When I put my pencil down after muddling through the last particularly hairy integration-by-substitution puzzler on the 2013 AB Calculus AP exam, I felt relieved – both that I had survived the exam, and, more fundamentally, that I’d never have to take a calculus class again. Seven years later, picking up a different pencil to take notes on Lecture 1 of MIT’s Principles of Molecular Bioengineering, I felt a familiar pit in my stomach. I was afraid of math, had never liked it, and had successfully avoided it since that last high school exam. My career and I had evolved – I knew that if I wanted to follow my passions and delve into the bioengineering domains I was growing to love, I would have to write an equation or two – and yet, when the whiteboard started filling with a thicket of differential equations, I realized my dread had been well-founded.
How, you might wonder, does a twenty-four-year-old with degrees in biochemistry and synthetic biology end up accepted to one of the world’s premier biological engineering programs with zero math courses on his transcript? When I graduated from a US high school in 2013, I packed it all up and ventured across the pond to start a new life at Imperial College London. I ended up staying four years for the bachelor’s (Biochemistry) and master’s (Systems and Synthetic Biology): endless banter between the 9am lecture hall and the 9pm pub, and only one quick math module in the first year (helpfully titled “Mathematics for Biologists,” optimistically starting with addition and subtraction, and unhelpfully wrapping up with the labelling of axes on a chart). What little math there was was cleverly integrated within the material at hand. Sure, we delved into enzyme kinetics until my hand cramped from sketching Lineweaver-Burke plots by hand; sure, we “wrote” a few Ordinary Differential Equations (ODEs) to model reaction systems, albeit minutes after being told what an ODE might be; and sure, an exam or two featured some real biophysics brainbreakers. The trick was to dodge and weave around the math by avoiding those questions on a “pick three of six questions” exam, Googling statistical tests the night before a lab report was due, and hacking away at supplied code until it spat out an appealing plot. Avoidance was easier than the harder work of confronting my fear of math – until I started to realize the projects and niches I wanted to explore were more at home in engineering departments, and what’s more, that I wanted to apply to engineering PhD programs.
Although I arrived at Synthetic Biology via Biochemistry, it is considered more of an engineering discipline in academia. I stayed at Imperial to do a masters in Systems and Synthetic Biology, where I had my first exposure to ODEs and MATLAB systems modeling; those modules were just brief enough to show me how much I didn’t know before I launched into a research project in a bioengineering lab. While that project allowed me to take a more classic wet lab genetic engineering approach to biomaterials, I felt limited by my poor math background when faced with my peers’ dissertations and posters, which brimmed with differential equations and sleek biochemical models. I started to realize I was at a crossroads: I could either continue avoiding math, and miss out on the complex models and analysis I was growing to love, or bite the bullet and apply to engineering PhD programs. I applied to six programs in the US, where I felt the extra years of preparatory foundational coursework would be my best (and, at this already niche point of my career, perhaps only) chance to start fresh and move into a different field.
Grad school applications are grueling to write for even the most well-suited applicant; when switching into a new field in a new country, there were little landmines scattered throughout the process that almost led me to give up several times. MIT asked me to tabulate every math class I took separately from my transcript, which resulted in an embarrassingly meager few entries for yours truly. I had the privilege and optimism to just blunder through these red flags, knowing the eventual projects in the synthetic biology labs would be a good fit no matter the alien novelty of the core engineering curricula. It’s almost impossible to apply to MIT without Imposter Syndrome rearing its nasty little head, so when I got the “all clear, you’re in” email, I felt only intense, euphoric relief.
It took just a few months for the apprehension to start creeping back in as I started looking through the core class syllabi and coursework. When I sat down in 20.420: Principles of Molecular Bioengineering, surrounded by a cohort of Biological and Chemical Engineers trained in the country’s finest technical programs, I knew it would be an uphill struggle. I knew it was time to face my fears head-on or fail out of grad school trying, and I knew my last-minute summer panic skimming of calculus coursework would have been a drop in the ocean separating me from my peers’ backgrounds.
What I hadn’t expected was how much they were willing to drop everything to help me. Beyond the incredible bonding of a cohort spending sleepless nights in our basement graduate office (aptly named The Dungeon), I was surprised by how people went out of their way to help me specifically – checking up on me, making sure I was keeping up with the code, and teaching me the hard way instead of just giving me their scripts. The semester turned into a touching season of “No Jelle Left Behind”: ODE demos on our Dungeon whiteboard, friends teaching me about (and suffering my) rubberducking at Darwin’s, and a rotating panel of incredibly patient debuggers ironing out my scripts. I was also happily surprised to discover my background wasn’t useless, just different; I could help my friends in turn when the focus shifted to complex biochemistry.
Don’t get me wrong, I still struggled! I pulled many an all-nighter unpacking dense ODEs and debugging stubbornly tangled code, and some problems came and went without a breakthrough. I finally realized that they were difficult by design, for everyone taking that class; talking to my peers who were also struggling with the problem sets showed me that I wasn’t dumb or lacking fundamental background skills, just struggling in the intended way on things designed to push and challenge me.
When I look back now, I can find a certain pride in the journey I took, and I wouldn’t change anything. Yes, it was a struggle to get up to par on the math, and no, I might never be as confident or eager to dive into complicated mathematical models or computational work. But that’s fine! I learned to take challenges head-on, to be honest about my weaknesses, to put in the hard work to fix them instead of avoiding them, and to find confidence in my specific strengths. I followed my interests throughout my academic career, and they led me to the perfect little niche project: grounded in biochemistry and protein design, but elevated by engineering principles, and yes, some math!