Large-scale computation of elementary flux modes with bit pattern trees

Motivation: Elementary flux modes (EFMs)—non-decomposable minimal pathways—are commonly accepted tools for metabolic network analysis under steady state conditions. Valid states of the network are linear superpositions of elementary modes shaping a polyhedral cone (the flux cone), which is a well-studied convex set in computational geometry. Computing EFMs is thus basically equivalent to extreme ray enumeration of polyhedral cones. This is a combinatorial problem with poorly scaling algorithms, preventing the large-scale analysis of metabolic networks so far. Results: Here, we introduce new algorithmic concepts that enable large-scale computation of EFMs. Distinguishing extreme rays from normal (composite) vectors is one critical aspect of the algorithm. We present a new recursive enumeration strategy with bit pattern trees for adjacent rays—the ancestors of extreme rays—that is roughly one order of magnitude faster than previous methods. Additionally, we introduce a rank updating method that is particularly well suited for parallel computation and a residue arithmetic method for matrix rank computations, which circumvents potential numerical instability problems. Multi-core architectures of modern CPUs can be exploited for further performance improvements. The methods are applied to a central metabolism network of Escherichia coli, resulting in ≈26 Mio. EFMs. Within the top 2% modes considering biomass production, most of the gain in flux variability is achieved. In addition, we compute ≈5 Mio. EFMs for the production of non-essential amino acids for a genome-scale metabolic network of Helicobacter pylori. Only large-scale EFM analysis reveals the >85% of modes that generate several amino acids simultaneously. Availability: An implementation in Java, with integration into MATLAB and support of various input formats, including SBML, is available at http://www.csb.ethz.ch in the tools section; sources are available from the authors upon request. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Predicting sinusoidal obstruction syndrome after allogeneic stem cell transplantation with the EASIX biomarker panel

No biomarker panel is established for prediction of sinusoidal obstruction syndrome/veno-occlusive disease (SOS/VOD), a major complication of allogeneic stem cell transplantation (alloSCT). We compared the potential of the Endothelial Activation and Stress Index (EASIX), based on lactate dehydrogenase, creatinine, and thrombocytes, with that of the SOS/VOD CIBMTR clinical risk score to predict SOS/VOD in two independent cohorts. In a third cohort, we studied the impact of endothelium-active prophylaxis with pravastatin and ursodeoxycholic acid (UDA) on SOS/VOD risk. The cumulative incidence of SOS/VOD within 28 days after alloSCT in the training cohort (Berlin, 2013-2015, n=446) and in the validation cohort (Heidelberg, 2002-2009, n=380) was 9.6% and 8.4%, respectively. In both cohorts, EASIX assessed at the day of alloSCT (EASIX-d0) was significantly associated with SOS/VOD incidence (p<0.0001), overall survival (OS) and non-relapse mortality (NRM). In contrast, the CIBMTR score showed no statistically significant association with SOS/VOD incidence, and did not predict OS and NRM. In patients receiving pravastatin/UDA, the cumulative incidence of SOS/VOD was significantly lower at 1.7% (p<0.0001, Heidelberg, 2010-2015, n=359) than in the two cohorts not receiving pravastatin/UDA. The protective effect was most pronounced in patients with high EASIX-d0. The cumulative SOS/VOD incidence in the highest EASIX-d0 quartiles were 18.1% and 16.8% in both cohorts without endothelial prophylaxis as compared to 2.2% in patients with pravastatin/UDA prophylaxis (p<0.0001). EASIX-d0 is the first validated biomarker for defining a subpopulation of alloSCT recipients at high risk for SOS/VOD. Statin/UDA endothelial prophylaxis could constitute a prophylactic measure for patients at increased SOS/VOD risk

Naturwissenschaftliche Erkenntnisgewinnung durch Modelle – Modellverständnis als Grundlage für Modellkompetenz

Das Modellverständnis als Element von Modellkompetenz wird durch folgende Aspekte strukturiert: die Definition des Begriffs „Modell“, Kriterien für Modelle, Zweck von Modellen, Veränderbarkeit von Modellen und multiple Modelle. Schüleraussagen zu diesen Kriterien werden in Anlehnung an CAREY et al. (1989), DRIVER et al. (1996) und GÜNTHER et al. (2004) qualitativ drei Levels zugeordnet. 70 Schülern der neunten Jahrgangsstufe an zwei Berliner Gymnasien wurden im Biologieunterricht offene Fragen zu ihrem Modellverständnis zur schriftlichen Bearbeitung vorgelegt. Die Antworten wurden nach der qualitativen Inhaltsanalyse (MAYRING 2003) ausgewertet. Ein Schwerpunkt in den erfassten Schülervorstellungen sind die deskriptiven Aspekte von Modellen, das heißt sie werden vorwiegend in ihrer Anschauungsfunktion wahrgenommen. Die Rolle von Modellen im wissenschaftlichen Erkenntnisprozess wurde in der Regel nicht erkannt. Darüber hinaus waren die Vorstellungen der Schüler bezogen auf die theoretischen Aspekte von Modellen häufi g inkonsistent. Im Rahmen der vorliegenden Untersuchung stellt sich demnach die Struktur des Modellverständnisses der Schüler als eher mosaikartig dar und weist auf ein kompartimentalisiertes Wissen über Modelle hin

Computing the shortest elementary flux modes in genome-scale metabolic networks

This article is available open access through the publisher’s website through the link below. Copyright @ The Author 2009.Motivation: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. Results: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online (http://bioinformatics.oxfordjournals.org/cgi/content/full/btp564/DC1).Fundação Calouste Gulbenkian, Fundação para a Ciência e a Tecnologia (FCT) and Siemens SA Portugal

Validation of a proxy‐reported SARC‐F questionnaire for current and retrospective screening of sarcopenia‐related functional impairments

BACKGROUND: The strength, assistance walking, rise from a chair, climb stairs, and falls (SARC‐F) questionnaire is a well‐established instrument for screening of sarcopenia and sarcopenia‐related functional impairments. As it is based on self‐reporting, its use precludes patients who are unable to answer the questionnaire as a consequence of severe acute diseases or cognitive impairment. Therefore, we aimed to validate a proxy‐reported version of the SARC‐F for both ad‐hoc as well as retrospective screening for severe sarcopenia‐related functional impairments. METHODS: Patients aged ≥60 years completed the SARC‐F and performed the short physical performance battery (SPPB) at baseline (T1). Proxies in Cohort A gave a simultaneous assessment of the patients' functional status with the proxy‐reported SARC‐F at T1 and again, retrospectively, after 3 months (T2). Proxies in Cohort B only completed the SARC‐F retrospectively at T2. The questionnaires' performances were assessed through sensitivity/specificity analyses and receiver operating characteristic (ROC) curves. For non‐inferiority analyses, results of both the patient‐reported and proxy‐reported SARC‐F were correlated with the SPPB total score as well as the results of the chair‐rise test subcategory; the respective correlation coefficients were tested against each other. RESULTS: One hundred and four patients and 135 proxies participated. Using a SPPB score < 9 points as the reference standard, the proxy‐reported SARC‐F identified patients at high risk for sarcopenia‐related functional impairment with a sensitivity of 0.81 (ad‐hoc), 0.88 (retrospective Cohort A), and 0.87 (retrospective Cohort B) as well as a specificity of 0.89 (ad‐hoc), 0.78 (retrospective Cohort A), and 0.64 (retrospective Cohort B). Areas under the ROC curves were ≥ 0.9 for the ad‐hoc proxy‐reported SARC‐F and the retrospective proxy‐reported SARC‐F in both cohorts. The proxy‐reported SARC‐F showed a non‐inferior correlation with the SPPB compared with the patient‐reported SARC‐F for ad‐hoc (P = <0.001) as well as retrospective screening for severe sarcopenia‐related functional impairment in both Cohorts A (P = 0.007) and B (P = 0.026). CONCLUSIONS: Proxy‐reported SARC‐F is a valid instrument for both ad‐hoc as well as retrospective screening for sarcopenia‐related functional impairment and could become the standard tool for evaluating this risk in older adults with severe acute disease, for example, in patients with quickly evolving haematological conditions

Principal elementary mode analysis (PEMA)

Principal component analysis (PCA) has been widely applied in fluxomics to compress data into a few latent structures in order to simplify the identification of metabolic patterns. These latent structures lack a direct biological interpretation due to the intrinsic constraints associated with a PCA model. Here we introduce a new method that significantly improves the interpretability of the principal components with a direct link to metabolic pathways. This method, called principal elementary mode analysis (PEMA), establishes a bridge between a PCA-like model, aimed at explaining the maximum variance in flux data, and the set of elementary modes (EMs) of a metabolic network. It provides an easy way to identify metabolic patterns in large fluxomics datasets in terms of the simplest pathways of the organism metabolism. The results using a real metabolic model of Escherichia coli show the ability of PEMA to identify the EMs that generated the different simulated flux distributions. Actual flux data of E. coli and Pichia pastoris cultures confirm the results observed in the simulated study, providing a biologically meaningful model to explain flux data of both organisms in terms of the EM activation. The PEMA toolbox is freely available for non-commercial purposes on http://mseg.webs.upv.es.Research in this study was partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds from the European Union through grants DPI2011-28112-C04-02 and DPI2014-55276-C5-1R. We would also acknowledge Fundacao para a Ciencia e Tecnologia for PhD fellowships with references SFRH/BD/67033/2009, SFRH/BD/70768/2010 and PTDC/BBB-BSS/2800/2012.Folch Fortuny, A.; Marques, R.; Isidro, IA.; Oliveira, R.; Ferrer, A. (2016). Principal elementary mode analysis (PEMA). Molecular BioSystems. 12(3):737-746. doi:10.1039/c5mb00828jS73774612

a direct encoding for nnc polyhedra

We present an alternative Double Description representation for the domain of NNC (not necessarily closed) polyhedra, together with the corresponding Chernikova-like conversion procedure. The representation uses no slack variable at all and provides a solution to a few technical issues caused by the encoding of an NNC polyhedron as a closed polyhedron in a higher dimension space. A preliminary experimental evaluation shows that the new conversion algorithm is able to achieve significant efficiency improvements