Endy:Measkit PLO

Introduction

1. Engineering many-component systems is made easier by developing collections of standard parts.
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.
3. Measurement of physical objects is well understood and has been successful developed and applied in other domains (e.g., principle of correlation, et cetera)
4. Measuring biological parts consistently has proven challenging, and may be unlike past experiences (due to...)
   • Due to the sensitivity and unpredictability of part performance to measurement conditions (e.g. cell strain)
5. Still, can frame measurement problem in context of impact of variation in instruments and conditions, which allows for (state three cases).
6. Thus, to try these ideas out / begin to make progress, we designed reference standards for promoters and RBSs, and developed models, that taken together allow for (accounting of some sorts of variation).
7. 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.
8. Taken together we demonstrated the utility of the reference standards (prefatory summary here).

Results

1. Model describing promoter (PoPS) and RBS (RIPS) activity in terms of GFP synthesis rate. E.g. principle of correlation for our GFP-expression based measurement approach.
2. Varying conditions can result in significant changes in the activity of promoters (PoPS)
   • Convert ‘GFP synthesis’ levels to PoPS via our models across different conditions to provide a picture of the range of variation in PoPS.
   • By measuring the same promoter across multiple conditions we can create empirical models for converting between the different conditions.
     • It is an open question how good these models are? Do they hold across many different promoters? (e.g. if one promoter has 2X expression under condition Y does another?)
3. Early results suggest that reference promoter may be able to provide useful models to control for variation in conditions. It seems that across some set of 'standard? should there be a name for these?' conditions the relative activity of at least two promoters may remain fairly constant.
   • Show figure comparing measurement of the activity of test promoter and reference promoter across 9 different measurement conditions (activity shown in PoPS). Should probably only use data where the instrument remains constant, as this will allow me to talk exclusively about the ability of the reference promoter to normalize across some set of measurement conditions (e.g. varying strain, plasmid, temperature, media).
4. We may also adopt a strategy where in some cases we can take a ‘shortcut?’ and only measure a reference promoter without calibrating the instrument to allow for measurement in PoPS. Some instruments may be hard to calibrate to real GFP in first place (e.g. FACS machines) others are easier (microscope, plate reader).
   • In this case we still control for conditions and since we are taking a relative measurement calibrating the machine is unnecessary as long as both the test promoter and the reference promoter are measured by the same machine under the same conditions.
   • Show figure of lab-lab variation across 6 schools with a set of 4 promoters. Report the coefficient of variation in the mean relative promoter activity.
5. Reporting measurements in relative promoter units (RPUs) will allow for characterization of activity that is comparable across some range of conditions and thus is a valuable characteristic to report when describing a part in such a way that it can be re-used.
   • To support part re-use we measured a total of 15 promoters and RBSs to bootstrap a collection of components characterized in this way.
   • We also listed a set of conditions across which a test promoter set maintained fixed RPU measurements within some range of error. (Obvious opportunity for future work here to (1) define a test set of promoters and (2) specify allowable error for a new condition to be added to the OK list).

Discussion

1. Reference standards are good because allows for a consistent framework in which to collect data that will support (first) empirical models regarding how activity of promoters and RBSs varies across conditions.
2. Reference standards are also good because theory of biological engineering not yet developed, and an auto-scaling / relative measurement may be more useful for many applications.
   • make use of counter example of importance of OFF being OFF, for some applications