CNApy: a CellNetAnalyzer GUI in Python for analyzing and designing metabolic networks.

SUMMARY: Constraint-based reconstruction and analysis (COBRA) is a widely used modeling framework for analyzing and designing metabolic networks. Here, we present CNApy, an open-source cross-platform desktop application written in Python, which offers a state-of-the-art graphical front-end for the intuitive analysis of metabolic networks with COBRA methods. While the basic look-and-feel of CNApy is similar to the user interface of the MATLAB toolbox CellNetAnalyzer, it provides various enhanced features by using components of the powerful Qt library. CNApy supports a number of standard and advanced COBRA techniques and further functionalities can be easily embedded in its GUI facilitating modular extension in the future. AVAILABILITY AND IMPLEMENTATION: CNApy can be installed via conda and its source code is freely available at https://github.com/cnapy-org/CNApy under the Apache 2 license.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies.

BACKGROUND: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions. RESULTS: We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements. CONCLUSION: Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks.

The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond.

Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM (http://ectogem.irisa.fr). This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

MOTIVATION: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. RESULTS: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. AVAILABILITY: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online. CONTACT: santiago.videla@irisa.fr.

Borane and borohydride complexes of the rare-earth elements: synthesis, structures, and butadiene polymerization catalysis.

The reaction of potassium 2,5-bis[N-(2,6-diisopropylphenyl)iminomethyl]pyrrolyl [(dip(2)-pyr)K] with the borohydrides of the larger rare-earth metals, [Ln(BH(4))(3)(thf)(3)] (Ln=La, Nd), afforded the expected products [Ln(BH(4))(2)(dip(2)-pyr)(thf)(2)]. As usual, the trisborohydrides reacted like pseudohalide compounds forming KBH(4) as a by-product. To compare the reactivity with the analogous halides, the dimeric neodymium complex [NdCl(2)(dip(2)-pyr)(thf)](2) was prepared by reaction of [(dip(2)-pyr)K] with anhydrous NdCl(3). Reaction of [(dip(2)-pyr)K] with the borohydrides of the smaller rare-earth metals, [Sc(BH(4))(3)(thf)(2)] and [Lu(BH(4))(3)(thf)(3)], resulted in a redox reaction of the BH(4) (-) group with one of the Schiff base functions of the ligand. In the resulting products, [Ln(BH(4)){(dip)(dip-BH(3))-pyr}(thf)(2)] (Ln=Sc, Lu), a dinegatively charged ligand with a new amido function, a Schiff base, and the pyrrolyl function is bound to the metal atom. The by-product of the reaction of the BH(4) (-) anion with the Schiff base function (a BH(3) molecule) is trapped in a unique reaction mode in the coordination sphere of the metal complex. The BH(3) molecule coordinates in an eta(2) fashion to the metal atom. The rare-earth-metal atoms are surrounded by the eta(2)-coordinated BH(3) molecule, the eta(3)-coordinated BH(4) (-) anion, two THF molecules, and the nitrogen atoms from the Schiff base and the pyrrolyl function. All new compounds were characterized by single-crystal X-ray diffraction. Low-temperature X-ray diffraction data at 6 K were collected to locate the hydrogen atoms of [Lu(BH(4)){(dip)(dip-BH(3))-pyr}(thf)(2)]. The (DIP(2)-pyr)(-) borohydride and chloride complexes of neodymium, [Nd(BH(4))(2)(dip(2)-pyr)(thf)(2)] and [NdCl(2)(dip(2)-pyr)(thf)](2), were also used as Ziegler-Natta catalysts for the polymerization of 1,3-butadiene to yield poly(cis-1,4-butadiene). Very high activities and good cis selectivities were observed by using each of these complexes as a catalyst in the presence of various cocatalyst mixtures.

Bis(amido)cyclodiphosph(III)azane complexes of yttrium and the lanthanides.

The first cyclodiphosph(III)azane complexes of the rare-earth elements have been synthesized. Reactions of the lithium salt cis-[(tBuNP)(2)(tBuN)(2){Li(thf)}(2)] with anhydrous yttrium trichloride or the heavier lanthanide trichlorides resulted in the corresponding cyclodiphosph(III)azane complexes [Li(thf)(4)][{(tBuNP)(2)(tBuN)(2)}LnCl(2)] (Ln=Y (1 a), Ho (1 b), Er (1 c)). The single-crystal X-ray structures showed that compounds 1 a-c consisted of ion pairs composed of a [Li(thf)(4)](+) cation and a C(2v) symmetric [{(tBuNP)(2)(tBuN)(2)}LnCl(2)](-) anion. By treating cis-[(tBuNP)(2)(tBuN)(2){Li(thf)}(2)] with anhydrous SmCl(3) in THF, the trimetallic complex [{(tBuNP)(2)(tBuN)(2)}SmCl(3)Li(2)(thf)(4)] (2) was obtained. The influence of the ionic radii of the lanthanides can be seen in the single-crystal X-ray structure of compound 2, which forms a six-membered Cl-Li-Cl-Li-Cl-Sm metallacycle. The ring adopts a boat conformation in which one chlorine atom and the samarium atom are displaced from the Cl(2)Li(2) least-square plane. Heating of the metalate complexes in toluene resulted in the extrusion of lithium chloride and the formation of the neutral dimeric metal chloride complexes of the composition [(tBuNP)(2)(tBuN)(2)LnCl(thf)](2) (Ln=Y (3 a), La (3 b) Nd (3 c), Sm (3 d)). Furthermore, treating 1 a with KNPh(2) resulted in a lithium metalate complex of the composition [Li(thf)(4)][{(tBuNP)(2)(tBuN)(2)}Y(NPh(2))(2)] (4). The coordination mode of the {(tBuNP)(2)(tBuN)(2)}(2-) ligand in 4 is different to that observed in 1 a-c, 2, and 3 a-d; instead of a symmetric eta(2) coordination of the ligand, a heterocubane-type structure is observed in the solid state. The complex [(tBuNP)(2)(tBuN)(2)NdCl(thf)] (3 c) was used as a Ziegler-Natta catalyst for the polymerization of 1,3-butadiene to poly-cis-1,4-butadiene. The observed activities of the Ziegler-Natta catalyst strongly depended upon the nature of the cocatalyst; in some case very high turnover rates and a cis selectivity of 93-94 % were observed.

Designing optimal experiments to discriminate interaction graph models.

Modern methods for the inference of cellular networks from experimental data often express nondeterminism through an ensemble of candidate models. To discriminate among these candidates new experiments need to be carried out. Theoretically, the number of possible experiments is exponential in the number of possible perturbations. In praxis, experiments are expensive and there exist several limiting constraints. Limiting factors exist on the combinations of perturbations that are technically possible, which components can be measured, and on the number of affordable experiments. Further, not all experiments are equally well suited to discriminate model candidates. The goal of optimal experiment design is to determine those experiments that discriminate most of the candidates while minimizing the costs. We present an approach for experiment planning with interaction graph models and sign consistency methods. This new approach can be used in combination with methods for network inference and consistency checking. We applied our method to study the Erythropoietin signal transduction in human kidney cells HEK293. We first used simulated experiment data from an ODE model to demonstrate in silico that our experimental design results in the inference of the gold standard model. Finally, we used the approach to plan in vivo experiments that discriminate model candidates for the Erythropoietin signal transduction in this cell line.

Use of CellNetAnalyzer in biotechnology and metabolic engineering.

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer, a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design.

Putative bacterial interactions from metagenomic knowledge with an integrative systems ecology approach.

Following the trend of studies that investigate microbial ecosystems using different metagenomic techniques, we propose a new integrative systems ecology approach that aims to decipher functional roles within a consortium through the integration of genomic and metabolic knowledge at genome scale. For the sake of application, using public genomes of five bacterial strains involved in copper bioleaching: Acidiphilium cryptum, Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans, we first reconstructed a global metabolic network. Next, using a parsimony assumption, we deciphered sets of genes, called Sets from Genome Segments (SGS), that (1) are close on their respective genomes, (2) take an active part in metabolic pathways and (3) whose associated metabolic reactions are also closely connected within metabolic networks. Overall, this SGS paradigm depicts genomic functional units that emphasize respective roles of bacterial strains to catalyze metabolic pathways and environmental processes. Our analysis suggested that only few functional metabolic genes are horizontally transferred within the consortium and that no single bacterial strain can accomplish by itself the whole copper bioleaching. The use of SGS pinpoints a functional compartmentalization among the investigated species and exhibits putative bacterial interactions necessary for promoting these pathways.