Minimal cut sets in a metabolic network are elementary modes in a dual network

Motivation: Elementary modes (EMs) and minimal cut sets (MCSs) provide important techniques for metabolic network modeling. Whereas EMs describe minimal subnetworks that can function in steady state, MCSs are sets of reactions whose removal will disable certain network functions. Effective algorithms were developed for EM computation while calculation of MCSs is typically addressed by indirect methods requiring the computation of EMs as initial step. Results: In this contribution, we provide a method that determines MCSs directly without calculating the EMs. We introduce a duality framework for metabolic networks where the enumeration of MCSs in the original network is reduced to identifying the EMs in a dual network. As a further extension, we propose a generalization of MCSs in metabolic networks by allowing the combination of inhomogeneous constraints on reaction rates. This framework provides a promising tool to open the concept of EMs and MCSs to a wider class of applications. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

BACKGROUND: A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. RESULTS: YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at . CONCLUSION: A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. Results: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. Conclusions: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks

Computing paths and cycles in biological interaction graphs

<p>Abstract</p> <p>Background</p> <p>Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.</p> <p>Results</p> <p>We first review algorithms for the enumeration of paths and cycles and show that these algorithms are superior to a recently proposed enumeration approach based on elementary-modes computation. The main part of this work deals with the computation of shortest positive/negative paths, an NP-complete problem for which only very few algorithms are described in the literature. We propose extensions and several new algorithm variants for computing either exact results or approximations. Benchmarks with various concrete biological networks show that exact results can sometimes be obtained in networks with several hundred nodes. A class of even larger graphs can still be treated exactly by a new algorithm combining exhaustive and simple search strategies. For graphs, where the computation of exact solutions becomes time-consuming or infeasible, we devised an approximative algorithm with polynomial complexity. Strikingly, in realistic networks (where a comparison with exact results was possible) this algorithm delivered results that are very close or equal to the exact values. This phenomenon can probably be attributed to the particular topology of cellular signaling and regulatory networks which contain a relatively low number of negative feedback loops.</p> <p>Conclusion</p> <p>The calculation of shortest positive/negative paths and cycles in interaction graphs is an important method for network analysis in Systems Biology. This contribution draws the attention of the community to this important computational problem and provides a number of new algorithms, partially specifically tailored for biological interaction graphs. All algorithms have been implemented in the <it>CellNetAnalyzer </it>framework which can be downloaded for academic use at <url>http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html</url>.</p

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

Självreglerat lärande och motivation i olika typer av idrotter: En jämförelse

Självreglerat lärande är en metakognitiv process som visat sig främja idrottares prestation och utveckling. En förutsättning för denna process är att idrottare känner motivation till att utvecklas inom sin idrott. Hur självreglerat lärande och motivation korrelerar samt eventuella skillnader mellan lagidrottare och individuella idrottare avseende självreglerat lärande och motivation är dock relativt outforskade områden. Således var syftet med föreliggande studie att undersöka detta närmare. I studien användes ett teoretiskt ramverk baserat på Self-Regulated Learning (SRL) samt Self Determination Theory (SDT) för att undersöka dessa områden hos 102 tävlingsaktiva idrottare mellan 18-30 år (M = 23,71, SD = 3,77) i deras träningsvardag. Data samlades in genom två elektroniska självskattningsformulär: The Behavioral Regulation in Sport Questionnare (BRSQ-24) och Self-Regulated Learning-TT (SRL-TT). Variansanalyser genomfördes för att undersöka eventuella skillnader avseende SRL och motivationsform mellan respektive grupp. Korrelationsanalyser genomfördes för att undersöka sambanden mellan SRL och motivationsform.  Resultaten visade att individuella idrottare skattade en signifikant högre användning av samtliga SRL-strategier än lagidrottare, medan grupperna inte skiljde sig signifikant gällande motivationsform. Vidare var sambandet mellan autonom motivation och samtliga SRL-faser signifikant positivt för hela urvalet. Resultaten kan förklaras av att det inom individuella idrotter är mer naturligt att använda självreglerat lärande samt att idrottares motivationsform och i vilken utsträckning de använder sig av SRL-strategier korrelerar. Framtida forskning kan med fördel undersöka på vilket sätt idrottares motivationsform och SRL påverkar varandra

Självreglerat lärande och motivation i olika typer av idrotter: En jämförelse

Självreglerat lärande är en metakognitiv process som visat sig främja idrottares prestation och utveckling. En förutsättning för denna process är att idrottare känner motivation till att utvecklas inom sin idrott. Hur självreglerat lärande och motivation korrelerar samt eventuella skillnader mellan lagidrottare och individuella idrottare avseende självreglerat lärande och motivation är dock relativt outforskade områden. Således var syftet med föreliggande studie att undersöka detta närmare. I studien användes ett teoretiskt ramverk baserat på Self-Regulated Learning (SRL) samt Self Determination Theory (SDT) för att undersöka dessa områden hos 102 tävlingsaktiva idrottare mellan 18-30 år (M = 23,71, SD = 3,77) i deras träningsvardag. Data samlades in genom två elektroniska självskattningsformulär: The Behavioral Regulation in Sport Questionnare (BRSQ-24) och Self-Regulated Learning-TT (SRL-TT). Variansanalyser genomfördes för att undersöka eventuella skillnader avseende SRL och motivationsform mellan respektive grupp. Korrelationsanalyser genomfördes för att undersöka sambanden mellan SRL och motivationsform.  Resultaten visade att individuella idrottare skattade en signifikant högre användning av samtliga SRL-strategier än lagidrottare, medan grupperna inte skiljde sig signifikant gällande motivationsform. Vidare var sambandet mellan autonom motivation och samtliga SRL-faser signifikant positivt för hela urvalet. Resultaten kan förklaras av att det inom individuella idrotter är mer naturligt att använda självreglerat lärande samt att idrottares motivationsform och i vilken utsträckning de använder sig av SRL-strategier korrelerar. Framtida forskning kan med fördel undersöka på vilket sätt idrottares motivationsform och SRL påverkar varandra