Abstract
Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.
Highlights
Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms
The recent model of E. histolytica glycolysis applies a knowledge-based method called metabolic network to each part of the pathway: the first part from glucose to dihydroxyacetone phosphate and the second part (Fig. 1) from 3-phosphoglycerate (3PG) to pyruvate (Pyr)[8]. These studies found that 3-phoshoglycerate mutase (PGAM) was the main controlling factor in the second part of glycolysis, whereas pyruvate phosphate dikinase (PPDK) exerted the lowest flux control
This result comes in conflict with previous r esearch[6], which identified PGAM and PPDK as important flux control steps of amoebal glycolysis
Summary
Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. The recent model of E. histolytica glycolysis applies a knowledge-based method called metabolic network to each part of the pathway: the first part from glucose to dihydroxyacetone phosphate and the second part (Fig. 1) from 3-phosphoglycerate (3PG) to pyruvate (Pyr)[8]. These studies found that 3-phoshoglycerate mutase (PGAM) was the main controlling factor in the second part of glycolysis, whereas pyruvate phosphate dikinase (PPDK) exerted the lowest flux control. Here our study is based on the experimental results of Moreno-Sanchez[8]
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