A Founder’s Story: Chris Li, CEO of BioBox Analytics (1/3)
I’ve always loved computers and software.
I remember booting one up in my aunt’s office back in 95' and loading an 18Mb video game from a floppy disk. But I never thought I’d be building software one day as a career. My goal was medicine or life-science research. Fast forward to my 20’s and I’ve just received an opportunity to work as a research student in a brand new research institute.
Dream do come true.
Biology, meet Bioinformatics
First week on the job, my boss says to me, “We’re a small lab, and bioinformatics is too expensive right now for us. Your project this summer is to do bioinformatics.” I had no idea what that meant, but I stood there, nodded and accepted the challenge enthusiastically.
After googling, “What is bioinformatics”, I sat back into my uncomfortable lab chair, and thought to myself, “What did I just get myself into?”.
I thought life-science research was about tissue-culture, mouse-models, complex biochemical manipulations, yet here I am trying to figure how to use a Makefile to compile this program?
“do Bioinformatics”
Looking back now, I still chuckle at the notion of “do bioinformatics”. Bioinformatics is the happy love-child of computing, software engineering, advanced probability and statistics, and biology. It’s a discipline borne out of necessity through a tectonic change in life-sciences research — next generation sequencing (NGS).
Find any major life-sciences high impact research paper in the last decade. You’d be hard-pressed to see one that didn’t use some form of bioinformatics. It’s now a foundational aspect of life-sciences research. Through NGS tech and advancements in computation biology, we’re now able to decipher information about biology that lead to the discovery of new genes, disease-causing mutations, and fundamental biological processes at an unprecedented rate.
But there is a cost to this knowledge, as an unintended side-effect emerged. These advancements made software literacy and programming chops one of the most sought after skills in biological research.
Fortunately, in my story, the summer of doing bioinformatics ignited in me a deep curiosity, passion, and borderline obsession with the cross-section of software, stats, ML, and biology. A few years later, by the time I entered grad-school, I was fully self-sufficient in bioinformatics and was able to leverage those skills to blast through the first two years and generate enough data for a reclassification. But throughout my grad school experience I saw first-hand what happens if you were less fortunate and didn’t have the time to train in bioinformatics.
My email inbox and backlog of work was consistently full from collaborator and colleagues requests for bioinformatic support. I can’t count how many late nights I’ve had with colleagues crunching through numbers with them, whipping up figures, and translating their biological research question into a computational pipeline. Don’t get me wrong, I did this gladly and would do it whenever asked, because this is what science is about. Scientists help each other in the pursuit of knowledge, not trading tit-for-tat favours. But I could see their frustration from losing the autonomy of conducting/performing this work by themselves. The frustration from a request to collaborators or bioinformatic services being unanswered for weeks, only to have the results/figures come back different from their expectations, and trigger another round of this toxic cycle.
It started with a napkin
One consequential Friday evening, me and my now co-founders met up after work and went across the street to the bar. A few too many pitchers later, we began discussing these issues and on a bar napkin, we drew out a hypothetical system to solve this problem. At the time, we didn’t think it would grow into what BioBox is today. We just wanted the emails to stop so we could get back to our own projects. It was a small simple web-app that loaded their data, and gave them the ability to run basic stats tests, plotting, and simple analyses. After a month of tinkering on weekends, I sent it out to a few colleagues and the results were amazing. Inbox — 0 new emails. Then the feature requests started coming in.
After a little more research into how pervasive this problem was, the three of us decided take the leap and we left our careers behind to found BioBox.
It’s been almost 2 years (at the time of this writing) since we committed to this path. The purpose of our company is to build a platform that provides autonomy back to the biologist by giving them all the tools they need to execute their bioinformatic analyses. In so doing, freeing up the bioinformaticians from requests like, “Please make my plot more red”, and getting their time back to focus on the things they love doing, like developing new algorithms, tools, and pipelines.
We live in a time where sequencing your entire genome is cheaper than your iPhone
We are blessed to live in an era of scientific progress where we’ve generated and collected more biological data than ever before.
But having data is not the same as having knowledge. Transforming data into knowledge relies on the creative/innovative thinking from our biologists and the efficiency/ingenuity from our bioinformaticians.
Pushing the boundaries of science is like rowing a boat upstream. It takes work, commitment, sweat, and energy from our scientists. At BioBox, we’re not rowing the boat for you, we can’t. Only you can. But what we can do is give you the best oars, boat, and gear to support you along your journey. This is the singular mission for us here at BioBox.
This story is a part of a month-long series to highlight the people and mission of BioBox Analytics. Follow along to see what we’re up to as an early-stage Biotech startup:
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A Founder’s Story: Hamza Farooq, CTO of BioBox Analytics (2/3)
A Founder’s Story: Julian Mazzitelli, CIO of BioBox Analytics (3/3)