Measurement/How to Succeed

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How to Succeed


Tips for Teams

Before you begin working in the lab, you should think about how you will measure your results. Careful measurement practices are a hallmark of successful iGEM projects (see exemplary projects). But think about going beyond validating your own project: the most impressive teams produce robust, reproducible results that others can build on for years to come.

Here are a few general tips to get you started:

  • Be creative! We love seeing new and innovative approaches that showcases what’s unique about your measurement activities.
  • Understand the limits of your methods. No instrument or assay is perfect. Learn the range of signals that can be measured, the precision that can be expected, and the typicals types of errors and artifacts. Determine how many replicates are necessary, include process controls, and report an appropriate number of significant figures.
  • Communicate clearly about units and controls. Report in a way that shows the source and validation of your data so that the judges and teams better understand what you have accomplished.
  • Use the measurement kit provided as part of the iGEM distribution and other suggested resources
  • Provide data that others can use. Consider what you can do to help other teams reuse and apply your work. How might your approach be used in different contexts?


In addition, here are four specific rules to follow to ensure good measurement practices:

  1. Report measurements in standard, comparable units. Do not use arbitrary or relative units. The resources page gives methods for calibrating fluorescence data.
  2. Always include process controls to validate your protocol and instruments. Process controls are samples with known behavior, such as fluorescein, wild-type cells, and GFP driven by a strong constitutive promoter. These should give the same behavior in every experiment. If they do not, then you know there is likely a problem with your experimental data.
  3. Use appropriate statistics. Gene expression is a complex catalytic reaction, and for that reason its variation is expected to be multiplicative rather than additive, and we should generally compute geometric statistics instead: means and standard deviations on the logarithm of the data. It is also important to distinguish between the mean standard deviation, such as you'd want to use to report the variation in single cell behavior, and the standard deviation of means, such as you'd want to use to report the amount of variation between replicates.
  4. Present data clearly. Write your units on your axes, include process control information for comparison, distinguish between geometric and arithmetic statistics, and use a log axis when presenting geometric statistics.

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