Endy:Measkit PLO/v2

Overall

Important points

1. PoPS is an appropriate unit for promoter activity
2. Promoter activity is sensitive to experimental conditions
3. Promoter activity may be effected equivalently across different promoters when they are placed in different conditions
4. Relative promoter activity measured in SPUs by normalizing to a reference standard may allow for the specification of a range of promoters and a range of conditions where relative promoter activity is predictable. (key point)

Things I'd like to leave out

1. Calibration of instruments to make PoPS measurement.
2. Reporting our measurements in PoPS on axis in figures. We didn't seriously determine the values of parameters in our GFP->PoPS model for any of the different conditions. We can say that this is significant work and in general is a strike against measurement in PoPS.
   • Need to have aparagraph describing why PoPS is hard to measure. It may be useful to include a sample calculation relating PoPS to RPUs in order to demonstrate the different constants, etc.
3. RBS measurements - The only RBS measurements were taken in the Endy lab and we lack the significant multi-condition data and the lab-lab data that the promoters have. Also it requires a separate run through of the model and presents a more challenging set of issues with canceling some parameters since different RBS = different mRNAs. My main concern is that it distracts from the overall story. We could mention, but i'd rather see it in the Supplementary materials with a mention in the discussion?

P-level outline

Introduction

1. Engineering many-component systems is made easier by developing collections of standard parts that can be reused.
2. It is easier still to predict the behavior of engineered biological systems assembled from standard parts if the component parts themselves were well characterized.
   • be scholarly here, making reference to current parts collections, and lack of characterization thereof.
     • For example the registry of biological parts contains X promoters and Y RBSs Z of which have been measured.
3. Measurement of physical objects is well understood and has been successfully developed and applied in other domains (e.g., principle of correlation, et cetera). A couple lessons:
   1. In order to make a measurement we need (1) a method of measurement and (2) a principle of correlation. For example... in measuring length...
   2. In order to control for the effect of conditions on the object being measured, we can (a) hold the conditions constant, (b) measure objects that are insensitive to conditions (c) Specify models that predict activity across different conditions.
4. Measuring biological parts consistently has proven challenging, and may be unlike past experiences
   • It is unrealistic to expect that researchers will be able to use standard conditions in the short term. This is both due to practical constraints of replicating the exact media, cells, growth conditions, etc, in independent laboratories as well as due to engineering constrains where different applications simply require different operating conditions (much as cars must drive in different temperature and weather).
   • The property we are measuring, promoter activity, is sensitive to experimental conditions.
   • Our models for promoter activity in different conditions are incomplete / non-existent.
   • "There is no such thing as a standard component, because even a standard component works differently depending on the environment." -- New York Times, Tuesday, Jan 17, 2006, Custom-Made Microbes, at Your Service
5. Biological part measurement is not as hopeless as it looks, we can address the challenge of part performance varying across conditions by measuring relative promoter activity to a reference standard.
   • Would be nice hear to have an example of another measurement unit that is reported as a relative measure in order to account for changes in the property being measured due to measurement conditions.
   • Over time the empirical data collected across many conditions will begin to remedy our lack of theoretical(?) models of promoter activity under different conditions.
6. Thus, to try these ideas out / begin to make progress, we designed a reference standard for promoters and developed models, that taken together allow for (accounting of some sorts of variation).
   • We did the following (i) used a reference standard to evaluate variation in conditions and instruments ourselves, and (ii) distributed a reference kit to validate the approach across multiple labs. Taken together we demonstrated the utility of the reference standards (prefatory summary here).

Results

1. We encapsulated our current understanding of the relationship of the observed property (GFP synthesis) to the property we are trying to measure (PoPS) in a mechanistic model. We used this model to demonstrate that by measuring relative promoter activity (in units of SPUs) cancel out many of the aspects of measurement conditions that are likely to effect promoter activity (e.g. GFP maturation rate).
   • note that other models would be derived for other measurement methods
   • show the figure with sensitivity analysis of model parameters.
2. Varying conditions produce different levels of observed GFP synthesis
   • Some of these conditions are likely to change the activity of the promoter itself (e.g. more polymerases in different strains), others change the parameters of our model relating GFP to PoPS (e.g. copy # of the plasmid)
   • Note that parameterizing the model for GFP->PoPS for each set of conditions is challenging and we did not do it here. As a result we can't report results in PoPS (for instance, the variation in GFP synthesis levels could be entirely due to copy number changes under each condition - we don't know).
   • Although the observed measurement (GFP synthesis) varies with conditions, we expected from the model that by reporting a relative promoter activity many of the sources of variability will fall out.
3. Relative promoter activity holds constant across several conditions
   • Again, we expect this based on our model
   • Show the plot comparing absolute measures of a single promoter to relative measures of the same promoter across 7 conditions
   • These results suggest that we might be able to colelct a set of promoters and a set of conditions where relative promoter activity (SPUs) is predictable.
4. We tested the practical value of SPU measurements by measuring 4 promoters across 6 independent labs
   • Show the figure of inter-lab measurement
   • The labs were able to measure equivalent relative promoter activities supporting the value of SPUs as a measurement unit