A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient

<p>Abstract</p> <p>Background</p> <p>An indirect approach is usually used to estimate the metabolic fluxes of an organism: couple the available measurements with known biological constraints (e.g. stoichiometry). Typically this estimation is done under a static point of view. Therefore, the fluxes so obtained are only valid while the environmental conditions and the cell state remain stable. However, estimating the evolution over time of the metabolic fluxes is valuable to investigate the dynamic behaviour of an organism and also to monitor industrial processes. Although Metabolic Flux Analysis can be successively applied with this aim, this approach has two drawbacks: i) sometimes it cannot be used because there is a lack of measurable fluxes, and ii) the uncertainty of experimental measurements cannot be considered. The Flux Balance Analysis could be used instead, but the assumption of optimal behaviour of the organism brings other difficulties.</p> <p>Results</p> <p>We propose a procedure to estimate the evolution of the metabolic fluxes that is structured as follows: 1) measure the concentrations of extracellular species and biomass, 2) convert this data to measured fluxes and 3) estimate the non-measured fluxes using the Flux Spectrum Approach, a variant of Metabolic Flux Analysis that overcomes the difficulties mentioned above without assuming optimal behaviour. We apply the procedure to a real problem taken from the literature: estimate the metabolic fluxes during a cultivation of CHO cells in batch mode. We show that it provides a reliable and rich estimation of the non-measured fluxes, thanks to considering measurements uncertainty and reversibility constraints. We also demonstrate that this procedure can estimate the non-measured fluxes even when there is a lack of measurable species. In addition, it offers a new method to deal with inconsistency.</p> <p>Conclusion</p> <p>This work introduces a procedure to estimate time-varying metabolic fluxes that copes with the insufficiency of measured species and with its intrinsic uncertainty. The procedure can be used as an off-line analysis of previously collected data, providing an insight into the dynamic behaviour of the organism. It can be also profitable to the on-line monitoring of a running process, mitigating the traditional lack of reliable on-line sensors in industrial environments.</p

Cytosolic NADPH balancing in Penicillium chrysogenum cultivated on mixtures of glucose and ethanol

The in vivo flux through the oxidative branch of the pentose phosphate pathway (oxPPP) in Penicillium chrysogenum was determined during growth in glucose/ethanol carbon-limited chemostat cultures, at the same growth rate. Non-stationary 13C flux analysis was used to measure the oxPPP flux. A nearly constant oxPPP flux was found for all glucose/ethanol ratios studied. This indicates that the cytosolic NADPH supply is independent of the amount of assimilated ethanol. The cofactor assignment in the model of van Gulik et al. (Biotechnol Bioeng 68(6):602–618, 2000) was supported using the published genome annotation of P. chrysogenum. Metabolic flux analysis showed that NADPH requirements in the cytosol remain nearly the same in these experiments due to constant biomass growth. Based on the cytosolic NADPH balance, it is known that the cytosolic aldehyde dehydrogenase in P. chrysogenum is NAD +  dependent. Metabolic modeling shows that changing the NAD + -aldehyde dehydrogenase to NADP + -aldehyde dehydrogenase can increase the penicillin yield on substrate

Inferring Orthology and Paralogy.

The distinction between orthologs and paralogs, genes that started diverging by speciation versus duplication, is relevant in a wide range of contexts, most notably phylogenetic tree inference and protein function annotation. In this chapter, we provide an overview of the methods used to infer orthology and paralogy. We survey both graph-based approaches (and their various grouping strategies) and tree-based approaches, which solve the more general problem of gene/species tree reconciliation. We discuss conceptual differences among the various orthology inference methods and databases and examine the difficult issue of verifying and benchmarking orthology predictions. Finally, we review typical applications of orthologous genes, groups, and reconciled trees and conclude with thoughts on future methodological developments