1. Chemotaxis: Another go Chrisantha Fernando Systems Biology Centre Birmingham University Chrisantha Fernando Systems Biology Centre Birmingham University

2. = Active Tar = Methyl group = Inactive Tar TUMBLE Now add Chemoattractant RUN CheY-P CheY CheB-P CheB Motor CheA

3. Tumbling via CheY CheA-P RmRm R

4. RmRm R CheBPCheB S CheAP CheA

5. RmRm R CheBPCheB S CheAP CheA Use MM kinetics to describe each of the enzyme reactions i.e.

6. RmRm R CheBPCheB S CheAP CheA

7. Initial values Parameters Methylation and De-methylation is ‘Saturated’ [R] Rate of reaction per unit CheBP concentration

8. [S] 0.0001 0.001 0.01 0.1 [R m ] = Methylated Receptor [CheA-P] ≈ tumbling frequency [CheB-P]

9. 0.001 0.01 0.1 1.0 [R m ] = Methylated Receptor The limit of perfect adaptation occurs when new R m can no longer be produced

10. [S] 0.0001 0.001 0.01 0.1 Non-saturated methylation and demethylation No-perfect adaptation.

11. The First (wrong) Model Available at… http://www.pdn.cam.ac.uk/groups/comp-cell/Publications.html

12. Moving on…  We can go through points that were confusing again…  It is important you understand the principles of how to model these systems..  Mass action kinetics  MM kinetics  Inhibition (competitive and non-competitive)  Saturation of enzymes  We can go through points that were confusing again…  It is important you understand the principles of how to model these systems..  Mass action kinetics  MM kinetics  Inhibition (competitive and non-competitive)  Saturation of enzymes

13. Stochastic Modeling  So far we have been doing deterministic modeling.  Stochastic models consider individual molecules, undergoing discrete reaction events.  These models diverge when particle numbers are low.  By the end of this course you will be able to model both using ODEs and stochastic modeling, all the circuits I’ve talked about previously, and more. For now, familiarize yourself with bionetS.  So far we have been doing deterministic modeling.  Stochastic models consider individual molecules, undergoing discrete reaction events.  These models diverge when particle numbers are low.  By the end of this course you will be able to model both using ODEs and stochastic modeling, all the circuits I’ve talked about previously, and more. For now, familiarize yourself with bionetS.

14. BioNetS Easy to use

15. Here is a paper written using the tool…

16. Lets start with some simple chemical networks…

17. CheZ RCheZ R Rm Example of a Saturated Enzyme (CheZ) acting to methylate R