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.

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.

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.

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.

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.

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.

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.

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).

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.