Friday, May 10, 2013

Mathematics + Biology: A Stem Cell Genetics Love Story

(photo: Wikimedia Commons)
A video by Stanford University recently reminded me of my own surprise, as a biologist, of the power of math. The video and corresponding Scope blog entry describes Stanford stem cell scientists getting a shock when a mere engineer gave them a lesson in speeding up research.

One nice perk to choosing a cell biology path in college was my light load of math classes. Years later, ironically enough, I took a job at a company that simulates human physiology and disease using mathematical models. I pored through published research data while my engineering and mathematician colleagues applied that data to our models. I was astonished at and became a true believer in the power of math to give rapid, non-intuitive insights into complex biological questions.

The model predictions were often a tough sell on our customer-scientists maybe because, like me, they had developed an aversion to math during their studies. “How can you possibly make a math model of biology? There’s too much complexity and we don’t know enough yet”, was a typical knee-jerk response.

These memories were re-awoken this week when I watched Stanford’s video. CIRM-grantee Irv Weissman describes how a few years back Debashis Sahoo, then an electrical engineering graduate student, approached him with a sort of bet: give him information about two key genes involved in stem cell development and within seconds his novel mathematical algorithm will screen through public databases to identify new genes involved in that same developmental pathway. Weissman recalled the meeting:
So of course we laughed. That was impossible… He turned back to his computer, interrogated the whole database…and came up with 17 genes in the middle from his invented algorithm that saved us a decade of work. To cover the grants is about $250,000 a year. So if it was 10 years, that’s $2.5 million. 
This work, funded in part by a grant from CIRM, was a validation of Sahoo’s approach. And so like a Johnny Appleseed of genetics, Sahoo has followed up by wandering the Stanford halls using his algorithm to help cultivate new insights for each lab he visits. In one published case, his tool identified cellular tags that could predict how aggressive a bladder cancer will be. This finding could provide diagnostics that are more rapid and less expensive than existing ones.

“More rapid” and “less expensive” are music to our ears. This math meets biology collaboration is the epitome of how our agency supports innovative, interdisciplinary approaches to stem cell research in order to accelerate the pace of getting basic discoveries to the clinic.

See a previous blog and video about a CIRM Tools and Technology Award for another example of interfacing engineering and stem cell biology as a means to overcome roadblocks to the clinic. 


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