"Demonstrate your learning capabilities," how exactly to do that, I wondered. Develop a mental model!
I've spent the past few hours reading about the field of genomics & next-gen sequencing, with respect to one firm: Seven Bridges Genomics.
First I developed a sense of...
- Promise -- Hard: Routine genomic diagnosis; Harder: Personalized Medicine
- Problem -- Next-gen sequencing Data → Actionable Results
- Solution -- ???
After that, I was bit stuck. How can one summarize an entire field with one mental model, one graphic.
I thought about...
- Competitive landscape (SWOT)
- BCG Matrix (Definite with ? for most firms)
- Key players (Companies, People, Locations)
But none of those are quite it. What is the root cause of the Problem. Here is a perfectly, imperfect mental model (all mental models are wrong, but some are useful) that seems to be working for me...
I think it comes down to drivers and constraints. Drivers being those things that push a technology forward, which of course require some metric to track changes in status (italics). Constraints being the rate-limiting resource which most hamper development, also complete with metrics (not-shown, but examples would be number of distinct technologies in the pipeline vs. maturity/ETA, cost per base-pair).
Each aspect of the ecosystem, from sequencing → assembly → analysis, has its own unique set of drivers and constraints.
Now, is there a rate-limiting step in the ecosystem as a whole? If so, that's as good a place as any to begin with high-impact solution...
- Sequencing -- Cost and time is already falling, with a healthy pipeline of new technologies (i.e. not a 'Pfizer').
- Assembly -- Incremental improvements in Robustness and speed. Throwing more compute (cheap!) at it generally seems to do the trick.
- Analysis -- More data (sequencing & assembly) don't seem to be resulting in more actionable insight. Ding ding. I think we have a winner.
One thing that may temper going for the rate-limiting step is relative easiness of attacking other problems first. They're all hard problems, so lets stick to our guns and go with rate-limiting.
Which brings us full-circle, back to Seven Bridges Genomics and their solution, Igor, a cloud-based analysis framework.
The software is constraint-oriented, knocking down barriers to compute and people. Let our clever architecture and Amazon Web Services (AWS) take care of the computational scaling. Let our clever bioinformaticians do the heavy-lifting, standardizing workflows for common problems, adapting and scaling existing solutions and maybe even banging out something completely novel.
The result -- time and cost savings due to the experience curve effects, standardization and economies of scale. Awesome, no?
It remains to be seen whether they can compete effectively. It's a crowded space, with no clear market leader; but that's a story for Part 2. Other takes here and here.
PS. I also quite enjoyed the play on the Seven Bridges problem (aside: at least I think it's intentional). Change the graph, e.g. bombing a bridge -- which is more or less what they hope to do with the analytics end of things, -- and you can force a solution.