Meeting Measurement Precision Requirements for Effective Engineering of Genetic Regulatory Networks

We've got a new preprint up today, "Meeting Measurement Precision Requirements for Effective Engineering of Genetic Regulatory Networks", that is an unusual mixture of theoretical analysis and interlaboratory study. 

The work started out as an investigation of the replicability of flow cytometry measurements. Flow cytometry, as readers of this blog may know, is one of my favorite biological measurement tools, since it lets us obtain measurements from large numbers of individual cells. I've been involved in a number of projects that have put it to good use in engineering biological devices, and the calibration methods available let us put real, biologically-sensible units on the measurements. But just how good are these measurements and how reproducible?  That's what we set out to study with a consortium of collaborators and about two dozen flow cytometers.

Then we went to go write it up, and a rabbit hole opened beneath our feet, sucking us down into an unexpected set of theoretical questions. We had a number (~1.5-fold precision), but was that a good number? In fact, how do we even decide what a good number is? What do you even need to do good engineering? 

Maybe we should have just called that "future work" and published what we had. But we didn't. We followed that rabbit hole down and the manuscript went into limbo. But when it came out of limbo, the manuscript was standing on its head and had an answer. What started as an investigation of flow cytometry became an investigation of the general requirements for effective biological engineering, with the work on flow cytometry becoming one verified answer for how to meet those requirements.

Basically, you want to be on the left side of the red line.

We ended up with a (highly abstract, conservative) formula for estimating how well one needs to know values in order to engineer gene regulation. And for most state of the art work, it means you need to have a measurement precision somewhere in the range of 1.2-fold to 2.0-fold, with calibrated flow cytometry right smack in the middle.

I'm happy with these dual results, and I think they should be useful to help us move another couple of steps towards a world of reliable and predictable biological engineering.