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What I wish I knew at the start of MD/PhD

People who know me know that I like to give advice, often unsolicited. I am clearly not too self-conscious about that – everybody holds on to an inner pearl of conviction that they have something to express that others would value. I’m just worse at hiding it, or whatever. So, on this momentous occasion of my second post to our blog, it’s only too tempting to dole out some hard-earned pointers.


Given my healthy self-image, I have no doubt that the secrets uncovered herein will definitely, absolutely improve your experience as a bright eyed and bushy tailed pre-med, new MD/PhDer, or PGY12 (we have studied… closely… our target audience). You will not find this anywhere else. So here we go: what I, with the benefit of hindsight and the rose tinted glasses of a new beginning, wish I had known starting out.


1) It’s the people, stupid. You’ve heard this a million times: Surround yourself with good mentors. Pick the person not the project (ideally both). Don’t ever think you are the smartest person in the room. Your peers are your greatest resource, etc. That’s all true. But there’s a catch: how do you make these judgment calls about complex people and environments quickly, and more importantly, accurately? On the one hand, you are hopefully aware of your own biases and prejudices, and of those of the people, be it friends or advisors, who are advising you. On the other, in the flurry of meetings and of a sea of new faces, first impressions and superficialities (e.g. polished lab websites and prestigious publications) provide lazy shortcuts to quick decisions. Looking back, it is easy to rationalize decisions (you will learn soon enough about post-hoc hypothesizing…). In the thick of it, however, how do you extrapolate a polite 15-minute conversation with a potential PI (lab head) to a potentially 4+ year-experience with them as the arbiter of your academic future? How do you go from a coffee chat with a happy grad student (with a mysterious, deep emptiness to their stare) to peacefully sharing cramped lab space and reagents?


I, for one, am not (or have realized over time that I am not) naturally good at this. By implying certain “instincts” are needed, I am indeed proclaiming and decrying the fact that this is not taught. And, in not being taught, this propagates all sorts of privileges and inequalities, but that’s a topic for deserving of another post, altogether. So, what to do? One option is to dawdle and panic until the deadlines hit. Another is to collect as many of these conversations as possible and realize there’s little consistency. At the risk of sounding cliché, I am sorry to say that there’s no perfect solution to making that initial decision! That’s it. You are dealing with people in a complex environment. People are inconsistent, and complex systems are unpredictable. Still, a finite amount of data, even if variable and inconsistent, is infinitely better than no data, and you can use it to make an imperfect but done decision. The goal is to hopefully avoid slow-motion train wrecks. Indeed, if you are starting a PhD or similar, you will soon find yourself on a speeding train, and some days, you may not be sure if the driver is awake or if it’s even on the tracks anymore. The plan: stay on, stay calm, and do your thing as long as you think it’s going somewhere and there’s a plan to stop; jump off if it’s about to crash (to “fail quickly” is some of the best advice I have received). Hopefully the driver remembers to steer every once in a while, and the passengers aren’t all asleep or serial killers (again, based on your personal, subjective judgment). Sometimes you’ll catch a glimpse of the fellow at the station who hitched a ride on a bullet train and whizzed past you, or another forging ahead in a helicopter, only to hit the mountainside above a tunnel and crash. You might even be tempted at some point to make your way towards the locomotive and toot-toot, chugga-chugga your way towards your goals with ruthless efficiency, not realizing that the tracks had your train locked into the glorious destination from the start. Is anything outside real anyway? This has clearly gone too far, and is either completely nonsensical or a glorious metaphor for life. But hopefully the metaphor will inject some sense of Bokononistic confidence in you if you’re lost, or a healthy dose of impostor syndrome if you’re too non-lost. At least, you have my permission to enjoy the ride (eye roll, I know).


2) Don’t reinvent the wheel, but do invent something. Here’s how some graduate experiences go for the first few weeks (maybe months, years, maybe specific to bio wet lab, maybe mine…): You finally get to do the cell culture/surgery/western blot/PCR/analysis that you learned in bio 101 or undergrad lab for a reason! You have your own project now (you’ve probably been assigned or dreamed up enough aims for 3 R01s and 14 years of work). You are going to revolutionize the field and get a couple high impact papers in the process, and hopefully you’ll be worthy of a doctorate, but who knows. You spent a week planning the first experiment, cited 10 papers in your written protocol and asked your advisor 20 questions about every step, considered emailing a famous PI about best pipetting practices, and only have 394 groups for this first trial run. Once you get this done on Monday, you have 6 follow up experiments for the rest of the week, and next Monday you’ll have Figure 1 of your first paper ready by lab meeting at 9 am.


Well, guess what, the cells never grew, there was a protocol for this in the lab anyway, and the famous PI did not answer your question about whether a P20 or P100 is better for pipetting 20 microliters. Your exact experiment was in fact done for an 1873 paper that’s somehow still behind a paywall. All of this to say: please don’t reinvent the wheel. Sure, you were admitted because of your drive and creativity (or did they filter by GPA, GRE, and APGARs), but those play a more prominent role after you master (or at least are comfortable with) basic techniques and unspoken standards of your field.


Before Picasso began doing the squares and circles, he actually painted like a normal person. So, my dear future Picasso, it’s like riding a bicycle. I guess sometimes a cycling Picasso who is also juggling (in reference to our interview with Dr. Sean Stowell). Zooming out: what applies to techniques and protocols applies to the project as a whole. Yes, you have to have novelty in your project. Yes, do tackle an important problem addressing a fundamental question in a novel way because you’re a disruptor and the rules don’t apply to your paradigm shifting work. But at the same time, you’re in a specific lab—or environment, or work group—to use that group’s resources and talents. Perhaps start by repeating a protocol, and then modify or combine it with new wisdom (that you might very well get from a conversation with a member of a neighboring lab, or someone at a conference, or a paper from an adjacent field) to tackle your important question. Now this might be misconstrued as an excuse to be lazy, intellectually or physically. Some advisors want you to be self-reliant from the get-go, for better or for worse, even as they rely on you to do the hands-on work. To be clear, I am not recommending going after the low hanging fruit, punting your work on labmates/collaborators, or doing as few experiments as possible. For more insight, continue to the next stream of consciousness bullet point. I know, oxymoronic. Shush and read on, trainee.


3) Pick your basket, pick your eggs. As a graduate student, you have to have a starting point, not only in terms of support and know-how in your lab/group, but also intellectually. This could be an interesting result, an inconsistency in the field, or a known unknown that drives and motivates your specific question. The “resources” and “motivation” could be existing unique expertise and techniques in your chosen mentor’s lab, a mystery around the mechanism of a drug or pathway. Ideally, here would be clinical impact to the drug and/or pathway, but that cannot always be predicted! Sometimes there might even be the *appearance* of an explanation in the literature, but as one of our interviewees recalled her mentor saying: the literature is often wrong. All of these points conspire to make PhD projects good launching points for future work and successful career.


As graduate students (and academics in general), a common refrain we hear is that we have much flexibility in choosing what to work on. The best mentors (see: most of our Behind the Microscope podcast interviews) allow their mentees significant freedoms in their studies, sometimes at significant risk to themselves! But there are additional costs. At our level (as students), many struggle with constantly existing in a meta-space of meta-questions: what question should I ask, rather than purely: what is the answer to a given question? This is very different from “traditional” schooling (although, ironically, the liberal arts approach, often considered the diagonal opposite of STEM, technical or professional education, proclaims to teach students how to figure out what is worth learning and to exist in this meta space throughout their lives). This can be exhausting, both mentally and physically, when you end up doing 10 times the number of experiments than what goes into your paper because 90% only served as fodder for finding and then tweaking your question. At a PI level, I imagine this is what translates into seemingly endless grant writing. This is normal, and, in fact, is precisely how Picassos cycle, even after they’ve Picasso’ed. But, only as long as the choice of basket (your environment, mentors, colleagues, field) and eggs (projects, experiments, techniques, models) are good and improving, and, at the risk of reiterating the above, compatible.


4) Conclusion: Reflect, optimize, iterate. I thought I was writing you this “advice” because I am over the hump and all better and wise, but indeed I was all the while reflecting. It is very cathartic and highly recommended. At this point in my journey (having re-entered medical school as an M3 after completing my PhD), I notice that I’ve reflected more on my PhD experience than my first half of the MD. Reflecting on the reflection, I am tempted to say that that is because of the infinitely larger number of decisions, both small and large, are required during the PhD than the first two years of medical school. Reflection and optimization make more sense in the context of decisions made by me, rather than things laid out and decided for me. I think more decisions will be required in M3-4, and I think in this upcoming iteration I will do better. Sound the whistle!


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Michael Sayegh, PhD



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