Monday, May 19, 2014

Due Diligence: Seven Bridges Genomics (Part 1)

"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...

Mental Model for Genomics & Next-gen sequencing landscape

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 solutionIgor, 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.

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