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Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.

Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 850

International audience; Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

5 pages; Optimization-based analysis of metabolic networks have been used to design metabolic engineering strategies aiming at enhancing the production of a target of interest. Here, we present MeDUSA, a comprehensive tool for the calculation of a stoichiometric capacitance allowing for increasing the maximum production of a given product. MeDUSA provides an extensive sage-based package which, given a metabolic network and an objective function, (i) computes a stoichiometric capacitance and (ii) investigates the effect of adding the calculated stoichiometric capacitance.

Background Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature. Results We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods. Conclusions We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.

Constraint-based approaches have proved successful in analyzing complex metabolic networks. They restrict the range of all possible behaviors that a metabolic system can display under governing constraints. The set of all possible flux distributions over a metabolic network at steady state defines a polyhedral cone, the steady-state flux cone. This cone can be analyzed using an inner description, based on sets of generating vectors such as elementary modes or extreme pathways. We present a new constraint-based approach to metabolic network analysis, characterizing a metabolic network by its minimal metabolic behaviors and the reversible metabolic space. Our method uses an outer description of the flux cone, based on sets of non-negativity constraints. The resulting description is minimal and unique. We then study the relationship between inner and outer descriptions of the cone. We give a generic procedure to show how inner descriptions can be computed from the outer one. We use this procedure to explain why the size of the inner descriptions may be several orders of magnitude larger than that of the outer description. Our approach suggests a refined classification of reactions according to their reversibility type (irreversible, pseudo-irreversible, and fully reversible). Using these concepts, we improve an existing algorithm for identifying blocked and coupled reactions and devise a new algorithm for flux coupling analysis. We extend this analysis by introducing minimal direction cuts (MDCs) which prevent a target reaction from being performed in an undesired direction. We develop an algorithm which allows not only for computing MDCs in a metabolic network, but also for a direct calculation of minimal cut sets (MCSs). Based on our refined classification of reactions, we also provide a constraint-based approach for analyzing the changes in the overall capabilities of a metabolic network following a gene deletion. Flux coupling and gene deletion analysis help for identifying important reactions in metabolic networks. Alternatively, the essentiality of reactions can be assessed using control-effective flux (CEF) analysis, which is based on elementary modes. We compare CEF analysis with the use of a minimal generating set of the flux cone to elucidate crucial reactions., In der Analyse metabolischer Netzwerke haben constraintbasierte Ansätze erfolgreiche Anwendung gefunden. Hierbei wird der Bereich des möglichen Verhaltens eines metabolischen Systems durch zusätzliche Anforderungen an das System eingeschränkt. Die resultierende Menge aller Flussverteilungen eines metabolischen Netzwerks im stationären Zustand hat die Gestalt eines polyedrischen Kegels, welcher Flusskegel genannt wird. Eine innere Beschreibung dieses Kegels basierend auf Mengen erzeugender Vektoren, wie etwa Elementarmodi oder Extremalpfade, ermöglicht eine effiziente Analyse. Wir haben einen neuen constraintbasierten Ansatz zur Analyse metabolischer Netzwerke entwickelt, in dem das System durch minimale metabolische Verhaltensmuster und den reversiblen metabolischen Raum charakterisiert wird. In unserer Methode kommt eine äußere Beschreibung des Flusskegels zur Anwendung, die wir durch Ausnutzung von Nicht-Negativitäts-Bedingungen erhalten. Diese Beschreibung ist minimal und eindeutig. Wir untersuchen die Beziehung zwischen innerer und äußerer Beschreibung des Kegels und stellen ein allgemeines Verfahren zur Herleitung der inneren aus der äußeren Beschreibung vor. Dieses Verfahren verdeutlicht, warum die äußere im Vergleich zur inneren Beschreibung eine meist sehr viel kompaktere, sogar bis zu mehreren Größenordungen kleinere Darstellung liefert. In unserem Ansatz verwenden wir eine verfeinerte Klassifikation von Reaktionen des metabolischen Netzwerks entsprechend ihres Reversibilitäts-Typus (irreversibel, pseudo-irreversibel und vollständig reversibel). Diese Einteilung ermöglicht uns eine deutliche Verbesserung existierender Algorithmen zur Bestimmung von blockierten und gekoppelten Reaktionen und die Formulierung eines neuen, effizienten Algorithmus für die Flusskopplungsanalyse. Die von uns eingeführten minimalen gerichteten Schnitte (MDCs), die die Ausführung einer Zielreaktion in eine ungewünschte Richtung verhindern, erweitern die klassische Flusskopplungsanalyse. Ein von uns entwickelter Algorithmus ermöglicht nicht nur die Berechnung von MDCs in einem metabolischen Netzwerk, sondern auch die direkte Ermittlung minimaler Schnittmengen (MCSs). Basierend auf unserer verfeinerten Klassifizierung von Reaktionen stellen wir schließlich einen constraintbasierten Ansatz zur Analyse der durch Gen-Ausfall ausgelösten Beeinträchtigungen globaler Fähigkeiten eines metabolischen Netzwerks vor. Flusskopplungs- und Gen-Ausfall-Analyse helfen bei der Identifikation essentieller Reaktionen im metabolischen System. Altenativ kann die Bedeutung von Reaktionen für die Netzwerkfunktionen mittels auf Elementarmodi basierender control-effective Fluss-Analyse (CEF) bewertet werden. Wir vergleichen CEF-Analyse mit der Verwendung eines minimalen Erzeugendensystems für die Bestimmung von Schlüsselreaktionen.

Discovering heterogeneous regulatory motifs remains a difficult problem in biological sequence analysis. In this context, statistical learning or pattern search techniques on their own have shown some limitations. However, significant benefits can be taken from their complementarity. We selected two state-of-the-art methods: a multi-class support vector machine (M-SVM) from the statistical learning domain associated with a performant discrete pattern matching algorithm grappe, and in- tegrated them into a web technology based graphical software: KOALAB (KOupled Algorithmic and Learning Approach for Biology)1 . We applied our method on motif discovery within nucleic acid sequences using experimental SELEX results as training database for the M-SVM. An application dealing with the search for splicing regulatory protein binding sites in HIV-1 genome shows the potential of such an approach.

Stage de DEA. Rapport de stage.; L'analyse des voies métaboliques détermine les voies caractéristiques permettant de décrire à elles seules toutes les distributions de flux possibles dans un réseau métabolique. Ces voies caractéristiques sont les rayons extrêmes d'un cône convexe représentant l'espace des flux qui vérifient les contraintes stoechiométriques du système. Diverses approches ont récemment été développées pour déterminer ces voies particulières. Cependant, leur recherche occasionne une explosion combinatoire dans le cas de systèmes biologiques réels. Pour pallier à ce problème, cette étude propose d'améliorer la formalisation du réseau métabolique. Cela permet de déterminer un ensemble minimale des voies métaboliques caractéristiques dites génériques. Ces dernières décrivent l'ensemble des comportements possibles d'un système vivant. Pour ce problème mieux formalisé, il est alors possible d'utiliser la méthode de la double description qui est plus performante que les algorithmes standards pour calculer les rayons extrêmes. || Metabolic pathways analysis determine the characteristic pathways which describes by themselves all the possible flux in a metabolic network. Those pathways correspond to the extrem rays of a convex cone which include all the flux verifying the stoichiome