacteria, if you passage them under lab conditions for extended periods of time they usually lower competence levels (which suggests an evolutionary cost). Like I said though, the lab experiments are only performed over limited amounts of passage time.

So to sum this all together, I apologize for any perceived slights. That's not my intention (yeah, I know get out the bingo cards). If you feel I've been too personal, please let me know and I'll try and fix anyway I can. There are many great groups focused on understanding natural transformation in bacteria and I respect much of their work. I've just spent way too much time worrying about evolutionary scenarios that usually pop up in discussion sections of these papers without (what I perceive) is firm grounding within evolutionary biology. These papers usually aren't usually set up to be direct tests of evolutionary theory, but it's very easy to write about how we think evolution should work. These papers usually end up being very good at describing the if natural transformation works for gene exchange under certain scenarios rather than how it's actually happening in nature. That's completely OK, just be careful about extrapolating.

*c'mon, that one was way too easy

How to (not) write a microbiome grant, part II. A deeper dive on preliminary data

As in Part I, a few quick notes that come to mind as I'm reviewing microbiome grants....


One of the biggest challenges and frustrations with grant writing is knowing just how much and what type preliminary data to include and how much detail on methods to provide. Within the context of the grant, preliminary data has a couple of different jobs. First, it's there to convince the reviewers that you can actually perform the type of experiments and analyses that you are proposing. Second, it's there to justify why the proposed experiments are interesting or necessary. There's certainly no magical formula, but I think there are a few things to keep in mind to when struggling over these two variables. (DISCLAIMER: just one person's opinion)

1. The amount of preliminary data required changes throughout the course of your career.

Don't kill the messenger, but track records matter. It's just the way it is. Early career researchers need to include more detail and need to justify their proposed experiments moreso than established researchers. If I'm reading a grant and I see that the PI has published (even as a preprint, because I can go and read the methods if there is a question in my mind) these kinds of analyses before, it's much easier to believe they'll be successful performing the proposed analyses. All else equal, that inherently gives established researches a leg up given page limits.

2. You must include enough detail to convince me that you know what you're talking about with the analyses.

If I'm reading a grant that proposes types of experiments that I'm familiar with, I probably have a decent idea of the associated pitfalls and critical variables. If you've done the experiments or understand how to do them well enough to carry them out, you should also have an idea of the critical points to include in your methods and analyses. It's very likely that, even if you don't have experience with specific protocols, that you'll know someone that does...do whatever you can to understand the ins and outs of the proposed experiments and write enough detail to cover the critical points. Assume that at least one reviewer is going to be familiar with the experimental protocols and include enough information to convince this reviewer you know what you're talking about. Assume that other reviewers may not understand the protocols and include enough basic information to give them an idea of what you're talking about.

3. The type of preliminary data required changes throughout the course of your career.

If you have a proven track record in the field, or if you've hit both of the above points in your grant, the preliminary data within your proposal should provide just enough smoke to convince the reviewer that there's a fire somewhere (metaphorical of course). It's very easy to propose "fishing expedition" experiments, one's where you are going to make a lot of observations and some magical result is going to come from combining together all this data.

When I was started as a PI, I kept proposing a few different RNAseq experiments that I thought would be very interesting and insightful. Inevitably, I'd get the reviews back and I'd get dinged for not having a hypothesis. "Pssssshhhhh" I'd say to myself as I gripped my stress relief ball, "The hypothesis is that gene expression WILL change!". With a bit more perspective gained from grant panel experience, I understand exactly now what the "fishing expedition" critique means. It's a combination of the psychology of having to review a bunch of related grants at a single time coupled with the reality that you have to bin grants into different piles as a reviewer.

Here's an example with microbiomes (in the style of Law and Order, this very example may be based on real events that are happening at this exact moment). Say a researcher has 10, 15-page microbiome grants to review before the panel meeting. A large percentage of these grants are interested in measuring microbiome dynamics over time, space, and across individuals. The methods and proposed analyses are usually very similar across a large swath of these grants. The only ways left to bin as a reviewer are by host species and whether there's enough smoke to think there may be a fire. If you can't make the case that your study system is different than other ones, chances are that your grant is going to get lumped in with those proposing similar methods and placed in the "others" pile unless someone else on the panel makes a good case during discussion.

Preliminary data is your way to make the grant stand out. If you are proposing that individual to individual variation matters, or that changes in microbiome dynamics over time matter, it's easy enough to get a few samples and sequence 16s. It doesn't have to be a full study, it just has to be enough to show that there is some signal that's interesting enough to follow up on. There's a surprising lack of pilot experiments in a lot of these microbiome grants (IMO), and the only thing I can think of is that it's hard to find sequencing centers that can process a handful of 16s samples relatively cheaply. I again assume that this is because you typically you need a certain threshold number of samples for a MiSeq run (vs. Sanger sequencing where you can perform just one reaction). One way to get around this is to find others that are interested in generating the same kind of data and pool resources together to pull together a whole MiSeq run. There seem to be a couple of places that could facilitate finding others to pool with (like GenoHub).

My null hypothesis as a reviewer is that microbiome dynamics are going to be the same in your system as they are in well studied systems. Use this preliminary data and pilot experiments to disprove my reviewer null hypothesis. Are there differences abundances or frequences for taxa that are important in other systems? For instance, if you're proposing a phyllosphere microbiome study, off the top of my head I can imagine a top five for the taxa you should find in high abundance. Is your system different (If you can't answer that, there's some reading you should do).  If you sequence a couple of plants, do the larger plants have differences in microbiomes that you can follow up on? Is there something unique about your microbiome of interest compared to others (i.e. the rice rhizosphere apparently has some archea). If there are differences between these experiments and previous ones from other systems, think about hypotheses to explain why these differences and build your grant off of that. "Sequence everything and sort out the important trends later" doesn't work when every grant is proposing to do the same thing.

4. "But I don't have any preliminary data to include"

Yes you do. It may not be your own, but there are enough public datasets for you to reanalyze other's work (to at least show that you can do the analyses and give yourself some sort of track record). It doesn't even have to focus on the system you are proposing to work in so long as it moves your narrative forward (see Points 1 and 2 above).

<slight update to point 4> There's a flipside to this. If you're proposing experiments similar to others that have been published before in the same system, don't just cite the previous papers. Give your reviewers a context for why your proposed study is going to be different than previously published studies.

How to (not) write a microbiome grant, Part I

I'm involved in something that I shouldn't talk much about, but suffice it to say I'm smack dab in the middle of evaluating multiple different microbiome grants. As such I'm the exact target audience you should be aiming to reach with your microbiome grant, literally, the exact target audience. There are a few grantsmanship things at the forefront of my reviewing mind right now that I'd like to get down in written form with the hopes of helping people out in the future (myself included). I'm especially wary going forward because there is currently a push to make microbiome studies the next BRAINI and we as a community are going to do ourselves a huge disservice if we get this wrong. The last thing I want to see is a huge movement for government funding within an area I care deeply about, only to have it not really come close to living up to the hype. I'll definitely have more to say on this as I digest some more, but here are a few things to keep you going:

1. Think about your hypothesis when designing experiments and keep this hypothesis in the forefront of your thoughts. 

Microbiome constituency is going to change over time at some level, it's going to change in some way with pretty much every manipulation you can think of. It is soooooo tempting to write experiments that focus on measuring this change over time, measuring how individuals differ, or that measure the variation associated with treatment X. Maybe one community changes at different rates than others? Maybe there is something magical and emergent that happens when you put all of this community information together?

When you are writing your grant, you are going to be couching the effect of the microbiome in terms of something. The microbiome is important for human/plant health, the microbiome affects geochemical cycling, the microbiome affects evolution of species, etc.....The problem I'm repeatedly seeing with grants is that the whole project is built with the idea that the microbiome will have an effect on X, and measuring how treatment Y affects the microbiome is important for understanding X, but to me at least it seems a lot of people forget to directly link treatment Y with its effect on X. If the treating the phyllosphere microbiome with jasmonic acid is going to change its constituency or dynamics, and jasmonic acid is therefore predicted to affect "plant health", please try to include measurements of "plant health" within your treatments. Keep the whole hypothesis in mind and don't just measure how treatment Y will change the microbiome and then assume that this change is going to impact X. Directly measure the impact of Y on X in the context of the microbiome.

2. It is very tempting to want to use the latest technology to measure "system level" effects of the microbiome. Proceed at your own risk, and with enough preliminary data to make me believe that you can adequately carry out the experiments. 

A couple of years ago, on a completely different panel, every grant seemingly included RNAseq. Now every grant is including metabolomics and metatranscriptomics. These technologies are awesomely powerful, and they will truly revolutionize some areas of science. If you are writing a grant, however, don't just say you will evaluate the microbiome with "metatranscriptomics". I want to see data for how many reads you might expect in a given environment (pilot experiments work wonders). I want to know that you are proposing to sequence enough depth to actually have a reasonable chance at seeing differences. Every system is different, and just citing papers that it's possible doesn't do the trick. This is especially challenging when studying microbiome communities within a host. Much of the metatranscriptomics work right now is being done in environmental communities, and a lot of the papers/technologies are being developed with these kinds of studies in mind. Any time you include a eukaryotic host in microbiome studies, you are going to get A LOT of eukaryotic RNA in your metatranscriptomes. Sure, pulling down with PolyA might clean up some, but for a lot of plant systems the chloroplast RNA isn't polyadenylated and is present at high frequencies. For 16s based studies you can design PNA blockers to limit the amount of eukaryotic contamination that comes through, but this doesn't work at a metatranscriptomic level yet. Sure, you can just throw everything onto a few HiSeq lanes and only use bacterial RNA reads, but as a reviewer I want to see enough information to convince me that using the "sequence everything and cull what you don't want approach" or maybe "the overkill-ome" is going to work. Tell me the fraction of reads that are host vs. microbiome, even if you only have this information from a small scale pilot study.

3. I don't ever want to see this in your grant:

Figure 1. Bad Hairball Plot. 
mage from: http://www.wired.com/wp-content/uploads/images_blogs/wiredscience/2013/11/bad_hairball.jpg

That is a plot of some random network I found with a Google search. Microbiome studies tend to sample many different taxa under different situations over time. It's tempting to put all of this interaction data together in a plot such as the one above, so that in your grant you can demonstrate that you do "systems biology". THERE IS NOTHING USEFUL VISUALLY IN THESE KIND OF PLOTS. In many cases, the nodes aren't even labelled. The only thing I can assume that this plot is trying to show is that you can do "systems biology". Grant space is so precious and limited, why waste it on a figure that doesn't relay any information at all?

If you do find interesting interactions, feel free to make a small figure including just a few nodes (labelled of course) that shows these interactions and explains what this interaction visually means. A hairball plot like the one above serves absolutely no purpose for the reviewer of a grant, and actually annoys me. Don't annoy the reviewer.

End of rant for now:)

Realized and Fundamental Niches in Academia

Two things I read last week motivated me to sit down and post some thoughts. Last night I found some time to read Jesse Shapiro's new preprint (because, for once, my kiddo went to sleep early and I still had some energy at the end of the day), which focused on using metagenomic data to tease apart how recombination and selective sweeps affect genetic diversity within bacterial populations over time. It provides quite a good summary of recent research into this topic and I found myself binge reading a bunch of newer papers late on a Sunday night. It's surely not for everyone, but I am and have always been fascinated by this research topic. When I finished grad school, this was kind of what I thought I'd be researching over the course of my career.

I'm a few months from submitting my tenure packet, and have been working for the better part of the last 5 years to sculpt that document. Over that time I've been lucky to have really good people working in my lab, and we've been lucky enough to be decently successful in terms of funding and manuscripts. However, even though all of the work we're doing is interesting and exciting, it has ostensibly nothing to do with recombination in bacterial populations despite my intrinsic interests. I wouldn't say that I'm sad about this, but I definitely have feelings that border on regret.

Couple these thoughts with a great blog post I read earlier in the week from Proflikesubstance describing "Tenure Funk". Since I don't have tenure, it's a bit premature for me to say anything about the feelings after accomplishing that goal, however I think I'm on a trajectory towards something that resembles a funk. As a PI you work so hard to find ways to carry out experiments, pay for research, and take care of your people. You have to do what you can to make sure that the lab survives. When I started my lab, a friend (also a PI) described having to "sell out" in order to find ways to fund research. There are no doubt lucky people out there that can do exactly the research they want and find ways to pay for it, but I think there are a lot of us out there that find that our "lanes" diverge from where we thought they'd go. You have to do what you can to survive, and this often means downshifting your energy away from experiments you love to pursue the fundable. Aside from $, there are also institutional pressures that direct your research. I'm in a Plant Science department in a School of Agriculture. I feel compelled to work on systems and questions that my whole department and school can easily relate to. Sure, there are bits and pieces of research that would allow me to investigate recombination in bacterial populations in agricultural settings, but I find these projects more difficult to sell across the campus/school than applied projects.

In ecology, researchers talk about fundamental and realized niches. A fundamental niche is the total space/role that an organism can theoretically fill in nature if not affected by outside forces. The realized niche is the space that an organism actually fills in nature. As we progress through our careers, we all find out what our realized academic niche is (this may be what type of college you work at, what type of research you do, what industry you work in, etc...). This often differs from our fundamental academic niche because outside forces affect the direction of our lives. At certain points in our careers (like tenure time) there are benchmarks that force us to sit down and evaluate how we're doing. It's at these benchmark times that the divergence between our realized and fundamental academic niches becomes apparent. I can see pretty clearly now how these realizations could lead to "tenure funk" or related funks across careers. Can't say that I know how to fix it, although the idea of taking sabbaticals to think and develop projects is definitely appealing. I also have a feeling that realization of divergence between what I'm doing and what I'd do given unlimited funds is actually quite good in the end.

It just hit me that, in my 5th year review meeting, my department head made exactly this point. Tenure is a great time to evaluate the course of your career and what you'd change. Now that I've established a couple of interesting (and fundable so far, fingers crossed) systems, I can begin to tweak these to ask questions that align better with my intrinsic interest in bacterial evolution. When you start your lab everything goes so fast and time management is so difficult that you have to focus on only the most important things.  Now that I'm a few years in, I have a better sense of how to carry out smaller exploratory projects given time constraints. Being a PI is the greatest job in the world (IMO, for me) even though it's stressful as hell and bathes you in bad news most of the time. Truth is that it takes a few years to figure out how to navigate the system where the research you truly want to do may not be fundable. The research is still quite possible, it just takes some time and perspective to see how to get the paths to converge again.

The grass may look greener

Second post as I sit and think about my first five years running a lab...

Inevitably, there is going to be something about your lab/research situation that you're not happy with. If these things are within your control, great! You can hopefully fix them and move on. Other institutional things will be out of your immediate control or above your paygrade. With this second set, you can either look to find jobs elsewhere or find creative ways to make your current situation improve.

One of the most difficult things for me to deal with as a PI at Arizona is that there is no 'real' central Microbiology program. There are numerous smart and talented researchers across the campus who are microbiologists, but it's not as cohesive a unit as other places I've been. Part of this is historical inertia. Part of this is stuff that's over my paygrade. Part of this is that many of us have obligations to other programs on campus and simply can't devote the time that we would like to fostering those relationships. There are only 24 hours in the day, and my loyalty (for lack of a better word) has to be to Plant Sciences first and foremost because that is my home department.

For whatever reason, and I may definitely be the person to blame for this, I have felt a bit lonely on campus researchwise. Everyone else is doing great things, but I've never truly felt that other research interests on campus significantly overlapped with my own interests in evolutionary microbiology. Sure, I can bounce grant/experiment ideas off of people and receive very useful feedback, but I haven't been able to find a community of researchers on campus to discuss topics like "adaptation", "pleiotropy", "horizontal gene transfer" etc...in general ways. I really enjoy lofty discussions about where the field of experimental evolution is going, but I haven't met anyone else on campus to grab beers with and talk shop. If you're out there, please come find me! On top of all this, many of the microbiology folks associated with the EEB department here have up and left in the last few years.

I didn't appreciate it until recently, but our own research careers are hugely shaped by the environment we are in. My last post was about how my research trajectory changed in the first five years of my lab. I can definitely say that those changes were precipitated by what kinds of scientific interactions were available to me on campus. Lately I've been wondering though, how would my own experiments or grants have changed if there were a couple of more labs on campus generally involved in evolutionary micro (or if I knew about them)? Would I have had a bunch of different collaborations than I do right now? My research ideas would no doubt be shaped again if I changed Universities and joined more of an EEB department, do I want that to happen now?

Without going into details, I think that the problem described above is institutional. Without hiring numerous new PIs, which has been a bit difficult at state universities since 2008, there are only two options to remedy my intellectual withdrawl. I could change institutions, which has it's own set of issues, or I could find a way to get the interactions I needed off campus.

A couple of years ago I had to defend my time spent on Twitter to my department head. What I said then, and still do, is that Twitter has been an intellectual lifesaver in addition to any other tangible benefits. I can go there and find papers that I wouldn't have the time to search out otherwise. I can interact with people whose intellectual interests better align with my own, and carry out great (to the extent that any character limited discussion can be) discussions with people about new results or research trajectories. I've tried to get better grant feedback by posting these and asking for comments, which hasn't worked quite like I'd have hoped but I think was still worthwhile. I connected with a couple of folks who were willing to read over other versions of grants and offer really constructive critiques.

Ecology/evolutionary microbiology seminars on campus are few and far between (for instance, EEB has had some micro people in to give seminars). Sometimes I've been able to invite people to give Plant Sciences seminars, but you have to fill a certain niche for me to feel OK doing that. Given this context, microseminar has been another intellectual lifesaver and has filled one of my on campus blindspots. Along these same lines, I've participated in Google hangout journal clubs and am thinking about incorporating those kinds of things into my own lab meetings. It's fun to actually have the person who wrote the paper get in on the discussion, and with the magic of Youtube these discussions are archived for everyone to see. I'm going to try and work both of these activities into my lab meetings next term, because that way the time is already scheduled.

Long story short, there is no perfect situation as far as I can tell, but there may never have been. Some research environments may foster new discoveries (i.e. Bell labs) but there were all sorts of downsides and infighting that happened there too. There are tons of letters (actual letters!) from back in the day between researchers talking about their ideas to each other and which provide a bit of coloring for how experiments are described in textbooks. I don't think I've ever written a letter to another researcher with a pen and paper, however, I've been able to find ways to placate some of my intellectual cravings through social media. FOIA requests aside, future historians are going to have a lot of archived tweets/blog posts/videos to sift through to understand how scientific revolutions happened in the 2000s. I think loneliness happens to everyone in this job at some point or another. At least for me, time spent interacting online has helped to quell these feelings a bit and because of that it's time well spent.

The five year plan redux

Rounding into about my fifth year on the job as a PI, I've started to look back and think about how I made it to this point. This will probably be a series of posts as the ideas jump into my head, but today I've been wondering about how a lab's research focus changes.

Five years ago I sat down and thought about a five year plan for my lab. I was just coming off of a postdoc using comparative genomics to study virulence evolution in the plant pathogen Pseudomonas syringae. Given that I was (and still am) in a Plant Sciences department, I thought that it would be a good idea to continue studying virulence in P. syringae, and I knew that there were some interesting/safe results left to mine from my postdoc data. In parallel, I was thinking that I wanted to get back into studying experimental evolution of microbial populations. I actually chose the lab for my postdoc in order to get experience with Pseudomonas with the hope of eventually setting up such systems. For my "riskier" projects I wanted to use experimental evolution to look at the effects of horizontal gene transfer on adaptation.

For a couple or three years I followed my plan. I've been able to publish a few papers on virulence in P. syringae. I've got my experimental evolution system up and running and have published some of the necessary background work. There remain a bunch of different paths that I can follow for either of those two projects that I am exciting to try and follow up on.

The amazing thing to me though is that I'm not currently funded to do any of that. I've written numerous grants (20?) for these projects across multiple agencies, but they just haven't been successful. A few of these grants came REEEAAAALLLYYYYY close to funding, but just didn't make the cut. Getting any of these grants would have been great, but I think my research program is actually stronger because of those failures. Sure, every rejection email sucks, but I was constantly evaluating and reevaluating research directions. For the plant pathogen work, there was a lot of competition and my grants (even though they were solid I think) just didn't stand out because there were many other labs doing approximately similar things. The experimental evolution work just didn't seem to hit the right chord to the right people, again and again and again.

I've been lucky to get a handful of grants recently, but none of these projects was on my radar five years ago. One of the funded projects started out as a random email question between Betsy Arnold and I in about year 2 of my lab and has blossomed incredibly since then. It's one of those exciting projects where we find a new result every week or so, yet every thing about the system remains pretty black-boxish. Another newly funded project started as an observation by my postdoc Kevin Hockett around year 3 of the lab. He started out playing around with diverse strains of P. syringae, seeing how these strains interacted with one another. We kept pushing the genetics of the system because nothing published could explain the results. Turns out we stumbled into a really cool evolutionary story.

The point of this whole post is that I had a plan, but the plan necessarily changed. Since grad school, I've imagined how my research career would look. Never did I think I'd end up in a Plant Sciences department (there are pluses and minuses, but that's a post for another time). The questions I thought my lab would be focusing on have fallen to the wayside. I'm still quite interested in them and have a variety of undergrads plowing ahead, but they aren't on the forefront anymore. The projects that have been successfully funded came together after I spent a couple of years focused on completely different topics. I'm an N of 1, and I have no idea if my story is shared by other researchers, but there are so many posts about how to be a PI that I figure I'd share this data point. I have no clue what the future truly holds, but I'm just going to keep being curious about the world because it's been good to me so far.

A healthy dose of skepticism and the need for editors

As some of you know, I've been engaged in an interesting dialogue as a reviewer for Frontiers recently. This got me thinking about the role of editors in the process of publication, but also about how my own brain interprets experimental data. I was originally going to write a couple of posts, but I think they work together so now you just get a singular post that's slightly longer.

 Long story short, currently, I disagree with the way that the authors have analyzed their data and am waiting to actually "endorse" the publication at a Frontiers journal. If you snoop around in a couple of months I'm guessing that you'll be able to figure out what I'm talking about because at Frontiers the reviewers names are listed openly on the final PDF. This whole process has led me to rethink the way that Frontiers actually performs review (which takes place in an interactive forum where authors respond directly to comments from reviewers). I love the idea of open, non-anonymous review and am strongly in favor of making public a record of review for each paper. For reasons I'll elaborate on below, I think this system is slightly flawed.

Maybe I've just grown cynical over the years, but the first thing I do when I get awesome new data is to question how I screwed up. Was everything randomized? Did the strains get contaminated? Etc, etc...Ideally all of these questions are answered by experimental controls, but I'm good at thinking of extravagant and elaborate ways in which I'm wrong. Nature is often quite good at this too I've found, although that's the fun of biology (after a period of cursing the sky). Thanks in a large part to this  self-skepticism, I'm always thinking about the next ways to adequately control for experiments which leads me to wait to pull the trigger on submitting publications. My grad school and PD advisors helped to reign in these skeptical tendencies and slowroll of manuscript submissions just a bit by pointing out that nothing is ever perfect. The voices are always still there though.

These same tendencies act when I'm reviewing other papers. Sometimes things are easy to believe just by comparing summary stats to the reported data, but other times I'd like to see the primary data and dig my hands personally into the underlying statistical model/assumptions until I truly believe it. In many cases I have to actually ask to see this primary data, which is not great, but at least with anonymity I don't worry about directly questioning the author's abilities. I mean, inherently if you are asking for primary data because the stats seem wonky then you're implicitly questioning other people's abilities. When my name is not going to be known I don't worry as much about the social ramifications of it all and I sleep better at night.

I am way too over-critical of my own experiments. A little bit of skepticism is healthy, but too much self-skepticism as a scientist paralyzes your career. Even as a reviewer I worry about being over-critical and asking for tedious and minuscule changes that might not ultimately matter. When you are knee deep into reviewing a paper it's easy to lose sight of the bigger picture. This is where the editor comes in. Each time we review a paper, we make a list of critical and less than critical things that need to be "fixed" before publication. Oftentimes the editors will read these list from every reviewer and distill down the absolute requirements. Editors often have their own impression of what makes a publishable unit (that's for another post though, suffice it to say that's why direct track 2 submission to PNAS no longer exists). What I've come to think is that editors are absolutely required in the current publishing process. Reviewers and authors are on about the same level in the dynamic, but the editor inherently has an overriding sense of authority in the whole process. They can take reviewers comments and immediately disregard the ones that aren't critical. They can emphasize to authors exactly everything that needs to be done. The authority is key because both reviewers and authors are deferential to it. As a reviewer I'm not worried about asking too small a question because 1) everything I write in the review is important to me and 2) I know that the good editors will know when I'm being too specific are nit-picky.

With this Frontiers article I've had to respond to the authors that "I'd like to see the primary data". Having received many reviews in my career, I know exactly how this comment will be received. When it comes from a reviewer directly it seems nit-picky and maybe even a bit of a personal affront. If the editor agrees, there is a bit more weight to the comment. It felt weird having to directly comment to the authors that I wanted to see their data. They're a good lab and I worry that their impression of me (since they'll know my name after it's published) will change for the worse. These are things you can't control, but that's how it goes.

For all of you out there who have papers I'll review in the future, know that I'm even harder on myself. I'd like to think self-skepticism is part of what makes me good at my job though.

Metagenomics and "A feeling for the organism"

Evelyn Keller's biography of Barbara McClintock is entitled "A feeling for the organism". A few paragraphs in this last chapter sum up quite nicely how McClintock viewed the scientific enterprise and discoveries:

"Over and over again, she tells us one must have the time to look, the patience to "hear what the material has to say to you," the openness to "let it come to you." Above all, one must have "a feeling for the organism." One must understand "how it grows, understand its parts, understand when something is going wrong with it. [An organism] isn't just a piece of plastic, it's something that is constantly being affected by the environment, constantly showing attributes or disabilities in its growth. You have to be aware of all of that.... You need to know those plants well enough so that if anything changes, ... you [can] look at the plant and right away you know what this damage you see is from-something that scraped across it or something that bit it or something that the wind did." You need to have a feeling for every individual plant. "No two plants are exactly alike. They're all different, and as a consequence, you have to know that difference," she explains. "I start with the seedling, and I don't want to leave it. I don't feel I really know the story ifI don't watch the plant all the way along. So I know every plant in the field. I know them intimately, and I find it a great pleasure to know them." This intimate knowledge, made possible by years of close association with the organism she studies, is a prerequisite for her extraordinary perspicacity. "I have learned so much about the com plant that when I see things, I can interpret [them] right away." Both literally and figuratively, her "feeling for the organism"

I agree wholeheartedly. Others may science differently, but I make my living by looking and studying everything about the bacteria I work with. Going into each experiment I have an idea of what to expect (even if these "experiments" simply involve streaking out cultures from frozen). If you give me a genome of Pseudomonas syringae, I can tell you the main components you'll find . I can tell you how certain strains will grow (or won't), what the colonies will look like, how long they'll take to pop up, what color they'll be. It took me a few years to gather this intuition, but now that it's engrained I like to think I have an innate sense when something is "off". I liken this to a scientific Spidey-sense. The challenging part is truly knowing when to follow up on these odd results, when to store away for the future, and when to disregard them as uninteresting.

I was reminded of McClintock's "feeling for the organism" by a couple of stories from metagenomics that have popped up across my feeds. Before I say anything else, I don't intend to denigrate the quality of the science or data underlying these stories by any means. The work is solid, I just think we're starting to find some limitations in the power of "big science" and these holes usually pop up in the discussion sections of papers and press releases. The first of these stories was a tour de force looking at metagenomics of the NYC subway system. The authors reported a variety of interesting results, but the tag line that a lot of news outlets seemed to focus on were the presence of Yersinia pestis (plague) and Bacillus anthracis (anthrax) within the subway system. The limitations of these methods have been hashed out already (here and here), but I want to focus on the inherent lack of "a feeling for the organism" when dealing with metagenomic data. Studies of any open microbial ecosystems are going to find a diversity of taxa. Unless you bring in specialists, there is simply no way to know the ins and outs of each organism. In the case of the NYC subway metagenome, from my interpretation at least, the authors looked at only bits and pieces of the Yersinia and Bacillus genomes without capturing the whole picture. They had to do this because the story was so inherently large that you couldn't possibly investigate everything in depth. However, specialists with "a feeling" for either Yersinia or Bacillus could have provided a viewpoint on which directions (other genes to look at, levels of nucleotide diversity which seem a bit too high) to follow up on to truly demonstrate presence of these bugs within the subway.

Likewise, one part of the story on urban microbes in this piece caught my eye:

"Rodents are under study, too. White-footed mice (Peromyscus leucopus) in New York City carry more Helicobacter and Atopobium bacteria — associated with stomach ulcers and bacterial vaginosis in humans — than their suburban counterparts"

I worked and slaved over Helicobacter pylori cultures all through grad school. I simultaneously loved and hated that bug. It's finicky growth patterns are the reason I moved over to study the reliably growing Pseudomonas after grad school. Unless something has dramatically changed since I've been in the literature (which is completely possible), rodents are terrible hosts for H. pylori strains that cause stomach ulcers (general overview here). You can get a subset of H. pylori strains to grow in mice, but there are often a variety of genetic changes that take place that allow them to adapt (see here).  I wouldn't be surprised if there were multiple Helicobacter strains within mice in NYC, but my money is on the fact that they aren't Helicobacter pylori that could cause stomach ulcers.

These are the tradeoffs that are made when dealing with immense data sets, and I'm not quite sure how to fix this. No one has a feeling for ALL THE MICROBZ. If you have a fun/interesting story on microbiomes that focuses on a couple of taxa in bold, at least try to run your data and ideas past someone that truly has a feeling for these organisms before publishing the paper. If it holds up after that, more power to you.

Navigating the waters of NSF grant submission

*Disclaimer: What follows is  a post about structural biases I've perceived within the NSF Biology system. I think these biases are intrinsic but keep in mind I could be completely wrong (and if you have different views, please feel free to comment). They also aren't inherently bad or need to be fixed, they just exist based on the pool of reviewers/panelists and timing of the grant cycles. It's a bit rambling, but I'm hoping to provide at least a slightly useful insight or two.

Even before the new office smell has worn off, and in many cases before you've actually moved into your office, the thoughts of many PIs newly merging onto the tenure track are focused on grant writing. This isn't going to be a post about how to get NSF grants, but more along the lines of "things I've experienced writing grants across panels". Grant writing is truly an art. Something that I didn't truly appreciate before is that, as with any piece of art, each target audience has their own subjective opinions. I've had my lab for 4 1/2 year now and have written grants to DEB, MCB, and IOS. I've been fortunate enough to sit on preproposal and full proposal panels. One of the most difficult ongoing lessons I'm learning is that grants written to each of these are very different beasts.

1) Preproposals change the game. DEB and IOS require preproposals, MCB does not. I'll save most of the comments about pre vs. full proposals for other posts, but suffice it to say that writing a convincing preproposal takes a different skill set than writing a convincing full proposal. Since preproposals don't go out for external review, the fate of your grant is entirely influenced by the composition of the panel. In panels that get a lot of submissions focused on similar systems (at least from what I've seen at IOS where there are only a handful of well-worn symbiosis models) novelty can be a benefit. If you propose to work with a new/novel system, and the science makes sense, you can get some bonus points if every other grant is focused on model organisms. Furthermore, while there are certainly benefits to working with a model system, it's more likely that someone on the preproposal panel will know little details about the nuances of the organism and can call you out for poor experimental design. On the other hand, if you are proposing to work in a system that no one on the panel truly has expertise in, you better be able to convince them in four pages that the experiments are feasible. Depending on overlap of the panel's expertise with your own grant, there could be details missed during the preproposal discussion/reviews, and their will likely be subtle misinterpretations. It's just how it goes and feeds into the noise of the system. These things can be ironed out in the full proposal though because those will go out for external review. I get the feeling that DEB grants and review panels have a much higher variance in topic and system than IOS panels. If such a difference truly exists it definitely adds a new psychological layer into the process.

One last thing to mention in regards to the effects of preproposals. There is likely to at least be a little overlap between reviewers of your successful preproposal and your full proposal. I can't speak to anyone else on this, but when discussing full proposals I remembered the discussions surrounding the preproposals. I remembered perceived weaknesses and strengths and I tried to see how the authors dealt with these criticisms. I can't help but think that it's a good idea to dedicate some of your full proposal to laying out a response to your preproposal reviews.

2) Timing can matter for CAREER grants, especially since you have a choice about which panel to submit to. Submission of IOS/DEB full proposals occurs in summer and overlaps with CAREER award deadlines. Panels evaluating full proposals for both of these programs will also evaluate CAREER awards at the same time. Given the vetting of ideas that occurs due to preproposals, differences between CAREER grants and full proposals were often pretty glaring. It's also possible that you could have turned your non-invited preproposal into a CAREER grant, and that it would be reviewed by the same panel for both IOS/DEB.

In contrast, the normal deadlines for MCB panels that I've applied to are now in November. Therefore, if I submit a CAREER award to MCB there is no chance that the grant could be reviewed by the same panel that it would be as a regular submission. This matters because I've had some CAREER grants go to what I perceive as weird places at MCB (like Engineering panels) and they get evaluated very differently than they do at the regular November panels. Differences in criteria between regular and CAREER grants aside, the science may be essentially the same in the grants I've submitted but I get a feeling that there is much more variance in the CAREER reviews simply because the panel isn't quite the fit I imagine it to be. I think this also factors in because I'm not convinced that reviews of CAREER grants inform my writing of regular MCB grants (and vice versa), whereas I think you can get more traction out of reviews regardless of grant type at both IOS and DEB.

3) Funding rates are low regardless, but DEB (evolutionary processes at least, I can't speak to anything ecology) feels like an even steeper climb for microbiologists than for other biologists. The first few times that I had grants rejected from DEB, the POs made statements like "you have to convince frog biologists that your work is important". These comments were spot on and looking back I did a terrible job at describing how my work applied across systems. However, and I could be wrong about this although the few people I've asked back up my intuition, I'm not sure that grants from frog biologists at DEB get the reverse critique of "convincing microbiologists that your work is important". I'm not sure what this means, and certainly some great microbiology work gets funded through DEB, but it feels like there is a slightly implicit bias from the reviewers against microbial evolution work at DEB. There are some generally important evolutionary phenomena in bacteria (like rampant horizontal gene transfer) that simply don't apply across systems. Likewise, there are some generally important evolutionary phenomena in eukaryotes (sex ratio biases, diploidy) that don't really cleanly apply to bacteria. Given the broad makeup of review panels at DEB, I think it's just hard to get some types of microbial work funded through there even though in a world with unlimited funding it's the right place for it. It's possible that the reverse is true at IOS because most model symbiosis systems involve microbes.