Interworking: From computer science to biology and back

Schematical overview of the connection between biology and computer science. Wet laboratory provided mathematical modeling and information processing with experimental data and knowledge, while mathematical modeling supported the other two with parameter evaluation. Information processing provided wet laboratory with experimental validation of the biological implementation and used the combined mathematical model as test bench.

E. lemming is a special project, because the final product has biological and computer science 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. This process is called interworking: multiple parts interact on each other to create a comprehensive result.

Experimental data

Bacterial movement

<<< how did experiments help to improve the bacterial movement model? write about this here. >>>

Cell detection

<<< how did experimental data (E. coli) help to create cell detection? write about this here. >>>

Parameter evaluation

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. Parameter Evaluation: Insights for wet laboratory provide more information about this topic.

**Table 1: Evaluation results for wet laboratory**
Che	LSP1	LSP2	[Asp]	[AP]	[anchor]
CheY	PIF3	PhyB	10^-6 uM	40 uM	50 uM

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 Parameter Evaluation: Insights for information processing for more details.

**Table 2: Evaluation results for information processing**
model	tc RL	tc FRL
Spiro et al.	0.2200s	0.3485s
Mello & Tu	0.1380s	0.3205s

Team:ETHZ Basel/Achievements/Systems Design

From 2010.igem.org