Team:ETHZ Basel/Achievements/Systems Design
From 2010.igem.org
Systems Design
E. lemming is a special project, because the final product has biological and engineering aspects in equal parts. For this reason, it was decided to connect the wet laboratory and the modeling subteam closely from the very beginning to make the best decisions for the whole project.
Wet Laboratory: Experimental data
Bacterial movement
Besides its theoretical importance of closing the modeling loop of our system, the bacterial movement model also brings together biological and theoretical results, by the in silico reproduction of the in vivo observed data.
All parameters of this model are based on statistical estimates of the biologically observed chemotaxis behavior. Furthermore, by fixing the parameters for which the biological evidence supports independence, the model is accurately estimating the remaining set of parameters, providing us with reliable biological feedback & novel insights.
In this way, the wet laboratory team received the modeler's input on the predicted final behavior of E. lemming, which served as a further input in tuning the design of the wet laboratory experiments.
Cell detection
The starting point of the cell detection algorithm were the microscope images of wild type E. coli, which are targeted for detection. The parameters of the algorithm (eg. threshold for Sobel filter, filter parameters to distinguish real cells from other objects & spots) were tuned using the properties of real cells.
Mathematical Modeling: Experimental design
Modeling insights for wet laboratory
Mathematical modeling of biological pathways should never be an end in itself. Instead, mathematical models should be build to give insights into the pathway of interest, which are otherwise hard to obtain, or to speed up experimental work. In this project, a molecular setup for implementation by the wet laboratory was evaluated and resulted in an effort alleviating priority list of BioBrick candidates. Experimental Design: Insights for wet laboratory provide more information about this topic.
Che | LSP1 | LSP2 | [Asp] | [AP] | [anchor] |
---|---|---|---|---|---|
CheY | PIF3 | PhyB | 10^-6 μM | 40 μM | 50 μM |
Modeling insights for information processing
Furthermore, the combined model was used to estimate the reaction time of the network on the red and far-red light pulses and the optimal configuration of the light pulse setup, crucial for the controller design. See Experimental Design: Insights for information processing for more details.
model | tc RL | tc FRL |
---|---|---|
Spiro et al. | 0.1524s | 0.4092s |
Mello & Tu | 0.1305s | 0.2985s |
Download
The evaluation script for experimental design can be downloaded from SourceForge.net. It requires the E. lemming Matlab Toolbox to be installed. The script is also included in the complete package.
- [http://sourceforge.net/projects/ethzigem10/files/experimentalDesign.zip/download Download experimental design from SourceForge.net]
Information Processing: Experimental validation
Information Processing is used to validate the experimental design and implementation. Using the in silico testbench from mathematical modeling, information processing was able to validate the general principle of the selected experimental design, given validity of used assumptions.