Revision as of 20:16, 22 October 2010

Information Processing Overview

Information processing principle of E. lemming. Tumbling / directed movement rates are monitored by image processing algorithms, which are linked to the light-pulse generator. This means that E. coli tumbling is induced or suppressed simply by pressing a light switch! This synthetic network enables control of single E. coli cells.

Although the synthetic network we implemented makes the tumbling frequency of E. coli cells dependent on red and far-red light, the biological part alone is not sufficient to control the swimming direction of our E. lemming. Thus, it is complemented by a complex in-silico network centered around a controller which guides the cell towards the desired destination.

The E. coli cells are imaged using microscopy techniques. The resulting images are processed by fast cell detection and cell tracking algorithms, which determine the actual movement direction of the bacterium. The desired reference direction is set by the user and, by means of a controller algorithm, light signals (red light and far-red light) are automatically activated. Therefore, by time-dependently changing the tumbling frequency, the cell is forced to swim in a desired direction in real time.

The current microscope image represents the in-vivo -> in-silico interface, while the red and far-red light signals represent the in-silico -> in-vivo one. By interconnecting both sub-networks, we thus can close the loop and obtain the overall network, which allows us to increase the information processing capabilities significantly compared to traditional synthetic networks completely realized in-vivo.

This section is meant to describe in detail the in-silico part of the overall network. For the in-vivo part, please refer to the Biology & Wet Laboratory section.

Imaging

The cells are placed in a 50 μm (?) high flow channel restricting their movement to the x/y-plane, thus preventing them from swimming out of focus. They are imaged in bright field by an automatized microscope with 40x magnification approximately every 0.3s. The image is send via a local network or the Internet to the controller workstation, which forwards them to Matlab/Simulink.

Cell detection and Cell tracking

In the controller workstation, the images are pre-processed by the Lemming Toolbox and the cells are detected and tracked in real-time, by means of fast image processing algorithms developed by our team. From the change of position between the microscope frames, the current direction of E.lemming is estimated.

User experience

The Toolbox is connected to either a joystick or a keyboard with which the user can choose the cell he/she wants to control and interactively define the reference direction for the E.lemming in real time.

Controller

For controlling E. lemming, our modeling group implemented five different control algorithms, based on the same template. The actual direction of E.lemming, together with the desired direction set by the user and the time-point of the simulation form the inputs of the algorithms, while boolean values for red light and far-red light represent the outputs. Based on original combinations of error minimization, hysteresis, noise suppression and predictions, our algorithms decide when to send red or far-red light. This decision is then send back through the network to the microscope computer, which activates or deactivates the respective diodes, thus closing the loop between the in-silico and in-vivo part of the network.

Furthermore the controller detects if a cell is swimming out of the field of vision of the microscope and automatically adjusts the position of the x/y-stage.

Visualization

Finally, the microscope image is post-processed to show the position of all cells, the selected cell and its current and reference direction, and visualized on the computer screen or with a beamer.

@@ Line 8: / Line 8: @@
 <div class="thumbcaption"><div class="magnify"><a href="http://www.youtube.com/watch?v=1qQBmMcMZDI?hd=1" class="external" title="Enlarge"><img src="/wiki/skins/common/images/magnify-clip.png" width="15" height="11" alt="" /></a></div><b>Information processing principle of E. lemming.</b> Tumbling / directed movement rates are monitored by image processing algorithms, which are linked to the light-pulse generator. This means that <i>E. coli</i> tumbling is induced or suppressed simply by pressing a light switch! This synthetic network enables control of single <i>E. coli</i> cells.</div></div></div>
 </html>
-Although the synthetic network we implemented makes the tumbling frequency of ''E. coli'' cells dependent on red and far-red light, the biological part alone is not sufficient to control the swimming direction of E.lemming. Thus, it is complemented by a complex ''in-silico'' network realizing a controller which automatically sends the light signals and, by thus time-dependently changing the tumbling frequency, forces the cell to swim in a desired direction.
+Although the synthetic network we implemented makes the tumbling frequency of ''E. coli'' cells dependent on red and far-red light, the biological part alone is not sufficient to control the swimming direction of our E. lemming. Thus, it is complemented by a complex ''in-silico'' network centered around a controller which guides the cell towards the desired destination.
-The interface between the two sub-networks, the ''in-vivo'' network and the ''in-silico'' network, is defined as the current microscope image (''in-vivo'' -> ''in-silico'') and the red and far-red light signals (''in-silico'' -> ''in-vivo'').
+The ''E. coli'' cells are imaged using microscopy techniques. The resulting images are processed by fast cell detection and cell tracking algorithms, which determine the actual movement direction of the bacterium. The desired reference direction is set by the user and, by means of a controller algorithm, light signals (red light and far-red light) are automatically activated. Therefore, by time-dependently changing the tumbling frequency, the cell is forced to swim in a desired direction in real time.
-By interconnecting both sub-networks, we thus can close the loop and obtain the overall network, which allows us to increase the information processing capabilities significantly compared to traditional synthetic networks completely realized ''in-vivo''.
+The current microscope image represents the ''in-vivo'' -> ''in-silico'' interface, while the red and far-red light signals represent the ''in-silico'' -> ''in-vivo'' one. By interconnecting both sub-networks, we thus can close the loop and obtain the overall network, which allows us to increase the information processing capabilities significantly compared to traditional synthetic networks completely realized ''in-vivo''.
 This section is meant to describe in detail the ''in-silico'' part of the overall network. For the ''in-vivo'' part, please refer to the [[Team:ETHZ_Basel/Biology | Biology & Wet Laboratory]] section.<br clear="all" />

Team:ETHZ Basel/InformationProcessing

From 2010.igem.org