Welcome to the Teamwiki of the iGEM Aachen in 2014!
Cellock Holmes - a case of identity
Nosocomial infections - solving a real-world problem
Existing sterilizing methods fail to completely eradicate harmful pathogens on solid surfaces, resulting in a high number of patients (~3.2 million in Europe1) to be treated annually due to such infections. The European Union estimated that at least 800,000 of these cases would be preventable by implementing an intensive hygiene program, raising the necessity for a real-time detection technique. Current techniques fail since they are not effective, economical and rapid2.
Our iGEM Aachen team, consisting of life science, engineering as well as computer science students, aims to tackle these difficulties by developing a real-time pathogen detection technique, called ‘Cellock Holmes’, using SynBio and open hardware.
Our project is not only applicable to the detection of pathogens but we are looking to develop it further into a platform for a general 2D detection of nearly any cell or substance.
Strategy and focus
While detection is the first step, characterization and quantification are equally important to reliably assess the danger of an infection.
Cellock Holmes is devised based upon a SynBio approach comprising of a two-dimensional biosensor and a measurement device . The two-dimensional biosensor (Figure 1) is designed to recognize quorum sensing molecules secreted by the pathogen cells and generate a distinct fluorescence signal.
Figure 1: Sampling of microorganisms (left) for the detection with a 2D biosensor (right)
In parallel, the team also aims to develop a more flexible novel molecular detection system for the biosensor based on binding proteins and genetic probes.
In parallel, the team also aims to develop a more flexible novel molecular detection system for the biosensor based on binding proteins and genetic probes.
These molecular approaches were selected as a reasonable spectrum from the established to novel and high-potential modules.
The Aachen team is committed to consider all aspects of the entire project, including biosafety. The living cells inside the measurement device are ensured to be killed by irradiation with strong UV light. In this way neither the sampled pathogens nor the genetically modified sensor cells can escape our biosensor unit.
Modular hardware ‒ Open for versatility
Figure 2 Measurement device based on counting of visual signal density
By embracing the open hardware approach and using both low- and high-level components such as Arduino microcontrollers and Raspberry Pi, we maximize the measurement device’s versatility.
The visual signals generated by the biosensor will be captured by a camera (Figure 2) and analyzed by our measurement software, ‘Measurarty’. The software uses modern region- and graph-cut-based evaluation methods to analyze the data efficiently.
The device will be finally tailored to perfectly fit the needs of end users, for example by minimizing the need for electricity.
Direct Applications
Detection and identification of pathogens with Cellock Holmes is crucial in different scenarios such as
Hospitals
R&D labs
Nursing homes
Food & water industry
The cost-efficiency allows for a standardized method to uncouple low-budget institutions from the need for expensive equipment and highly trained personnel.
Furthermore, we would like to conduct an economic review of our project and, depending on the outcome, a business strategy based on the improvement and distribution of hardware and biological component kits.
The Device
Our device Cellock Holmes is designed to be an automated 2D fluorescence analyser. We aim to be able to quickly measure the fluorescence emitted by our sensor cells and automatically analyze the emitted images with our software Measurarty. This will enabel us to reliably detect the amount of CFU (colony forming units) of the pathogens present on the sample. The device will have different filters for different wavelenghts included to be able to analyze different fluorescent proteins at the same time.
To learn more about our device check out
Cellock Holmes.
The software Measurarty
The third part of our project is our software Measurarty. The software will allow us to analyze the fluorescence emitted by our sensor cells in a more advanced and better way than just using a simple Treshold. We will utilize a modern segmentation algorithm in combination with further, detailed image processing algorythms.
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