Unsolicited advice for new graduate students in the biological sciences

School just started up again here at UC Davis, which always makes me reflective about my first days as a graduate student. I've had a half-formed post in my mind for a while now about things I wished I'd known when I first started, so I thought this would be a good time to get them out there.

My experience is probably most relevant to people in the biological sciences, and especially those in microbiology. The further you are from that, the lower the chances that this might apply to you. But if your're reading a blog titled "Mostly Microbiology," I figure this might be up your alley. So without further ado, here is my list of unsolicited advice for new graduate students

Bad dad joke

1) Finding a lab/advisor.
Your number one priority in your first year. There are several things I think you should care about in finding a lab:

A) Personal Fit. Finding a fit between a advisor/professor and grad student is a funny thing. Different people care about different things. First, ask yourself how self-motivated you are. Be honest, as this helps to guide your selection. Do you want a mentor who will just leave you alone to do your work without interference, or one who leans more toward active mentoring? There isn't a universal rule as to which one is better, and many professors will be more active at the start of your time with them and wean you off their help over time until eventually you are working more independently. Just know that if you join a large lab with a very busy professor, she or he may not have as much time to be involved in your projects. Professors are busy people. You may get left in the hands of senior lab members (postdocs or staff) to answer many of your questions. Often times these are very capable individuals, especially with the current glut of people stuck waiting around for their own tenure track position to open up. Twenty years ago people with their type of experience and talent would have likely had their own labs already. The positive trade off with having less face time with the professor is that larger labs are often a sign of funding success (see point C).

Success baby is living the dream

B) Research fit. Ideally, it is better to find a lab doing things you are interested in. This will likely be your life for ~5+ years. Be open minded with rotations, as many subjects might not be immediately interesting at first glance, but reveal more intriguing aspects upon further study. Do you want to do basic research about the fundamentals of how things work, or more applied research that directly leads to a product or a cure? Do you want to do medical-oriented research or environmental/ecological research? The more options you have to choose from the better chance you have to find one that has funding for you. Speaking of which...


C) Funding.

At least not on the timeline one wants

Few things are worse than running out of funding halfway through your degree. Many principle investigators (PIs, so called due to their status as the person listed as getting the grant money) don't know if they have funding until right when you need to decide on a lab. Lab funding is a touchy subject, and often PIs have to adapt on the fly to changing circumstances. Funding yourself by landing a teaching assitantship (pays your stipend and tuition so it doesn't come out of their grant money) helps. It's a balancing act however, because the more you teach, the less time you have for research. As an aside, I think it's a very bad idea to have any form of outside employment while in grad school.


2) Classes

NOBODY

This is more controversial. In my opinion, your goals with graduate level courses should be to a) learn all the stuff relevant to your goals and b) pass the class. Grades are secondary. I've not seen very many places that care about your graduate GPA if you have a degree and publications. I was even told that it was tacky to include my GPA on my CV. Some graduate fellowships ask you for it, but from what I've heard, as long as you aren't failing, they mostly care more about your research proposal and not about your GPA. It's not like a 3.0 vs a 3.5 is going to make or break an otherwise outstanding fellowship application. Don't sacrifice research time and sleep to break your back for that A+, when you can achieve a B for half the effort. This is a very different approach from your undergraduate days, and lots of high-achieving people struggle with it. But the cost/benefit ratio (in my opinion) lies firmly on the side of more research and a lower GPA.

3) Research

True story bro (if somewhat hyperbolic)

Don't expect research success right off. Nearly everyone screws up a lot at the beginning. Here's a few general principles that might help you to not waste too much time and effort:

• Plan out your whole experiment, timeline and all, before you get out a single tube or reagent. Make sure you have all the necessary supplies. Few things are worse than realizing halfway through that you don't have time to finish that day, or are out of some critical reagent, and having a bunch of effort go for naught.
• Think about what controls to include. Doing the experiment the right way the first time sure helps.
• Try to design experiments that will tell you something useful no matter how the results turn out. Hoping for one specific result will inevitably doom you to being cursed by the Hacker Gods who programmed this reality (and like to mess with us from time to time) to uninterpretable results. Ask yourself what each possible result of an experiment would mean, and if the answer is "I don't know" or "Nothing," tweak your study design if possible.
• Design a physical workflow that helps you keep track of what you have and haven't done to each sample/tube. I like to move each tube from one rack to another (or from one row to another) after adding a reagent so that I know that I have added it to that tube. That way, if I get interrupted, I can go back an know where I was at with each tube.
• Learn some basic programming skills. I am still working on this, but it has been incredibly useful, and is a huge time saver in the long run. I like python.

The general idea with your research is that you should eventually know more about your narrow project than your mentor does. She or he will of course have a much larger total amount of and a wider breadth of knowledge, but you should know the particulars of what you are doing in and out, up and down, inside and out. Read ALL the relevant literature.

Ok, so that's pretty much impossible. Unless you define relevant tightly.

Figure out where you think the field you are in has things wrong, or where there are important holes in understanding. Once you have proven yourself with some initial success, start trying to branch out from the initial path your advisor put you on (however specific or general that path was). Start making suggestions of your own about how to approach the important questions. Isn't that kind of the point of grad school, to develop the ability to do independent research? You want to become a colleague of your advisor's, not just an employee. Of course, don't spend supply money or too much time on things that you haven't gotten approved by your advisor. They are paying you, after all. Just be an active participant in your research.

This is a process that takes time, of course. Meanwhile, read about, and attempt to banish any impostor syndrome that might set in. I like these two articles to understand the phenomena. It happens to lots of us, myself included.

I think just about all grad students feel this way sometimes.

Lastly, it is never too early to think about what you want to do next. Your advisor is probably most familiar with the academic route, since it is the one they took, but there are lots of other careers out there. Science communication, business, law, non-profits, government agencies, and public policy all have niches where you might find a home. I know lots of people who started grad school because they had a "get ALL the degrees" approach to life, and that's fine, but there are things you can do now to dip a toe in different ponds and see how you like it. Take advantage of opportunities to try on different shoes when they come your way in department emails, workshops, or volunteer opportunities.

Good luck this year!

New Paper: Stool Microbiota and Vaccine Responses of Infants

As part of getting my blogging groove back on, I thought I would talk about my part in this new paper. Together, Nazmul (the first author) and I analyzed the gut microbiota of a large cohort of infants in Bangladesh, paying extra attention to the bifidobacteria. Nazmul (and others) did a ton of work looking at the response to various vaccines in these infants. We found that the response to some vaccines was better in infants that had higher levels of bifidobacteria.

A couple of things of note about my contribution to this paper:

1) The correlations between vaccine response and the microbiota sometimes differed between species and subspecies of bifidobacteria, which means that A) not all bifidobacteria are equal, and B) that the effects can be correlated to a manageable set of genes. We were able to trace the effects down to subspecies level within the B. longum group thanks to a new method that I helped develop, which should be getting it's own paper soon.

2) The levels of bifidobacteria in these infants were really high compared to some other cohorts I have seen or studied myself. This was despite the high rate of c-section birth noted in the paper. I find this super interesting.

3) Parents, please vaccinate your kids so that stuff like this and this doesn't happen. Herd immunity is a real thing. When you choose not to vaccinate your child, you put other kids who can't get vaccinated for whatever reason (immunocompromised, etc.) or for whom vaccines don't work at risk. No matter what type of bacteria live in your child's gut, vaccination is almost assuredly better than no vaccination.

Anyway, I was glad to use my Bif-TRFLP technique on a new set of infants and be involved in this cool study. Grad school has given me the chance to collaborate on projects with a lot of great people on interesting things. I'm glad Charles Stephensen got interested in gut microbes and started this collaboration with my advisor. I'm increasingly convinced that collaborations are the best way to do impactful research. It's so hard to be an expert in all the relevent aspects of today's unsolved problems.

New blogging style

Life has become very busy lately and I have sadly neglected my once-thriving blog. I've had a lot of writing to do lately, and haven't felt much like adding more to my plate. To remedy this situation, I have decided that other than the occasional long-form post about new papers, I am going to try to write shorter quick-hitting posts from now on. We'll see if that helps lower the activation energy and gets me posting more. I do enjoy expressing myself in my own forum, but writing long posts became too daunting.

New Paper: A Comparison of Two Probiotic Strains of Bifidobacteria in Premature Infants

I wanted to write a little about my small part in this paper. My work in this collaboration is one of the times when I have most felt that I was making a real-world impact. Mark Underwood, the lead author on this paper, is a neonatologist and assistant professor at the UC Davis Medical Center, where he practices medicine in the neonatal intensive care unit (NICU) and cares for premature infants ("premies"). He had a use for the tool that I developed in my Bif-TRFLP paper, so I got to participate in this cool study.

A premie and its tiny little feet
from https://www.ucdmc.ucdavis.edu/children/clinical_services/NICU/

One of the major issues that premies face early in life is necrotizing enterocolitis (NEC), which is thought to arise in part due to imbalance in the gut bacterial community. Premies have underdeveloped intestinal tracts that are extra prone to infections. Probiotics have often been used to try to prevent this life-threatening disease, at least some of the time successfully. The idea is that the good bacteria can help the immature gut resist colonization by pathogens by taking up all the space, eating all the "bacteria food," and producing antimicrobial metabolites. There has been a lot of debate about which strains of probiotics to use, and how best to use them. This paper tested a few different supplementation mixes and how they affected the microbial community in the gut of premies. It showed some interesting results about improving the diversity of the community, improving levels of beneficial bifidobacteria, and showed species-level differences in the efficacy of the intervention. Here is a link to the paper.

My part in this paper was to use my Bif-TRFLP technique to figure out which species of bifidobacteria were found in the guts of some of Mark's NICU infants: before, during, and after a variety of treatments. I discovered that B. infantis, a strain that is known to eat the oligosaccharides (sugars) found in breast milk, colonizes infants better than the B. lactis strain we tested. When given breast milk the B. infantis strain was the dominant bifidobacteria in the infants, even if they were given B. lactis and not B. infantis. 

This begs the question of how the babies that were given B. lactis ended up colonized by B. infantis instead. They take lots of precautions in the NICU to try to avoid spreading bacteria around the environment, and try to keep these fragile infants from being exposed to harmful bugs like those that cause NEC. (You can take a look at the set-up they have in this really cool virtual tour of the NICU.) This work seems to show that the B. infantis floating around the NICU (maybe from the infants inoculated with it, stray inoculum itself, or just normal environmental strains, who knows!) can outcompete the non-human-milk-adapted B. lactis strain as long as they are given breast milk. The environmental B. infantis probably gets into the infant in much lower numbers than the comparatively massive B. lactis supplementation given to the study babies, so this is a remarkable finding.

This just goes to show the importance of strain specificity to an environment. Not all probiotics (or bacteria in general) are created equal, and they are not always well adapted to a given set of conditions. I really like being a microbial ecologist because it gives me the tools to answer questions like these. Evolution drives microbial community structures, and thinking about how that affects real world problems is a really rewarding exercise to me.

UPDATE- This work was profiled in the press. See here

New paper: Establishment of a Milk-Oriented Microbiota (MOM) in Early Life: How Babies Meet Their MOMs

A new paper just came out on which I am an author:

Establishment of a Milk-Oriented Microbiota (MOM) in Early Life: How Babies Meet Their MOMs. (Functional Food Reviews, Vol 5, No 1 (March), 2013: pp 3–12). Authors: Angela M. Zivkovic, PhD, Zachery T. Lewis, BS, J. Bruce German, PhD, David A. Mills, PhD

Link to the paper here if you have an institutional or other subscription.

I did not come up with the term "MOM" (Dave Mills did) but I thought it was brilliant, and I am glad to be part of the paper that coined the term. Its a good way to emphasize the importance of bacteria in helping a baby grow and thrive.

Little Johnny just found out he has TWO moms! (A Mom and a MOM.)
From http://www.flickr.com/photos/56323141@N00/2658949664

This paper is a review paper that talks about some of the latest research on how breast milk influences the microbes that live in babies. There are some components that help good bacteria grow (like the funky short sugar chains that good bacteria can eat) and some components that stop bad bacteria from growing (proteins that act like antibiotics, antibodies, and decoy molecules that fool pathogens into thinking they have attached to a cell and stop them from infecting us).

In this paper we talk about all the benefits that come from having a good mix of bacteria or "microbiota" in a baby. Associations have been found with the microbiota that include resistance to infection, reduced allergies, and reductions in other inflammatory conditions. There are even initial hints that autism might have a microbial component. This all suggests that having microbiota that doesn't cause inflammatory reactions early on in life might be important to educate our bodies about what is friend and what is foe.

The theory is that if the body has not-so-friendly bacteria in it early on, it seems to be hypersensitive to common allergens later in life. Bifidobacteria are very commonly found in breast-fed infants and seem to meet this non-inflammatory criteria. There is some evidence to suggest that they help calm the immune system down and keep the wrong things from getting to places where they might start triggering immune responses.

The "hygiene hypothesis" states that a lack of exposure to things that our distant ancestors commonly encountered ("dirty" things like feces and parasites and ... dirt) leads to our immune system overreacting to things that aren't really bad. As if it just gets bored and wants something to do, so it picks a fight with pollen or your cat's dander.

Felis catus, a common source of allergens. Sorry, I couldn't find an actual photo of one anywhere on the internet. Strange. I mean, I knew that very few people upload baby pictures, but it seems cats are even less common...

Some people think that if you have enough good bacteria around to keep the immune system busy monitoring harmless things it won't learn to react to the wrong things. With the recent explosion in pre-term and c-section births (both not nearly as survivable before modern medicine, and which are known to cause the baby to not get as healthy of a microbiota as easily) and the decline in breastfeeding, it seems possible that this could lead to at least some of the difference we observe in how many people in the "first-world" get allergies compared to how few people get them in the "third-world." Historically, mothers breast-fed their infants for a lot longer. They would continue to breast feed while weaning, a process which lasted a lot longer than it does now. The presence of the ingredients in breast milk and the bacteria they enrich might have helped keep the immune system from reacting to things like gluten and other common food allergens as they were introduced to the child.

C-section and pre-term births often cause altered microbiota profiles in the infant, which may have to do with problems in getting good microbiota from the mother. Shortly before this paper was published, but after it was submitted, Seth Bordenstein et al. came out with a really nice paper about how mothers pass on microbes to their infants across many species, not just humans (link here). You can go there for more details. I would have cited this paper it if it had been out.

One other point we made, which I though was important, was that diversity in an ecological community is not necessarily always a good thing. The common assumption I have seen many papers make is that a more diverse community is more stable and resistant to disturbances, and is always better. It may be that early on a more controlled community that is less diverse but maintained by specific inputs from the mother might be the better way to go for the overall health of the infant. A new Nature paper just came out on this subject, here.

Anyway, I thought we wrote a a nice overview paper if you are interested in learning about the benefits of babies being seeded with "good" microbes.

Conference Report: NIH "Human Microbiome Science: Vision for the Future" conference, Bethesda MD

I got back from this conference a couple a little while ago. I had the opportunity to present a poster of some of my work, and a chance to hear from some of the biggest names in the field of microbiome research. You should be able to watch individual talks as soon as they put them up here.

The idea behind this conference was to get feedback from scientists in the area about what the current state of our knowledge is, and to hear about what challenges and difficulties lie in the way of further progress. It was an impressive effort from the NIH people to figure out why they should be funding microbiome research and where the funding would do the most good. With the first phase of the Human Microbiome Project drawing to a close, it was a good chance to stop and organize and plan out what comes next.

I learned a lot about how NIH funding works, what the sub-institutes are, and what they fund. I got to meet a program officer and hear a talk from NIH Director Francis Collins, he of the Human Genome Project fame. I also got to hear talks from such luminaries as Rob Knight, Curtis Huttenhower (Nice guy!), Peter Turnbaugh, Ruth Ley, and Ed Yong (one of the best Twitter follows I ever made). My favorite talk was probably by Maria Gloria Dominguez Bello, who talked about the microbiomes of uncontacted indigenous tribes in Venezuela.

This was also my first time in Washington D.C.. I took the metro downtown one night to have dinner with my PhD advisor and his old PhD advisor (my grand-advisor?), Gary Dunny. Meeting him was a cool opportunity. I liked the atmosphere of the part of D.C. we went to.

I really think there is a bright future ahead in microbiome research, with discoveries just being made about the interactions of the microbiome with hormone regulation, autism development, new probiotics, immune system regulation and autoimmune disease, fecal transplants (So relatively unstudied! So exciting!), and obesity.

And who's to say scientists don't have a sense of humor! My advisor got a chuckle using this sign to talk about correlation vs. causation.

What order these go in makes for very different stories!

Another speaker used this video to talk about the seeking NIH funding in the current funding climate and overcoming challenges along the way. (The cheese is the funding, the grant applicant is the mouse)



I'll be speaking at two conferences between now and October, so this is not the end of conference season for me. It's nice to be able to do conferences at this stage of my PhD and talk about my data.

Book Review: The Signal and the Noise : Why So Many Predictions Fail – but Some Don't. By Nate Silver

How Politics, Sports, and Microbial Ecology are very much alike:

I thought this might be a topical post in light of Nate Silver's announcement that he will be moving his operation to ESPN. Nate is one of my favorite people, as his interests  (Sports, Politics, Big Data) match my own in many ways.

In the field of microbial ecology we are increasingly dealing with mounds upon mounds of data. This is due to the advent of DNA sequencing technologies that can count millions of pieces of DNA and tries to match them to databases that tell us which microbe the DNA came from. Sure, there is signal in these mounds, but there can also be lots of noise. When you make so many observations, there are bound to be some that happen by chance. Even if you are 95% certain that your observations aren't coincidence, it only takes 20 observations before you would expect one to be spurious (19/20= 95%)

 (Relevent XKCD) 

When I started analyzing my own sequencing data, I realized I needed a much better understanding of statistics to be able to grok what my results meant. I had very little formal statistical training, which is a sad reflection on my high school (where all the smart kids should take calculus, I was assured) and undergrad (required a "calculus for business majors" class, but no stats) programs. Ask me when was the last time I formally took a derivative or integral (my undergrad calculus class-- 10 years ago). Ask me when was the last time I used any statistics (yesterday). I think there is a fundamental disconnect between which math skills are actually needed by the majority of people, and which are taught in schools.

Because I didn't have a good foundation on things like Bayes' Theorem, I started looking around for a book that would teach me some fundamentals so I could develop a good feeling for what type of statistical tests would be most appropriate for my data. I didn't want to read a dry textbook. I heard about this book on an interview that Nate did on some TV show and thought it might be an interesting way to learn some statistics. I knew about Nate from his work in predicting elections (the 2012 US elections in particular) and some of his work in sports as well. 

This book talks about the advances in predictions in fields ranging from earthquakes and weather to sports, gambling, and politics. Many of these fields have large data sets to draw from, just like microbial ecology. If you think about it, we have been keeping records in baseball for a very long time. If you wanted to ask how left-handed pitchers do against left-handed batters in the 9th inning of tied games, there is probably a decent sample size to look at. 

As a long time fantasy football and basketball player (one of my hobbies) I have played around with sports statistics for a while to try to make better decisions about who to draft when and what trades to make. (Gotta fill that all-important virtual trophy case!) There is a similar problem in fantasy sports, lots of data, lots of noise. Some people swear that 3rd-year wide-receivers are the most likely to break out, since it take players that long to learn an NFL offense. People said the similar things about quarterbacks for a long time, but then Cam Newton, Andrew Luck, and Robert Griffin III came along and blew away the avoid-rookie-quarterbacks meme. When making sit-start decisions in fantasy basketball, "experts" say that all else being equal, you should always start the player who is playing in a game where the teams are worst at defense, since you get more possessions per game to pile up stats. In actual sports games (not fantasy) there is some debate on whether things like "momentum" are real (is a team/player on a winning streak or a hot scoring streak within a game more likely to perform better than they otherwise would?). I assume one of the reasons ESPN wanted Nate was to help viewers/readers figure which of these "mechanisms" is real and which is noise. The data is there, it just takes a trained person to analyze it. 

Politics also has large datasets going back many years. With this data people try to answer questions such as: Are local elections predictive of national trends? When is the state of the economy a predictor of presidential elections? Will a candidate's race play into the outcome of an election? It takes careful analysis to separate signal from noise. (See the Redskins Rule -- when the Washington Redskins of the NFL win their last home football game prior to the U.S. Presidential Election the incumbent party wins the electoral vote for the White House; when the Redskins lose, the non-incumbent party wins). 

Microbial ecology is similar in that we can get large datasets around which to make hypotheses about the way communities work. We can try to see if they are real by breaking down the numbers and testing our theories about how mechanisms work. Instead of altered run/pass ratios in games with inclement weather, we look at altered bacteroides/firmicutes ratios (different bacterial groups) in obese people. Some correlations end up being real (the proposed mechanism actually influences the outcome) and some end up being the microbial version of the Redskins Rule (no plausible way for the outcome of a football game to affect the outcome of the election). The real mental work comes in proposing likely mechanisms for the correlations you observe and designing further tests to see if those mechanisms hold true. This takes "subject-matter expertise." Instead of proposing that 3rd-year wide receives break out due to learning an offence, we propose that the physiological effects of pH cause shifts in soil communities. 

Anyway, I really enjoyed this book. It keeps a light tone, and was a pretty easy read, even for the statistically uninitiated like me. I recommend it for anyone who may want to work with "big data." I give it 5/5 Petri dishes!