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

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

Random sampling of elementary flux modes in large-scale metabolic networks

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states

Improved high-resolution global and regionalized isoscapes of δ¹⁸O, δ²H and d-excess in precipitation

AbstractPatterns of δ¹⁸O and δ²H in Earth’s precipitation provide essential scientific data for use in hydrological, climatological, ecological and forensic research. Insufficient global spatial data coverage promulgated the use of gridded datasets employing geostatistical techniques (isoscapes) for spatiotemporally coherent isotope predictions. Cluster-based isoscape regionalization combines the advantages of local or regional prediction calibrations into a global framework. Here we present a revision of a Regionalized Cluster-Based Water Isotope Prediction model (RCWIP2) incorporating new isotope data having extensive spatial coverage and a wider array of predictor variables combined with high-resolution gridded climatic data. We introduced coupling of δ¹⁸O and δ²H (e.g., d-excess constrained) in the model predictions to prevent runaway isoscapes when each isotope is modelled separately and cross-checked observed versus modelled d-excess values. We improved model error quantification by adopting full uncertainty propagation in all calculations. RCWIP2 improved the RMSE over previous isoscape models by ca. 0.3 ‰ for δ¹⁸O and 2.5 ‰ for δ²H with an uncertainty https://isotopehydrologynetwork.iaea.org.Abstract Patterns of δ¹⁸O and δ²H in Earth’s precipitation provide essential scientific data for use in hydrological, climatological, ecological and forensic research. Insufficient global spatial data coverage promulgated the use of gridded datasets employing geostatistical techniques (isoscapes) for spatiotemporally coherent isotope predictions. Cluster-based isoscape regionalization combines the advantages of local or regional prediction calibrations into a global framework. Here we present a revision of a Regionalized Cluster-Based Water Isotope Prediction model (RCWIP2) incorporating new isotope data having extensive spatial coverage and a wider array of predictor variables combined with high-resolution gridded climatic data. We introduced coupling of δ¹⁸O and δ²H (e.g., d-excess constrained) in the model predictions to prevent runaway isoscapes when each isotope is modelled separately and cross-checked observed versus modelled d-excess values. We improved model error quantification by adopting full uncertainty propagation in all calculations. RCWIP2 improved the RMSE over previous isoscape models by ca. 0.3 ‰ for δ¹⁸O and 2.5 ‰ for δ²H with an uncertainty <1.0 ‰ for δ¹⁸O and < 8 ‰ for δ²H for most regions of the world. The determination of the relative importance of each predictor variable in each ecoclimatic zone is a new approach to identify previously unrecognized climatic drivers on mean annual precipitation δ¹⁸O and δ²H. The improved RCWIP2 isoscape grids and maps (season, monthly, annual, regional) are available for download at https://isotopehydrologynetwork.iaea.org

Schlussbericht zur zweiten Förderphase des Projekts

Das Projekt „Know how to teach (K2teach) – Grundlegende Handlungskompetenzen für eine adaptive Unterrichtspraxis im Studium erwerben“ verfolgte im Rahmen der Qualitätsoffensive Lehrerbildung (QLB) in der zweiten Förderphase (2019-2023) weiterhin das Ziel, die Lehrkräftebildung an der Freien Universität Berlin (FU) nachhaltig, qualitativ und strukturell zu verbessern. Hierbei fokussierte die Projektarbeit in K2teach auf drei QLB-Handlungsfelder: A. die stärkere, qualitativ bessere Verknüpfung von Theorie- und Praxisanteilen im Lehramtsstudium, B. Profilierung der Strukturen der Lehrkräftebildung an der Freien Universität (FU) Berlin C. die stärkere Verzahnung von Fachdidaktiken und Bildungswissenschaften. Im Handlungsfeld A wurden praxisnahe Lehrformate entwickelt und erprobt, darunter fall- und videobasierte Lehr- und Lerntools (u.a. FOCUS Videoportal) sowie Lehr-Lern-Labor-Seminare. Die Arbeit mit realistischen Schulszenarien in fallbasierten Lehr-Lern-Tools, die fokussierte Analyse von Unterrichtsvideos sowie eine komplexitätsreduzierte Erfahrung von Unterricht in Lehr-Lern-Labor-Seminaren ermöglicht den Lehramtsstudierenden den Erwerb von (Vorläufer-)Kompetenzen professionellen Handelns sowie eine theoriebasierten Reflexion von Unterricht. Im Handlungsfeld B fokussierte K2teach auf die Profilierung und Entwicklung der Lehrkräftebildungsstruktur durch Weiterentwicklung, Verbreiterung und Verstetigung der entwickelten Lehr-Lern-Konzepte sowie durch die Erprobung eines neuen Masters of Education für Quereinsteiger\*innen in das Lehramt („Q-Master“) in einem Modellversuch in verschiedenen Fächern. Im Handlungsfeld C wurde die Verbindung von Fachdidaktiken und Bildungswissenschaften gestärkt. Dabei wurden interdisziplinäre Teams gebildet und fachübergreifende Lehr-Lern-Gelegenheiten sowie der Quereinstiegs-Master entwickelt. Alle K2teach-Maßnahmen wurden umfassend evaluiert. Als Ergebnis der zweiten Förderphase von K2teach konnten die in der ersten QLB-Förderphase zunächst exemplarisch für Pilotfächer entwickelten und beforschten Konzepte und Formate miteinander verzahnt und auf weitere Fächer, größere Studierendenkohorten sowie weitere Zielgruppen (Lehramtsanwärter*innen im Referendariat (2. Phase), Fort- und Weiterbildung von Lehrpersonen (3. Phase)) ausgeweitet werden. Die Kooperationen mit anderen Standorten der QLB wurden ausgebaut und die Anschlussfähigkeit an die zweite und dritte Phase der Lehrkräftebildung gesichert. Im Berichtszeitraum 2019 bis 2023 wurden die erfolgreich erprobten Projekt-Maßnahmen vollständig in die Strukturen der Dahlem School of Education (DSE) überführt, die nun die Funktion einer zentralen Schnittstelle für Studium und Lehre sowie Forschung im Bereich der Lehrkräftebildung der FU Berlin übernimmt. K2teach hat somit über zwei Förderphasen als wegweisendes Projekt der Lehrkräftebildung am Standort FU Berlin profilbildend gewirkt. Die initiierten Maßnahmen werden auch nach Ende der beiden Förderphasen fortgeführt. Datei-Upload durch TIBIn the second funding phase (2019-2023), the project "Know how to teach (K2teach) - Acquiring basic skills for adaptive teaching practice during your studies", continued to pursue the goal of sustainably, qualitatively, and structurally improving teacher training at Freie Universität (FU) Berlin as part of the “Qualitätsoffensive Lehrerbildung” (QLB). The project work in K2teach focused on three QLB areas of action: A. Stronger, qualitatively better linking of theoretical and practical components in teacher education, B. Strengthening of teacher education at FU Berlin, and C. Stronger integration of subject didactics and educational sciences. In area of action A, practice-oriented teaching formats were developed and tested, including case- and video-based teaching and learning tools (such as the FOCUS Video Portal) as well as teaching-learning laboratory seminars. Working with realistic school scenarios in case-based teaching-learning tools, focused analysis of teaching videos, and a complexity-reduced experience of teaching in the teaching-learning laboratory seminars enabled teacher education students to acquire (precursor) skills for professional action and theory-based reflection on teaching. In area of action B, K2teach focused on profiling and developing the teacher education structure through further development, expansion, and consolidation of the developed teaching-learning concepts, as well as through the establishment of a teacher education master's program for career changers (Q-Master) in various subjects. In area of action C, the connection between subject didactics and educational sciences was strengthened. Interdisciplinary teams were formed, and cross-disciplinary teaching-learning opportunities as well as the Q-Master were developed. All K2teach measures were comprehensively evaluated. As a result of the second funding phase of K2teach, the concepts and formats initially developed and researched as examples for pilot subjects in the first QLB funding phase were interlinked and extended to other subjects, larger student cohorts and other target groups (trainee teachers (2nd phase), further and in-service teacher training (3rd phase)). Collaboration with other QLB sites has been expanded and links with the second and third phases of teacher training have been ensured. In the reporting period 2019 to 2023, the successfully tested project measures were fully transferred into the regular teacher education structures of the Dahlem School of Education (DSE) which functions as a central interface for study, teaching and research in the field of teacher training at the FU Berlin. K2teach has thus had a profile-building effect over two funding phases as a pioneering project in teacher education at the FU Berlin. The measures initiated will be continued after the end of the two funding phases

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