Using a randomization scheme for

the initial algorithmic settings, we generated 100 sets of dynamic adjustments in enzyme activities that led to metabolite concentration trends consistent with observations. The overall result thus consisted of a band for each enzyme activity, within which about 90% of all solutions laid, as well as the average trend in each enzyme activity (Figure 5). Details of this analysis will be shown elsewhere. Figure 5 Examples of three classes of heat-induced changes in enzyme activities within sphingolipid metabolism. Heat stress causes the activities of: phosphoserine phosphatase to increase (a); diacylglycerol Inhibitors,research,lifescience,medical (DAG) ethanolamine phosphotransferase to decrease ( … The results are quite intriguing Inhibitors,research,lifescience,medical in detail, because they reveal the balance of three selleck compound forces acting, on the enzymes, induced by heat: Increased activity according to an enzyme’s Q10 value, as alluded to in Equation (2); diminished activity due to partial protein unfolding, an altered

half-life of the corresponding protein and/or mRNA, and/or a reduced production; and change in enzyme activity due to gene expression. As an example for the first category, the activity of phosphoserine phosphatase increases about three-fold and remains at this activity level for at least 30 min (Figure 5a). An example of the second category is diacylglycerol Inhibitors,research,lifescience,medical (DAG) ethanolamine phosphotransferase, whose activity

Inhibitors,research,lifescience,medical was inferred to decrease, after a brief initial increase according to its Q10 (Figure 5b). Sphingoid-1-phosphate phosphatase falls into the third category (Figure 5c). Initially its activity drops quickly, but after about 25 min not only recovers but increases well over its baseline activity. Of note is that these results were extracted from the concentration time series data and the dynamic model strictly by computational means and without additional information. 4. Conclusions In the Inhibitors,research,lifescience,medical past, the effects of heat stress adaptation in the central carbon metabolism of yeast cells have been modeled by forward approaches, that is, by constructing models from their components and subsequently assessing the effects of heat. Several of these studies were ultimately based on a steady-state metabolic model of glycolysis published by Curto et al. [59]. After extensions and adjustments, these models were subjected see more to what-if simulations and to validation tests of the consistency between model predictions and known information about the physiology of heat stress adaptation. An example of this strategy is [48]. The Sorribas group [45,46,47,52] improved on these early studies by developing rigorous optimization methods to explore the space of reasonable combinations of gene expression profiles and study the feasibility of each profile according to a priori established criteria.