Team:Aachen
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<i> Cellock Holmes </i> is devised based upon a SynBio approach comprising of a <b> two-dimensional biosensor and a measurement device </b>. The two-dimensional biosensor (Figure 1) is designed to recognize quorum sensing molecules secreted by the pathogen cells and generate a distinct fluorescence signal. | <i> Cellock Holmes </i> is devised based upon a SynBio approach comprising of a <b> two-dimensional biosensor and a measurement device </b>. The two-dimensional biosensor (Figure 1) is designed to recognize quorum sensing molecules secreted by the pathogen cells and generate a distinct fluorescence signal. | ||
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- | + | <img src="https://static.igem.org/mediawiki/2014/e/ec/Aachen_Infografik_sampling_biosensor.png" width="700" /> | |
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<i>Figure 1: Sampling of microorganisms (left) for the detection with a 2D biosensor (right)</i></figcaption> | <i>Figure 1: Sampling of microorganisms (left) for the detection with a 2D biosensor (right)</i></figcaption> | ||
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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. | ||
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<figure style="float: right; margin: 0px 15px 15px 0px;" > | <figure style="float: right; margin: 0px 15px 15px 0px;" > | ||
<img src="https://static.igem.org/mediawiki/2014/3/36/Aachen_Infografik_measuring.png" width="400" /> | <img src="https://static.igem.org/mediawiki/2014/3/36/Aachen_Infografik_measuring.png" width="400" /> | ||
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<i>Figure 2 Measurement device based on counting of visual signal density</i></figcaption> | <i>Figure 2 Measurement device based on counting of visual signal density</i></figcaption> | ||
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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. | 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. | ||
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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 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. | ||
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The device will be finally tailored to perfectly fit the needs of end users, for example by minimizing the need for electricity. | The device will be finally tailored to perfectly fit the needs of end users, for example by minimizing the need for electricity. | ||
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Direct Applications | Direct Applications |
Revision as of 11:19, 3 June 2014
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