Quantitative estimation of activity and quality for collections of functional genetic elements

Journal name:
Nature Methods
Year published:
Published online


The practice of engineering biology now depends on the ad hoc reuse of genetic elements whose precise activities vary across changing contexts. Methods are lacking for researchers to affordably coordinate the quantification and analysis of part performance across varied environments, as needed to identify, evaluate and improve problematic part types. We developed an easy-to-use analysis of variance (ANOVA) framework for quantifying the performance of genetic elements. For proof of concept, we assembled and analyzed combinations of prokaryotic transcription and translation initiation elements in Escherichia coli. We determined how estimation of part activity relates to the number of unique element combinations tested, and we show how to estimate expected ensemble-wide part activity from just one or two measurements. We propose a new statistic, biomolecular part 'quality', for tracking quantitative variation in part performance across changing contexts.

At a glance


  1. Composition of irregular transcription and translation genetic elements.
    Figure 1: Composition of irregular transcription and translation genetic elements.

    Schematic of 7 widely used promoters (p) and 11 5′ UTR (u) elements assembled in combination with two different genes of interest (GOIs), gfp and rfp, on a medium-copy (p15A) plasmid with chloramphenicol (Cam) resistance marker in E. coli (full element sequences via Supplementary Table 1). Promoters, 5′ UTRs and GOIs are typically considered to be well-defined, functionally independent genetic elements (abstract layer). However, irregular part boundaries create combination-specific junctions (physical layer) as parts are reused in combination (bottom). RBS, ribosome-binding site; SD, Shine-Dalgarno region.

  2. Observed variation and correlation of mRNA abundance and protein fluorescence from a full combinatorial library of expression control elements.
    Figure 2: Observed variation and correlation of mRNA abundance and protein fluorescence from a full combinatorial library of expression control elements.

    (a,b) Heat maps showing mRNA abundance for all combinations of transcription (p, rows) and translation (u, columns) elements driving the expression of gfp (a) or rfp (b). Each value is a dimensionless number corresponding to mean mRNA abundance measured from a cell population by bulk qPCR divided by the average abundance for all constructs within that panel. (c,d) Similarly mean-centered values for population average fluorescence intensities as measured by flow cytometry. The order of the elements in the matrices corresponds to a two-dimensional clustering performed on the data in c and held constant to facilitate visual comparison. Abundances are expressed on a log2 scale (mean-centered arbitrary units (a.u.)) and colored (thermometer scale). (e,f) mRNA abundance versus fluorescence for constructs driving gfp (e) and rfp (f) expression. (g,h) Pairwise comparison between mRNA levels (g) and fluorescence (h) for constructs driving gfp and rfp expression.

  3. Quantification of factors and interactions contributing to variation in mRNA abundance, translation efficiency and gene expression.
    Figure 3: Quantification of factors and interactions contributing to variation in mRNA abundance, translation efficiency and gene expression.

    Full factorial ANOVA50 was conducted to quantify the average contributions from genetic element types, and from interactions among elements, with respect to total variation in measured gene expression levels. (ac) Contributions of elements and interactions to total variation in protein fluorescence (a), mRNA abundance (b) and translation efficiency (c). 'Experimental error' represents the final term, ε, from equation (3) in Online Methods.

  4. Performance and quality scores for transcriptional and translation control elements.
    Figure 4: Performance and quality scores for transcriptional and translation control elements.

    Primary part-activity scores (bar heights, log2) giving the relative contribution of each promoter (p), 5′ UTR (u), and gene of interest (GOI) to observed fluorescence. Error bars indicate the standard error of all interactions involving each element with all other elements in a different functional category (Online Methods). As such, error bars reflect the variation of element performance in response to changes in proximal genetic context. Reciprocal interactions are color-coded as follows: gray, transcription elements and GOIs; blue, transcription and translation elements; green, translation elements and GOIs.

  5. Estimation of part activity with limited measurements.
    Figure 5: Estimation of part activity with limited measurements.

    (a) Estimated activity for the promoter p1 with increasing numbers of 5′ UTRs. n, number of possible unique 5′ UTR combinations as a function of the number of 5′ UTRs tested. (b) Estimated activities of the 5′ UTR u10 with increasing numbers of promoters. (c) Relative error, averaged across all promoters, in estimating the activities of promoters with increasing numbers of 5′ UTRs (Online Methods). (d) Relative error, average across all 5′ UTRs, in estimating the activities of 5′ UTRs with increasing numbers of promoters. The individual parts (red) and part pairs (blue) that give the highest accuracy in estimating the activity of any new element are indicated.


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Author information

  1. These authors contributed equally to this work.

    • Vivek K Mutalik,
    • Joao C Guimaraes,
    • Guillaume Cambray,
    • Drew Endy &
    • Adam P Arkin


  1. BIOFAB International Open Facility Advancing Biotechnology, Emeryville, California, USA.

    • Vivek K Mutalik,
    • Joao C Guimaraes,
    • Guillaume Cambray,
    • Quynh-Anh Mai,
    • Marc Juul Christoffersen,
    • Lance Martin,
    • Ayumi Yu,
    • Colin Lam,
    • Cesar Rodriguez,
    • Gaymon Bennett,
    • Jay D Keasling,
    • Drew Endy &
    • Adam P Arkin
  2. Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

    • Vivek K Mutalik,
    • Jay D Keasling &
    • Adam P Arkin
  3. Department of Bioengineering, University of California, Berkeley, Berkeley, California, USA.

    • Vivek K Mutalik,
    • Joao C Guimaraes,
    • Guillaume Cambray,
    • Quynh-Anh Mai,
    • Marc Juul Christoffersen,
    • Lance Martin,
    • Ayumi Yu,
    • Colin Lam,
    • Cesar Rodriguez,
    • Gaymon Bennett,
    • Jay D Keasling &
    • Adam P Arkin
  4. Department of Informatics, Computer Science and Technology Center, University of Minho, Campus de Gualtar, Braga, Portugal.

    • Joao C Guimaraes
  5. Department of Chemical & Biomolecular Engineering, University of California, Berkeley, Berkeley, California, USA.

    • Jay D Keasling
  6. Joint BioEnergy Institute, Emeryville, California, USA.

    • Jay D Keasling
  7. Department of Bioengineering, Stanford University, Stanford, California, USA.

    • Drew Endy
  8. Present addresses: Department of Bioengineering, Stanford University, Stanford, California, USA (L.M.); Philotic, Inc., San Francisco, California, USA (A.Y.); Autodesk, Inc., San Francisco, California, USA (C.R.); and Center for Biological Futures, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA (G.B.).

    • Lance Martin,
    • Ayumi Yu,
    • Cesar Rodriguez &
    • Gaymon Bennett


V.K.M., D.E. and A.P.A. conceived the study; V.K.M., G.C. and Q.-A.M. designed experiments; V.K.M., G.C., Q.-A.M., L.M., A.Y. and C.L. performed experiments; J.C.G. and G.C. built the computational model; V.K.M., G.C., J.C.G., D.E. and A.P.A. analyzed and interpreted the data; C.R. and M.J.C. provided software tools and database support; G.B. provided critical feedback on the framing the project; and V.K.M., J.C.G., G.C., J.D.K., D.E. and A.P.A. wrote the manuscript. All authors discussed and commented on the manuscript.

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The authors declare no competing financial interests.

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