Using Control Theory to Elucidate Biological Signalling Networks (BioSysBio 2009)

M A J Roberts et al.
University of Oxford

Personal comment: I think the title might have changed, but I was too slow on the title slide to get it.

His research focuses on chemotaxis pathways. In E.coli chemotaxis, the signal is sensed by MCP and changes the rate of CheA autophosphorylation. CheA can phosphotransfer to CheY and CheB. CheY-P interacts with motor leading to motor switching and direction changing of the bacterium. CheB-P demethylates MCP resulting in adaption (memory). CheR methylates the receptor. The pathway is less well understood in other bacteria. There are often multiple homologues and therefore have a higher complexity. One example is R.sphaeroides, which has two main chemotaxis operons: CheOp2, and CheOp3. But they don't know how the pathway works or how it is connected. He's working on figuring this out without doing all possible interactions in vitro to work it out. He'll do this by creating models for all possible connections and then invalidate some of them.

This chemotaxis system is useful experimentally as you can measure the live output from cells using cell tethering. They constructed sensing models where the ligand is able to directly or indirectly interact with both MCPs and Tlps. There are a number of parameters that aren't known, and were estimated by fitting wild-type data. You can vary the experimental parameters by 10% you still get models that fit WT data pretty well.

The next part of the model is transduction. They experimentally determined the parameters. Finally is the motor binding step, where a simple mechanism for binding is assumed. So they have a set of models which can all represent WT data. They want to maximize the magnitude of the difference between the model outputs in order to discriminate "best" betwen the two models. This is achieved using linearized model equations around the steady state.

The differences between the models under the initial conditions were quite small. So they simulated these in silico to try to get large differences in expression. For example, overexpressing CheY4 has little effect on the WT, so only choose those models that behave similarly. Further tests are performed to get down to a final model. Of course, other un-modelled reactions may also be correct, so they're looking at extending the approach to find other possibilities.

Monday Session 2
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!

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