Endy:Measkit PLO/v2

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Important points

  1. PoPS is an appropriate unit for promoter activity
  2. Promoter activity is sensitive to experimental conditions
  3. Promoter activity may (or may not) vary equivalently within a set of distinct promoters as they are placed in different conditions
  4. Relative promoter activity may be reported in Reference Promoter Units (RPUs) by normalizing to a reference standard, and may allow for the specification of a range of promoters and a range of conditions across which relative promoter activity is predictable. (key point)

Things we'd like to leave out

  1. Calibration of instruments (e.g., using purified GFP of known amounts).
  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, include paragraph in discussion making this point. Also say we need to get PoPS landscape at some point in the discussion.
    • Need to have a paragraph 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


  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. (i.e., reusability is enabled by predictability is enabled by characterization)
    • be scholarly here, making reference to current parts collections, and lack of characterization thereof.
      • For example the registry of biological parts contains X promoters of which Y have been measured.
  3. Making consistent in vivo measurements of biological parts has proven challenging previously because the part being measured is sensitive to experimental conditions
    • yeast pheremone
  4. Making consistent in vivo measurements of biological parts is also challenging because the biological component of the measurement instrument is sensitive to experimental conditions. Furthermore, it is difficult for researchers to keep either the conditions or even the measurement instrument constant.
    • We cannot easily solve this problem by holding conditions constant as 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.
    • Additionally it is difficult to even keep the measurement instrument itself constant, e.g. people use different strains, fluorescent detection devices, etc. Cite example of B-gal.
    • Our models for promoter activity in different conditions are incomplete / non-existent. our models for instrument variation 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. Although characterization of biological parts may be challenging, it is worth at least considering lessons from the measurement of other types of physical objects.
    • Thermal expansion as an example of how the sensitivity of measured properties to measurement conditions has been dealt with previously
    • 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.
    • Cite example of measurement of spines as using a relative unit.
    • 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).

[placeholder note regarding presenting somewhere the Stephanopolous PNAS work as searching for promoters whose activity is not sensitive to the (genetic) instruments used to characterize activity]

Results 2.0

  1. PoPS is the appropriate physical unit for promoter activity
    • note v. briefly how PoPS is a more generic equivalent of promoter clearance rate
    • Note that we don't have a direct method to measure PoPS
  2. We developed a "GFP reporter system" to provide an observable that we can measure.
  3. We developed a model that *could* be used to map the rate of change of an observable variable, GFP synthesis rate, to promoter activity (put details of model in supplement <-- perhaps only have final equation in main text).
  4. We varied both the measurement conditions and the measurement instrument. As expected we see a wide variation in the observed variable, GFP synthesis rate.
    • Figure 1 -- GFP synthesis rate varies greatly under different measurement conditions (strain, media, temperature, culture conditions) and with different measurement instruments (plasmid copy #). A pair of promoters measured across conditions
    • although we have <named model> we didn't convert to PoPS because <named model> does not account for many of these variables, and many parameters unknown
  5. We readily noticed that A & B change in concert, and thus considered the utility of a relative measure of promoter activity. To do this we... We call this relative measure of promoter activity "Reference Promoter Units," or RPU.
    • Figure 2 -- Relative promoter activity is less sensitive to measurement conditions. Ratio derived from Figure 1, showing RPUs
    • keep the text straightforward and be sure to report / take credit / explain the obvious results (e.g., note ranges of ratios).
  6. Given these results, we decided if the same approach would be useful for characterizing promoter activity across multiple locations. To do this we made a "reference promoter set" (supplementary materials and figures) and distributed the set along with instructions to multiple labs. we got back these results (walk reader through the results).
    • Figure 3 -- Relative promoter activity enables distributed characterization of promoters. Show multi-school data
  7. To encourage a distributed research community to measure promoters via RPUs we did two things. First, we measured 6 additional promoters, including many popular parts. Second, we made a "measurement kit" so that...
    • Figure 4 -- Reference promoter is useful across a range of promoter activities. Show full set / range of characterized promoters (~9 different promoters)


  1. Summary of what we did.
    • Demonstrated that absolute promoter activity varies across conditions
    • Demonstrated that relative promoter activity is constant across some range of conditions
    • Demonstrated that we could measure equivalent relative activity across labs that were using different measurement instruments (that would otherwise have provided incomparable measurements).
    • Measured some more promoters and made a kit.
  2. {set up relative v. absolute battle} It is unclear whether absolute or relative measurements will be best. For example, <in support of absolute>. For example, <in support of relative>. Ultimately, the choice of relative or absolute may likely depend on the application.
  3. {next steps for relative} More specifically, we can imagine a future engineering framework based on relative promoter activities rather than PoPS.
    • we collect the sets of conditions where promoters perform stably relative to each other
    • there may be sub-categories (e.g. stationary phase promoter performance may be the same across many different media conditions, but different from exponential phase).
    • We build a theoretical framework that models system performance based on relative activities rather than absolute activities.
      • For instance, we might expect that our system will experience a change in growth rate that might effect absolute promoter activity. Rather than keeping absolute activity constant, we might just design systems that function independent of this. Remind that natural biological systems, such as human anatomy, seem to similarly be based on constant ratios rather than constant absolute lengths.
  4. {next steps for absolute} Although relative promoter activity is useful, "absolute" promoter activity will e critical in some cases (e.g. strong off needed)
    • To support this we can begin to develop the PoPS-conditions landscape.
    • With a good enough PoPS-map we may be able to eventually predict the performance of new conditions a priori.
  5. Additional future work that seems worth doing
    • Establish a reference set of promoters and an allowable deviation from known values for a set of conditions to be considered “approved”.
    • Evaluate many new conditions to see how many are “approved”
    • Evaluate sensitivity to the measurement approach
      • we varied some parts of the measurement instrument (for instance, plasmid copy number), but should change the FP as well
      • Insulated promoters – promoters with activities that are insulated from the surrounding sequence will be better suited to reliable measurement as they will be less sensitive to the measurement instrument used.
    • Improved measurement instruments – e.g. moving to the chromosome, using more sensitive reporters for promoter activity, using multiple reporters to better control for variation in experimental conditions.
  6. Distribution, use, and improvement of kit
    • Reference standards only useful if they are adopted by a community. So need to make them readily available and easy to use…


  1. Use a consistent vocabulary for terms from measurement theory
    • Should probably use the Hand vocabulary as it is the most current – and in the intro he is pretty complete about providing different words.
  2. Is it worth having a diagram showing what parts of the system are the measurement instrument and what parts are the measurement conditions?
    • Consider for introduction