TY - JOUR A1 - Andorf, Sandra A1 - Meyer, Rhonda C. A1 - Selbig, Joachim A1 - Altmann, Thomas A1 - Repsilber, Dirk T1 - Integration of a systems biological network analysis and QTL results for biomass heterosis in arabidopsis thaliana JF - PLoS one N2 - To contribute to a further insight into heterosis we applied an integrative analysis to a systems biological network approach and a quantitative genetics analysis towards biomass heterosis in early Arabidopsis thaliana development. The study was performed on the parental accessions C24 and Col-0 and the reciprocal crosses. In an over-representation analysis it was tested if the overlap between the resulting gene lists of the two approaches is significantly larger than expected by chance. Top ranked genes in the results list of the systems biological analysis were significantly over-represented in the heterotic QTL candidate regions for either hybrid as well as regarding mid-parent and best-parent heterosis. This suggests that not only a few but rather several genes that influence biomass heterosis are located within each heterotic QTL region. Furthermore, the overlapping resulting genes of the two integrated approaches were particularly enriched in biomass related pathways. A chromosome-wise over-representation analysis gave rise to the hypothesis that chromosomes number 2 and 4 probably carry a majority of the genes involved in biomass heterosis in the early development of Arabidopsis thaliana. Y1 - 2012 U6 - https://doi.org/10.1371/journal.pone.0049951 SN - 1932-6203 VL - 7 IS - 11 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Basler, Georg A1 - Ebenhoeh, Oliver A1 - Selbig, Joachim A1 - Nikoloski, Zoran T1 - Mass-balanced randomization of metabolic networks JF - Bioinformatics N2 - Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. Results: We first review the shortcomings of the existing generic sampling scheme-switch randomization-and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties. Y1 - 2011 U6 - https://doi.org/10.1093/bioinformatics/btr145 SN - 1367-4803 VL - 27 IS - 10 SP - 1397 EP - 1403 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Basler, Georg A1 - Grimbs, Sergio A1 - Ebenhöh, Oliver A1 - Selbig, Joachim A1 - Nikoloski, Zoran T1 - Evolutionary significance of metabolic network properties JF - Interface : journal of the Royal Society N2 - Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone. KW - metabolic networks KW - significance KW - randomization KW - null model KW - centrality Y1 - 2012 U6 - https://doi.org/10.1098/rsif.2011.0652 SN - 1742-5689 VL - 9 IS - 71 SP - 1168 EP - 1176 PB - Royal Society CY - London ER - TY - JOUR A1 - Beerenwinkel, Niko A1 - Sing, Tobias A1 - Lengauer, Thomas A1 - Rahnenfuhrer, Joerg A1 - Roomp, Kirsten A1 - Savenkov, Igor A1 - Fischer, Roman A1 - Hoffmann, Daniel A1 - Selbig, Joachim A1 - Korn, Klaus A1 - Walter, Hauke A1 - Berg, Thomas A1 - Braun, Patrick A1 - Faetkenheuer, Gerd A1 - Oette, Mark A1 - Rockstroh, Juergen A1 - Kupfer, Bernd A1 - Kaiser, Rolf A1 - Daeumer, Martin T1 - Computational methods for the design of effective therapies against drug resistant HIV strains N2 - The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data Y1 - 2005 ER - TY - JOUR A1 - Bordag, Natalie A1 - Klie, Sebastian A1 - Jürchott, Kathrin A1 - Vierheller, Janine A1 - Schiewe, Hajo A1 - Albrecht, Valerie A1 - Tonn, Jörg-Christian A1 - Schwartz, Christoph A1 - Schichor, Christian A1 - Selbig, Joachim T1 - Glucocorticoid (dexamethasone)-induced metabolome changes in healthy males suggest prediction of response and side effects JF - Scientific reports N2 - Glucocorticoids are indispensable anti-inflammatory and decongestant drugs with high prevalence of use at (similar to)0.9% of the adult population. Better holistic insights into glucocorticoid-induced changes are crucial for effective use as concurrent medication and management of adverse effects. The profiles of 214 metabolites from plasma of 20 male healthy volunteers were recorded prior to and after ingestion of a single dose of 4 mg dexamethasone (+20 mg pantoprazole). Samples were drawn at three predefined time points per day: seven untreated (day 1 midday - day 3 midday) and four treated (day 3 evening - day 4 evening) per volunteer. Statistical analysis revealed tremendous impact of dexamethasone on the metabolome with 150 of 214 metabolites being significantly deregulated on at least one time point after treatment (ANOVA, Benjamini-Hochberg corrected, q < 0.05). Inter-person variability was high and remained uninfluenced by treatment. The clearly visible circadian rhythm prior to treatment was almost completely suppressed and deregulated by dexamethasone. The results draw a holistic picture of the severe metabolic deregulation induced by single-dose, short-term glucocorticoid application. The observed metabolic changes suggest a potential for early detection of severe side effects, raising hope for personalized early countermeasures increasing quality of life and reducing health care costs. Y1 - 2015 U6 - https://doi.org/10.1038/srep15954 SN - 2045-2322 VL - 5 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Bulik, Sascha A1 - Grimbs, Sergio A1 - Huthmacher, Carola A1 - Selbig, Joachim A1 - Holzhutter, Hermann G. T1 - Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws : a promising method for speeding up the kinetic modelling of complex metabolic networks N2 - Kinetic modelling of complex metabolic networks - a central goal of computational systems biology - is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified (nonmechanistic) rate equations (e.g. mass action, LinLog, Michaelis-Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG & Steuer R (2007) Mol Syst Biol3, 146, doi:10.1038/msb4100186]. Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks. Y1 - 2009 UR - http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291742-4658 U6 - https://doi.org/10.1111/j.1742-4658.2008.06784.x SN - 1742-464X ER - TY - JOUR A1 - Catchpole, Gareth A1 - Platzer, Alexander A1 - Weikert, Cornelia A1 - Kempkensteffen, Carsten A1 - Johannsen, Manfred A1 - Krause, Hans A1 - Jung, Klaus A1 - Miller, Kurt A1 - Willmitzer, Lothar A1 - Selbig, Joachim A1 - Weikert, Steffen T1 - Metabolic profiling reveals key metabolic features of renal cell carcinoma JF - Journal of cellular and molecular medicine : a journal of translational medicine N2 - Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. alpha-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC. KW - kidney cancer KW - metabolism KW - metabolomics KW - metastasis Y1 - 2011 U6 - https://doi.org/10.1111/j.1582-4934.2009.00939.x SN - 1582-1838 VL - 15 IS - 1 SP - 109 EP - 118 PB - Wiley-Blackwell CY - Malden ER - TY - JOUR A1 - Childs, Dorothee A1 - Grimbs, Sergio A1 - Selbig, Joachim T1 - Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns JF - Bioinformatics N2 - Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase. Y1 - 2015 U6 - https://doi.org/10.1093/bioinformatics/btv243 SN - 1367-4803 SN - 1460-2059 VL - 31 IS - 12 SP - 214 EP - 220 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Cordes, Frank A1 - Kaiser, Rolf A1 - Selbig, Joachim T1 - Bioinformatics approach to predicting HIV drug resistance N2 - The emergence of drug resistance remains one of the most challenging issues in the treatment of HIV-1 infection. The extreme replication dynamics of HIV facilitates its escape from the selective pressure exerted by the human immune system and by the applied combination drug therapy. This article reviews computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genotypic and phenotypic data. Genotypic assays are based on the analysis of mutations associated with reduced drug susceptibility, but are difficult to interpret due to the numerous mutations and mutational patterns that confer drug resistance. Phenotypic resistance or susceptibility can be experimentally evaluated by measuring the inhibition of the viral replication in cell culture assays. However, this procedure is expensive and time consuming Y1 - 2006 UR - http://www.expert-reviews.com/loi/erm U6 - https://doi.org/10.1586/14737159.6.2.207 SN - 1473-7159 ER - TY - JOUR A1 - Daub, Carsten O. A1 - Steuer, Ralf A1 - Selbig, Joachim A1 - Kloska, Sebastian T1 - Estimating mutual information using B-spline functions : an improved similarity measure for analysing gene expression data N2 - Background: The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. In the context of the clustering of genes with similar patterns of expression it has been suggested as a general quantity of similarity to extend commonly used linear measures. Since mutual information is defined in terms of discrete variables, its application to continuous data requires the use of binning procedures, which can lead to significant numerical errors for datasets of small or moderate size. Results: In this work, we propose a method for the numerical estimation of mutual information from continuous data. We investigate the characteristic properties arising from the application of our algorithm and show that our approach outperforms commonly used algorithms: The significance, as a measure of the power of distinction from random correlation, is significantly increased. This concept is subsequently illustrated on two large-scale gene expression datasets and the results are compared to those obtained using other similarity measures. A C++ source code of our algorithm is available for non- commercial use from kloska@scienion.de upon request. Conclusion: The utilisation of mutual information as similarity measure enables the detection of non-linear correlations in gene expression datasets. Frequently applied linear correlation measures, which are often used on an ad-hoc basis without further justification, are thereby extended Y1 - 2004 SN - 1471-2105 ER - TY - GEN A1 - Durek, Pawel A1 - Schudoma, Christian A1 - Weckwerth, Wolfram A1 - Selbig, Joachim A1 - Walther, Dirk T1 - Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins N2 - Background: Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites. Results: We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information. Conclusion: While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structurebased P-site prediction method has been made available at http://phos3d.mpimp-golm.mpg.de. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - paper 141 KW - Support vector machines KW - Microarray data KW - Docking interactions KW - Signal-transduction KW - Sequence alignment Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-45129 ER - TY - GEN A1 - Dworschak, Steve A1 - Grell, Susanne A1 - Nikiforova, Victoria J. A1 - Schaub, Torsten H. A1 - Selbig, Joachim T1 - Modeling biological networks by action languages via answer set programming T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - We describe an approach to modeling biological networks by action languages via answer set programming. To this end, we propose an action language for modeling biological networks, building on previous work by Baral et al. We introduce its syntax and semantics along with a translation into answer set programming, an efficient Boolean Constraint Programming Paradigm. Finally, we describe one of its applications, namely, the sulfur starvation response-pathway of the model plant Arabidopsis thaliana and sketch the functionality of our system and its usage. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 843 KW - biological network model KW - action language KW - answer set programming Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429846 SN - 1866-8372 IS - 843 ER - TY - JOUR A1 - Edlich-Muth, Christian A1 - Muraya, Moses M. A1 - Altmann, Thomas A1 - Selbig, Joachim T1 - Phenomic prediction of maize hybrids JF - Biosystems : journal of biological and information processing sciences N2 - Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging. (C) 2016 Elsevier Ireland Ltd. All rights reserved. KW - Hybrid prediction KW - LASSO KW - Regression KW - Maize KW - Phenomics Y1 - 2016 U6 - https://doi.org/10.1016/j.biosystems.2016.05.008 SN - 0303-2647 SN - 1872-8324 VL - 146 SP - 102 EP - 109 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Feher, Kristen A1 - Lisec, Jan A1 - Roemisch-Margl, Lilla A1 - Selbig, Joachim A1 - Gierl, Alfons A1 - Piepho, Hans-Peter A1 - Nikoloski, Zoran A1 - Willmitzer, Lothar T1 - Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach JF - PLoS one Y1 - 2014 U6 - https://doi.org/10.1371/journal.pone.0085435 SN - 1932-6203 VL - 9 IS - 1 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Flöter, André A1 - Nicolas, Jacques A1 - Schaub, Torsten H. A1 - Selbig, Joachim T1 - Threshold extraction in metabolite concentration data N2 - Motivation: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data. Results: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states Y1 - 2004 ER - TY - JOUR A1 - Flöter, André A1 - Nicolas, Jacques A1 - Schaub, Torsten H. A1 - Selbig, Joachim T1 - Threshold extraction in metabolite concentration data Y1 - 2003 UR - http://www.cs.uni-potsdam.de/wv/pdfformat/floeterGCB2003.pdf ER - TY - JOUR A1 - Flöter, André A1 - Selbig, Joachim A1 - Schaub, Torsten H. T1 - Finding metabolic pathways in decision forests Y1 - 2004 SN - 3-540-23221-4 ER - TY - JOUR A1 - Girbig, Dorothee A1 - Grimbs, Sergio A1 - Selbig, Joachim T1 - Systematic analysis of stability patterns in plant primary metabolism JF - PLoS one N2 - Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM) is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models. Y1 - 2012 U6 - https://doi.org/10.1371/journal.pone.0034686 SN - 1932-6203 VL - 7 IS - 4 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Girbig, Dorothee A1 - Selbig, Joachim A1 - Grimbs, Sergio T1 - A MATLAB toolbox for structural kinetic modeling JF - Bioinformatics N2 - Structural kinetic modeling (SKM) enables the analysis of dynamical properties of metabolic networks solely based on topological information and experimental data. Current SKM-based experiments are hampered by the time-intensive process of assigning model parameters and choosing appropriate sampling intervals for MonteCarlo experiments. We introduce a toolbox for the automatic and efficient construction and evaluation of structural kinetic models (SK models). Quantitative and qualitative analyses of network stability properties are performed in an automated manner. We illustrate the model building and analysis process in detailed example scripts that provide toolbox implementations of previously published literature models. Y1 - 2012 U6 - https://doi.org/10.1093/bioinformatics/bts473 SN - 1367-4803 VL - 28 IS - 19 SP - 2546 EP - 2547 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Grell, Susanne A1 - Schaub, Torsten H. A1 - Selbig, Joachim T1 - Modelling biological networks by action languages via set programming Y1 - 2006 UR - http://www.cs.uni-potsdam.de/wv/pdfformat/gebsch06c.pdf U6 - https://doi.org/10.1007/11799573 SN - 0302-9743 ER -