Exemplary Engineering Projects

On this page you will find examples of: Rigorous Characterization of Parts and Devices, Innovative Choices of What to Measure, Innovative Choices of Readout Technique, and Data Collaboration and Sharing.

Teams approach the measurement aspects of their projects in many thoughtful and creative ways. Here are some examples of recent iGEM projects that exemplify a particular aspect of good measurement practices, which are crucial to good engineering.

Rigorous Characterization of Parts and Devices

It is crucial to test the validity of genetic parts or hardware devices across a wide range of conditions, and just as important to document this characterization in an accessible way.

Aachen 2014

As part of their 2014 project, team Aachen built a hardware device that could simultaneously measure the OD and fluorescence of a culture sample in a cuvette. They calibrated both readouts by comparing their device’s performance against existing equipment in their lab on reference samples as well as a variety of biological samples, and wrote up a comprehensive, informative documentation page that describes the principles of their characterization and how to replicate it to validate the operation of devices built using the blueprints they provide.

Marburg 2015

Team Marburg took the promoters used in the 2015 Interlab Study and performed a characterization that embodies many aspects of good measurement practices. They measured the promoters in different cell strains expressing different fluorescent reporters using different readout techniques, and measured not just their mean expression level but also their variability and evolutionary robustness. Their results are concisely documented on their wiki alongside a great set of recommendations on how to improve measurement and characterization practices in iGEM.

Innovative Choices of What to Measure

Measurements in synthetic biology often focus on the behavior of the final reporter in a complex circuit. However, in order to provide a solid groundwork for modular engineering, good measurement approaches should strive to quantify as much of the underlying biological processes as possible.

William and Mary 2015

Recognizing that measurements of promoter expression often focused exclusively on mean expression, team William and Mary decided to measure the intrinsic noise, or variability, of frequently-used promoters from the BioBrick Registry. Through their 2015 project, they provided teams with a guideline on how to measure intrinsic promoter noise and started a conversation in iGEM about the importance of intrinsic part-performance variability in the construction of reliable, safe genetic circuits.

Lambert_GA 2018

Building upon their previous experiments, the 2018 Lambert_GA iGEM team provided an all-around solution to quantitatively measure bacteria colony color, using their Color Q software and Chrome Q hardware.

Innovative Choices of Readout Technique

Measurements in synthetic biology often focus on fluorescent reporters, but fluorescence is often not the best choice for representing certain kinds of biological processes or for performing on-site measurements with a portable device. We encourage teams to think critically about the best way to acquire quantitative values in standardized units with other measurement techniques.

TUDelft 2017

An important aspect of team TUDelft’s portable on-site diagnostic assay for antibiotic resistance was to have a simple readout that did not require complex equipment or training. They developed a clever opacity-based readout called CINDY-seq that can be interpreted with the naked eye, and validated its performance under different usage conditions.

Toulouse 2014

Team Toulouse made use of a diverse variety of measurement approaches to assess the efficacy of various properties of their 2014 project. Importantly, each measurement approach was chosen to try and ensure that its readout most directly represented the underlying process of interest, whether it be chemotaxis, binding ability, or fungicidal activity.

Data Collaboration and Sharing

Good measurement practices allow for better communication between you and other members of your scientific community. A great way to emphasize this aspect of measurement is to participate in collaborative efforts such as the Interlab Study, as well as to create and lead your own collaborative characterization efforts among other iGEM teams. Teams should also ensure that this communication extends to their future scientific community as well, such as by sharing their raw data and placing an explicit focus on clear, informative data visualization.

UGA-Georgia 2015

Team UGA-Georgia’s 2015 project focused on establishing the use of archaea as a chassis for synthetic biology. Understanding the need for replicable measurements of part performance as the basis of engineering, UGA-Georgia led a collaborative characterization effort across a total of 9 teams to measure the consistency of fluorescence expression in the mutated proteins from their archaeal system.

William and Mary 2017

Throughout their 2017 project, team William and Mary ensured that their graphs followed the principles of good data visualization. They represented their categorical data in univariate scatterplots instead of using bar graphs, which can obscure the underlying distribution of the data. Additionally, they reported their fluorescence measurements using the geometric mean and standard deviation, which is the correct way to represent the magnitude and variability in fluorescent expression.