Competitive, multi-objective, and compartmented Flux Balance Analysis for addressing tissue-specific inborn errors of metabolism.

The inborn error of metabolism phenylketonuria (PKU, OMIM 261600) is most often due to inactivation of phenylalanine hydroxylase (PAH), which converts phenylalanine (Phe) into tyrosine (Tyr). The reduced PAH activity increases blood concentration of phenylalanine and urine levels of phenylpyruvate. Flux balance analysis (FBA) of a single-compartment model of PKU predicts that maximum growth rate should be reduced unless Tyr is supplemented. However, the PKU phenotype is lack of development of brain function specifically, and Phe reduction rather than Tyr supplementation cures the disease. Phe and Tyr cross the blood-brain barrier (BBB) through the aromatic amino acid transporter implying that the two transport reactions interact. However, FBA does not accommodate such competitive interactions. We here report on an extension to FBA that enables it to deal with such interactions. We built a three-compartment model, made the common transport across the BBB explicit, and included dopamine and serotonin synthesis as parts of the brain function to be delivered by FBA. With these ramifications, FBA of the genome-scale metabolic model extended to three compartments does explain that (i) the disease is brain specific, (ii) phenylpyruvate in urine is a biomarker, (iii) excess of blood-phenylalanine rather than shortage of blood-tyrosine causes brain pathology, and (iv) Phe deprivation is the better therapy. The new approach also suggests (v) explanations for differences in pathology between individuals with the same PAH inactivation, and (vi) interference of disease and therapy with the functioning of other neurotransmitters.

Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis: insights into metabolic regulation.

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.