Background
Bacteria are capable of moving through their environment. Bacterial chemotaxis is the biasing of this movement towards regions of higher concentration of beneficial or lower concentration of toxic chemicals. In bacteria such as E. coli and R. sphaeroides, this is achieved when chemical ligands bound to membrane-spanning receptors initiate a signalling cascade of intracellular protein activity leading to the change in activity of the flagellar motor, which drives the extracellular flagellum (or flagella), causing the bacterium to change the direction in which it moves. Chemotaxis in E. coli is one of the best understood pathways in biology and there is a large amount of experimental data on structures, kinetics, in vivo protein concentrations and localisation. This relatively simple pathway has helped to conceptualize the signalling pathway of general sensory systems. However, with an increasing number of sequenced bacterial genomes it becomes evident that the chemotactic sensory mechanism of other bacteria is much more complex.
Objective
The aim of our research project is to apply results from control theory to develop novel approaches for designing experiments in order to elucidate the biochemical network structure of the chemotaxis mechanism in R. sphaeroides, which has multiple homologues of the E. coli proteins and hence is significantly more complex. The goal is to develop a systematic approach for finding the best experiment that will delineate the network structure.
Methodology & Results
To achieve this, we are constructing, in silico, various possible models of R. sphaeroides chemotaxis based on the current experimental evidence and gene homology that can explain current experimental observations. Applying results from optimal control theory, we determined the best input (ligand) profile that gives an output which would allow us to discriminate best between the proposed models, aiming to invalidate some of them. This input ligand profile is then administered to R. sphaeroides in a flow cell and the response is measured using a tethered cell assay.
We have also developed methods to determine the best initial conditions to discriminate between the models, based on the limitations of what can be implemented biochemically. These were then also tested in live cells.
We used the experimental results to invalidate some of the proposed network structures and hence determine the network connectivity. The final network topology will be confirmed using biochemical measurements (SPR / 2 Hybrid etc) allowing us to validate our approach.