In contrast to previous publications the parameter fitting procedure was modified: first the lumped parameters were estimated via nonlinear regression, finally all parameters were adjusted based on the estimation and literature data. Results from NCA allow to determine the influence of transcription factor activities on a set of selected genes. Data that were used in a different study were complemented with new experiments. In this experiment, glucose was pulsed to a culture growing under glucose limitation. Glucose was immediately taken up and after 10 h glucose was depleted. Acetate is produced during #selleck screening library keyword# growth on glucose and consumed after 15 h. The different energy sources lead
to different transcription factor activities that could be estimated with NCA. Furthermore, the influence of each transcription factor on each gene is described with a coupling factor κ. A crucial issue is the verification of the elements of the coupling matrix. In most studies—also in the first publication that Inhibitors,research,lifescience,medical introduces the method—the signs of the
entries were not validated with entries of databases. In our previous study [3] we already could show that an agreement for all entries is hardly possible but shows 70%–100% correct values. In the current study the error for transcription factor FruR is around 10%, that is, only one sign, here for the icd gene (isocitrate dehydrogenase in the TCA) is different Inhibitors,research,lifescience,medical Inhibitors,research,lifescience,medical from the data base entry. The values for pfkA, eno, gap, and pyk are determined from the experiments and are taken into account in further parts of the study. Interestingly the values for eno and gap are similar and are integrated into a single value for the lumped glycolytic reaction rgly. A structural analysis of the core model including all regulatory features was performed to calculate the behaviour of the intracellular metabolites of the core model (glucose-6-phosphate, fructose-1,6-bisphosphate, PEP and pyruvate). While the signs for fructose-1,6-bisphosphate and pyruvate are fixed
and show positive values, it is Inhibitors,research,lifescience,medical expected that both metabolites show increasing values if the uptake rate is increasing. In contrast, the signs of PEP and glucose-6-phosphate are not fixed. Since PEP is an important metabolite for the PTS and the PEP/pyruvate ratio determines the degree of phosphorylation, Histone demethylase the behavior of PEP in dependence on the growth rate was further explored. In a previous study, we analyzed the robustness of a simplified version of the model and it turns out that a monotonous decreasing course of PEP is more favourable with respect to robustness [11]. In this study, conditions for the extended model were derived allowing the course of PEP over the growth rate to show a maximum. These constraints are related to the regulatory properties on the transcriptional level (κ2 and κ3) and kinetic properties (α, β, K20).