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 Europe) 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 rapid.

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.

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 systems-on-a-chip (SoCs) as 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

• 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.

Social and cooperation opportunities

The open hardware components can be easily accessed and assembled and thus stimulate fair distribution of the entire technology involved in this project. For device construction, reusable parts will be employed wherever possible in order to reduce the environmental footprint.

Due to the modularity of our hardware components, we aim to create additional devices for self-assembly and use in the lab. We also intend to bring open hardware and software development much closer to synthetic biology, and consider several cooperation opportunities with other iGEM teams and industry partners.