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Using molecular genetics we have introduced uncoupled ATPase activity in two different bacterial species, Escherichia coli and Lactococcus lactis, and determined the elasticities of the growth rate and glycolytic flux towards the intracellular [ATP]/[ADP] ratio. During balanced growth in batch cultures of E. coli the ATP demand was found to have almost full control on the glycolytic flux (FCC=0.96) and the flux could be stimulated by 70%. In contrast to this, in L. lactis the control by ATP demand on the glycolytic flux was close to zero. However, when we used non-growing cells of L. lactis (which have a low glycolytic flux) the ATP demand had a high flux control and the flux could be stimulated more than two fold. We suggest that the extent to which ATP demand controls the glycolytic flux depends on how much excess capacity of glycolysis is present in the cells.

Experimental supply-demand analysis of yeast fermentative energy metabolism shows that control of the glycolytic flux is shared between supply and demand. In glucose limited chemostat cultures the supply block was modulated in a dilution rate change and demand block via a benzoic acid titration. Under these conditions the supply block had a flux control of 0.90 and the demand block a flux control of 0.10.

The genes of E. coli are located on a circular chromosome of 4.6 million basepairs. This 1.6 mm long molecule is compressed into a nucleoid to fit inside the 1-2 microm cell in a functional format. To examine the role of DNA supercoiling as nucleoid compaction force we modulated the activity of DNA gyrase by electronic, genetic, and chemical means. A model based on physical properties of DNA and other cell components predicts that relaxation of supercoiling expands the nucleoid. Nucleoid size did not increase after reduction of DNA gyrase activity by genetic or chemical means, but nucleoids did expand upon chemical inhibition of gyrase in chloramphenicol-treated cells, indicating that supercoiling may help to compress the genome.

Glucose addition and subsequent run-out experiments were compared to simulations with a detailed glycolytic model of Lactococcus lactis. The model was constructed largely on bases of enzyme kinetic data taken from literature and not adjusted for the specific simulations shown here. Upon glucose depletion a rapid increase in PEP, inorganic phosphate and a gradual decrease in fructose 1,6-bisphosphate (FBP) were measured and predicted by simulation. The dynamic changes in these and other intermediate concentrations as measured in the experiments were well predicted by the kinetic model.

Software to make a database of kinetic models accessible via the internet has been developed and a core database has been set up at http://jjj.biochem.sun.ac.za/. This repository of models, available to everyone with internet access, opens a whole new way in which we can make our models public. Via the database, a user can change enzyme parameters and run time simulations or steady state analyses. The interface is user friendly and no additional software is necessary. The database currently contains 10 models, but since the generation of the program code to include new models has largely been automated the addition of new models is straightforward and people are invited to submit their models to be included in the database.

Although metabolic control analysis (MCA) cannot be applied directly to microbial ecological systems because of mass conservation and stoichiometric constraints, we demonstrate here that Hierarchical Control Analysis (HCA) can be applied to such systems. We illustrate the approach for a particular ecosystem example of the biological synthesis of acetic acid from glucose, and uncover some surprising aspects to the control of this miniature ecosystem.

A numerical model of the LmrA multi-drug transport system of Lactococcus lactis is used to explore the possibility of distinguishing experimentally between two putative transport mechanisms, i.e., the vacuum-cleaner and the flippase mechanisms. This comparative model also serves as an example of numerical simulation with the scripting language Python and its scientific add-on Scipy.