Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

Multiobjective optimization requires of finding a trade-off between multiple objectives. However, most of the objectives are contradict towards each other, thus makes it difficult for the traditional approaches to find a solution that satisfies all objectives. Fortunately, the problems are able to solve by the aid of Pareto methods. Meanwhile, in in silico Metabolic Engineering, the identification of reaction knockout strategies that produce mutant strains with a permissible growth rate and product rate of desired metabolites is still hindered. Previously, Evolutionary Algorithms (EAs) has been successfully used in determining the reaction knockout strategies. Nevertheless, most methods work by optimizing one objective function, which is growth rate or production rate. Furthermore, in bioprocesses, it involves multiple and conflicting objectives. In this review, we aim to show the different multiobjective evolutionary optimization methods developed for tackling the multiple and conflicting objectives in in silico metabolic engineering, as well as the approaches in multiobjective optimization

Procyclic trypanosomes recycle glucose catabolites and TCA cycle intermediates to stimulate growth in the presence of physiological amounts of proline

Trypanosoma brucei, a protist responsible for human African trypanosomiasis (sleeping sickness), is transmitted by the tsetse fly where the procyclic forms of the parasite develop in the proline-rich (1–2 mM) and glucose-depleted digestive tract. Proline is essential for the midgut colonization of the parasite in the insect vector, however other carbon sources could be available and used to feed its central metabolism. Here we show that procyclic trypanosomes can consume and metabolize metabolic intermediates, including those excreted from glucose catabolism (succinate, alanine and pyruvate), with the exception of acetate, which is the ultimate end-product excreted by the parasite. Among the tested metabolites, tricarboxylic acid (TCA) cycle intermediates (succinate, malate and α-ketoglutarate) stimulated growth of the parasite in the presence of 2 mM proline. The pathways used for their metabolism were mapped by proton-NMR metabolic profiling and phenotypic analyses of thirteen RNAi and/or null mutants affecting central carbon metabolism. We showed that (i) malate is converted to succinate by both the reducing and oxidative branches of the TCA cycle, which demonstrates that procyclic trypanosomes can use the full TCA cycle, (ii) the enormous rate of α-ketoglutarate consumption (15-times higher than glucose) is possible thanks to the balanced production and consumption of NADH at the substrate level and (iii) α-ketoglutarate is toxic for trypanosomes if not appropriately metabolized as observed for an α-ketoglutarate dehydrogenase null mutant. In addition, epimastigotes produced from procyclics upon overexpression of RBP6 showed a growth defect in the presence of 2 mM proline, which is rescued by α-ketoglutarate, suggesting that physiological amounts of proline are not sufficient per se for the development of trypanosomes in the fly. In conclusion, these data show that trypanosomes can metabolize multiple metabolites, in addition to proline, which allows them to confront challenging environments in the fly

Improved differential search algorithms for metabolic network optimization

The capabilities of Escherichia coli and Zymomonas mobilis to efficiently converting substrate into valuable metabolites have caught the attention of many industries. However, the production rates of these metabolites are still below the maximum threshold. Over the years, the organism strain design was improvised through the development of metabolic network that eases the process of exploiting and manipulating organism to maximize its growth rate and to maximize metabolites production. Due to the complexity of metabolic networks and multiple objectives, it is difficult to identify near-optimal knockout reactions that can maximize both objectives. This research has developed two improved modelling-optimization methods. The first method introduces a Differential Search Algorithm and Flux Balance Analysis (DSAFBA) to identify knockout reactions that maximize the production rate of desired metabolites. The latter method develops a non-dominated searching DSAFBA (ndsDSAFBA) to investigate the trade-off relationship between production rate and its growth rate by identifying knockout reactions that maximize both objectives. These methods were assessed against three metabolic networks – E.coli core model, iAF1260 and iEM439 for production of succinic acid, acetic acid and ethanol. The results revealed that the improved methods are superior to the other state-of-the-art methods in terms of production rate, growth rate and computation time. The study has demonstrated that the two improved modelling-optimization methods could be used to identify near-optimal knockout reactions that maximize production of desired metabolites as well as the organism’s growth rate within a shorter computation time

Développement de méthodes bioinformatiques dédiées à la prédiction et l'analyse des réseaux métaboliques et des ARN non codants

L'identification des interactions survenant au niveau moléculaire joue un rôle crucial pour la compréhension du vivant. L'objectif de ce travail a consisté à développer des méthodes permettant de modéliser et de prédire ces interactions pour le métabolisme et la régulation de la transcription. Nous nous sommes basés pour cela sur la modélisation de ces systèmes sous la forme de graphes et d'automates. Nous avons dans un premier temps développé une méthode permettant de tester et de prédire la distribution du flux au sein d'un réseau métabolique en permettant la formulation d'une à plusieurs contraintes. Nous montrons que la prise en compte des données biologiques par cette méthode permet de mieux reproduire certains phénotypes observés in vivo pour notre modèle d'étude du métabolisme énergétique du parasite Trypanosoma brucei. Les résultats obtenus ont ainsi permis de fournir des éléments d'explication pour comprendre la flexibilité du flux de ce métabolisme, qui étaient cohérentes avec les données expérimentales. Dans un second temps, nous nous sommes intéressés à une catégorie particulière d'ARN non codants appelés sRNAs, qui sont impliqués dans la régulation de la réponse cellulaire aux variations environnementales. Nous avons développé une approche permettant de mieux prédire les interactions qu'ils effectuent avec d'autres ARN en nous basant sur une prédiction des interactions, une analyse par enrichissement du contexte biologique de ces cibles, et en développant un système de visualisation spécialement adapté à la manipulation de ces données. Nous avons appliqué notre méthode pour l'étude des sRNAs de la bactérie Escherichia coli. Les prédictions réalisées sont apparues être en accord avec les données expérimentales disponibles, et ont permis de proposer plusieurs nouvelles cibles candidates.The identification of the interactions occurring at the molecular level is crucial to understand the life process. The aim of this work was to develop methods to model and to predict these interactions for the metabolism and the regulation of transcription. We modeled these systems by graphs and automata.Firstly, we developed a method to test and to predict the flux distribution in a metabolic network, which consider the formulation of several constraints. We showed that this method can better mimic the in vivo phenotype of the energy metabolism of the parasite Trypanosoma brucei. The results enabled to provide a good explanation of the metabolic flux flexibility, which were consistent with the experimental data. Secondly, we have considered a particular class of non-coding RNAs called sRNAs, which are involved in the regulation of the cellular response to environmental changes. We developed an approach to better predict their interactions with other RNAs based on the interaction prediction, an enrichment analysis, and by developing a visualization system adapted to the manipulation of these data. We applied our method to the study of the sRNAs interactions within the bacteria Escherichia coli. The predictions were in agreement with the available experimental data, and helped to propose several new target candidates.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Étude de la variabilité des contributions de nutriments à un réseau métabolique (modélisation, optimisation et application en nutrition)

Nous développons une approche générique pour comprendre comment différents régimes alimentaires peuvent influencer la qualité et la composition du lait. Cette question s'intègre dans le cadre du Flux Balance Analysis (FBA), qui consiste à analyser un réseau métabolique en optimisant un système de contraintes linéaires. Nous avons proposé une extension du FBA pour analyser la transformation des nutriments en intégrant des hypothèses biologiques utilisées par différents modèles numériques dans un modèle générique de la glande mammaire. Notre méthode permet de quantifier les précurseurs qui interviennent dans la composition des sorties du système, en calculant des contributions des entrées dans les sorties [AIO]. A l'aide de cette approche, nous avons montré que la transformation des nutriments du lait ne peut pas être modélisée par l'optimisation d'une combinaison linéaire des flux des réactions sur un modèle du métabolisme mammaire. Pour étudier plus précisément la flexibilité d'un réseau métabolique, nous avons proposé un algorithme efficace de recherche locale pour calculer les valeurs extrémales des coefficients des AIOs. Cette approche permet de discriminer les traitements sans formuler d'hypothèses sur le comportement interne du système.This thesis proposes a generic approach to understanding how different diets affect the quality and composition of milk. This question is addressed in the framework of Flux Balance Analysis (FBA), which considers metabolic network analysis as an optimization issue on a system of linear constraints. In this work, we extended FBA to take into account nutrients transformation by incorporating general assumptions made by various numerical methods in a generic stoichiometric model of the mammary gland. Our method tries to quantify the precursor composition of each system output and to discuss the biological relevance of a set of flux in a given metabolic network. The composition is called contribution of inputs over outputs [AIO]. Using this method on the mammary metabolism, we could show that nutrients transformation cannot be properly modelled by optimizing a linear combination of reactions fluxes in the mammary gland model. In order to further investigate metabolic network flexibility, we have proposed an efficient local search algorithm computing the extremal values of AIO coefficients. This approach enables to discriminate diets without making any assumption on the internal behaviour of the system.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Flux Analysis of the Trypanosoma brucei Glycolysis Based on a Multiobjective-Criteria Bioinformatic Approach

Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function. Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps