Team:Austin UTexas/Design

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Making the Burden Monitor

The burden monitor was designed by incorporating a constitutive GFP cassette in the genomic DNA of E. coli, as described in Ceroni 2015. [1] This was achieved by cloning the GFP cassette into the CRIM integration vector plasmid pAH63. The cassette was then inserted into the λ loci of the DH10B genome with the assistance of the low copy helper plasmid pINT-ts that facilitated the “curing” of pAH63 after genomic insertion. [2]

Figure 1. Burden Monitor


Figure 2. Metabolic burden workflow.

The way we examine burden is two-fold:

  • First, we identify burdensome parts by measuring and comparing their growth rates. A reduction in growth rate is a telltale sign of the presence of significant metabolic burden.
  • After burdensome parts have been identified, using the Ellis Lab burden monitor design, we examine GFP expression to later identify the types of burden that the constructs experience.

  1. Growth Rates

    We ultimately quantified the metabolic burden of BioBricks via percent reduction in growth rate. The growth rate value for each part was obtained from burden assay runs during exponential phase growth.

  2. GFP Expression

    A low GFP expression rate indicated a higher level of burden and a high GFP expression rate indicated a lower level of burden.

  3. Burden data collection is a multi-step process:

    Find the full protocol here!↗

    First, BioBricks from iGEM distribution kits are transformed into the burden monitor E.coli strain. A colony is then selected from each plate and an overnight culture is made with LB, Kanamycin, and a third antibiotic (Chloramphenicol, Ampicillin, or Spectinomycin depending on the plasmid backbone). Each culture is then loaded in triplicates onto a 96-well plate that is then placed inside of a platereader that reads both optical density and GFP expression rate over a course of 6 hours. This platereader then outputs a measurement file that we pipe through our R script to make burden graphs.

    Quantifying two types of burden

    Five standards were created - constructs with known ribosome binding site and promoter strengths (ranging from weak to strong). By first measuring the burden of each of these, a calibration curve was established. Any deviation from the calibration curve implied additional cellular burden. However, deviation could happen in two ways, each with different implications for burden, that will be explored below. On the following graphs, the blue data point represents a burden monitor strain with no plasmid (and therefore no burden). The red data point represents a transformant strain that contains a genetic circuit that is imposing a burden upon the cell.

    1. Translational Burden

      As seen in Figure 3, any displacement along the calibration curve can be interpreted as burden that has resulted solely from ribosomal reallocation. This means that the host cell's ribosomes are being redirected away from its own genome, and towards the genetic construct that has been introduced into it. In this case, we can quantify the burden based on the percent deviation in growth rate from the calibration curve.

      Figure 3. Graphical depiction of burden due to ribosomal reallocation alone.

    2. 'Other' Burden

      As seen in Figure 4, any displacement away from (in this case, to the left of) the calibration curve can be interpreted as burden that has resulted from a source other than ribosomal reallocation. There is much speculation as to where this 'other' burden originates from, but one very likely possibility is toxicity because oftentimes the translated product of the introduced genetic circuit can be harmful to the sustenance of the host cell.

      Figure 4. Graphical depiction of 'other burden'- burden due to causes other than ribosomal reallocation.

    3. References

      [1] Ceroni, F., Algar, R., Stan, G., & Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nature Methods, 12(5), 415-418. doi:

      [2] Haldimann, A., & Wanner, B. L. (2001). Conditional-Replication, Integration, Excision, and Retrieval Plasmid-Host Systems for Gene Structure-Function Studies of Bacteria. Journal of Bacteriology,183(21), 6384-6393.