Team:ETH Zurich/expresults/diffusion

From 2014.igem.org

(Difference between revisions)
(Diffusion On Chip)
(Diffusion On Chip)
Line 1: Line 1:
== Diffusion On Chip ==
== Diffusion On Chip ==
-
Our project aims for the biological implementation of [https://2014.igem.org/Team:ETH_Zurich/project/background/modeling#Cellular_Automata cellular automata] with [https://2014.igem.org/Team:ETH_Zurich/modeling/xor#XOR_Logic_Gate XOR] logic gates. In order to achieve this, we had to find a way to create a regular grid of cells with a defined, optimal neighborhood. This means channel length, well size, and medium had to be optimized and properties were modelled with Matlab and Comsol whenever feasible. With that [https://2014.igem.org/Team:ETH_Zurich/modeling/diffmodel ''in silico'' results] we used CAD software to design our custom made mold, which where then 3D-printed and used for the production of PDMS molds ([https://2014.igem.org/Team:ETH_Zurich/lab/chip here] are more details available). The cells containing our gene circuitry were encapsulated in [https://2014.igem.org/Team:ETH_Zurich/lab/bead alginate beads] and loaded on the [https://2014.igem.org/Team:ETH_Zurich/lab/chip millifluidic chip]. This approach allowed us to establish a method for measuring diffusion and cell-to-cell communication. In particular, a step towards the emergence of complex patters by cell-to-cell communication was shown. Also the [https://2014.igem.org/Team:ETH_Zurich/modeling/diffmodel#Pattern_developing Comsol model] regarding pattern formation was confirmed experimentally. The final time-lapse video of the experiment is shown below in video 1.
+
Our project aims for the biological implementation of [https://2014.igem.org/Team:ETH_Zurich/project/background/modeling#Cellular_Automata cellular automata] with [https://2014.igem.org/Team:ETH_Zurich/modeling/xor#XOR_Logic_Gate XOR] logic gates. In order to achieve this, we found a way to create a regular grid of cells with a defined, optimal neighborhood. This means channel length, well size, and medium were optimized and the properties were modelled with Matlab and Comsol whenever feasible. With these [https://2014.igem.org/Team:ETH_Zurich/modeling/diffmodel ''in silico'' results] in minf we used CAD software to design our custom made molds, which where then 3D-printed and used for the production of ([https://2014.igem.org/Team:ETH_Zurich/lab/chip#PDMS_Chip_Preparation PDMS chips]. The cells containing our gene circuitry were encapsulated in [https://2014.igem.org/Team:ETH_Zurich/lab/bead alginate beads] and loaded on the [https://2014.igem.org/Team:ETH_Zurich/lab/chip millifluidic chip]. This approach allowed us to establish a method for measuring diffusion and cell-to-cell communication. In particular, a step towards the emergence of complex patters by cell-to-cell communication was made. Also the [https://2014.igem.org/Team:ETH_Zurich/modeling/diffmodel#Pattern_developing Comsol model] regarding pattern formation was confirmed experimentally with this rapid-prototyping approach. The final time-lapse video of the [https://2014.igem.org/Team:ETH_Zurich/project/background/modeling#Pattern_Formation pattern-formation] experiment is shown below in video 1.
 +
 
{|class="wikitable" style="background-color: white; text-align:center; width:auto; margin: auto;"
{|class="wikitable" style="background-color: white; text-align:center; width:auto; margin: auto;"

Revision as of 12:25, 17 October 2014

Diffusion On Chip

Our project aims for the biological implementation of cellular automata with XOR logic gates. In order to achieve this, we found a way to create a regular grid of cells with a defined, optimal neighborhood. This means channel length, well size, and medium were optimized and the properties were modelled with Matlab and Comsol whenever feasible. With these in silico results in minf we used CAD software to design our custom made molds, which where then 3D-printed and used for the production of (PDMS chips. The cells containing our gene circuitry were encapsulated in alginate beads and loaded on the millifluidic chip. This approach allowed us to establish a method for measuring diffusion and cell-to-cell communication. In particular, a step towards the emergence of complex patters by cell-to-cell communication was made. Also the Comsol model regarding pattern formation was confirmed experimentally with this rapid-prototyping approach. The final time-lapse video of the pattern-formation experiment is shown below in video 1.


Video 1