@misc{HischeLarhlimiSchwarzetal.2012, author = {Hische, Manuela and Larhlimi, Abdelhalim and Schwarz, Franziska and Fischer-Rosinsk{\´y}, Antje and Bobbert, Thomas and Assmann, Anke and Catchpole, Gareth S. and Pfeiffer, Andreas F. H. and Willmitzer, Lothar and Selbig, Joachim and Spranger, Joachim}, title = {A distinct metabolic signature predictsdevelopment of fasting plasma glucose}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, number = {850}, issn = {1866-8372}, doi = {10.25932/publishup-42740}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427400}, pages = {12}, year = {2012}, abstract = {Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.}, language = {en} } @article{SulpiceNikoloskiTschoepetal.2013, author = {Sulpice, Ronan and Nikoloski, Zoran and Tschoep, Hendrik and Antonio, Carla and Kleessen, Sabrina and Larhlimi, Abdelhalim and Selbig, Joachim and Ishihara, Hirofumi and Gibon, Yves and Fernie, Alisdair R. and Stitt, Mark}, title = {Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions(1[W][OA])}, series = {Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants}, volume = {162}, journal = {Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants}, number = {1}, publisher = {American Society of Plant Physiologists}, address = {Rockville}, issn = {0032-0889}, doi = {10.1104/pp.112.210104}, pages = {347 -- 363}, year = {2013}, abstract = {Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.}, language = {en} } @article{DavidMarashiLarhlimietal.2011, author = {David, Laszlo and Marashi, Sayed-Amir and Larhlimi, Abdelhalim and Mieth, Bettina and Bockmayr, Alexander}, title = {FFCA a feasibility-based method for flux coupling analysis of metabolic networks}, series = {BMC bioinformatics}, volume = {12}, journal = {BMC bioinformatics}, number = {12}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {10.1186/1471-2105-12-236}, pages = {7}, year = {2011}, abstract = {Background: Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature. Results: We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods. Conclusions: We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.}, language = {en} } @article{HoehenwarterLarhlimiHummeletal.2011, author = {H{\"o}henwarter, Wolfgang and Larhlimi, Abdelhalim and Hummel, Jan and Egelhofer, Volker and Selbig, Joachim and van Dongen, Joost T. and Wienkoop, Stefanie and Weckwerth, Wolfram}, title = {MAPA Distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber}, series = {Journal of proteome research}, volume = {10}, journal = {Journal of proteome research}, number = {7}, publisher = {American Chemical Society}, address = {Washington}, issn = {1535-3893}, doi = {10.1021/pr101109a}, pages = {2979 -- 2991}, year = {2011}, abstract = {Mass Accuracy Precursor Alignment is a fast and flexible method for comparative proteome analysis that allows the comparison of unprecedented numbers of shotgun proteomics analyses on a personal computer in a matter of hours. We compared 183 LC-MS analyses and more than 2 million MS/MS spectra and could define and separate the proteomic phenotypes of field grown tubers of 12 tetraploid cultivars of the crop plant Solanum tuberosum. Protein isoforms of patatin as well as other major gene families such as lipoxygenase and cysteine protease inhibitor that regulate tuber development were found to be the primary source of variability between the cultivars. This suggests that differentially expressed protein isoforms modulate genotype specific tuber development and the plant phenotype. We properly assigned the measured abundance of tryptic peptides to different protein isoforms that share extensive stretches of primary structure and thus inferred their abundance. Peptides unique to different protein isoforms were used to classify the remaining peptides assigned to the entire subset of isoforms based on a common abundance profile using multivariate statistical procedures. We identified nearly 4000,proteins which we used for quantitative functional annotation making this the most extensive study of the tuber proteome to date.}, language = {en} } @article{SchudomaLarhlimiWalther2011, author = {Schudoma, Christian and Larhlimi, Abdelhalim and Walther, Dirk}, title = {The influence of the local sequence environment on RNA loop structures}, series = {RNA : a publication of the RNA Society}, volume = {17}, journal = {RNA : a publication of the RNA Society}, number = {7}, publisher = {Cold Spring Harbor Laboratory Press}, address = {Cold Spring Harbor, NY}, issn = {1355-8382}, doi = {10.1261/rna.2550211}, pages = {1247 -- 1257}, year = {2011}, abstract = {RNA folding is assumed to be a hierarchical process. The secondary structure of an RNA molecule, signified by base-pairing and stacking interactions between the paired bases, is formed first. Subsequently, the RNA molecule adopts an energetically favorable three-dimensional conformation in the structural space determined mainly by the rotational degrees of freedom associated with the backbone of regions of unpaired nucleotides (loops). To what extent the backbone conformation of RNA loops also results from interactions within the local sequence context or rather follows global optimization constraints alone has not been addressed yet. Because the majority of base stacking interactions are exerted locally, a critical influence of local sequence on local structure appears plausible. Thus, local loop structure ought to be predictable, at least in part, from the local sequence context alone. To test this hypothesis, we used Random Forests on a nonredundant data set of unpaired nucleotides extracted from 97 X-ray structures from the Protein Data Bank (PDB) to predict discrete backbone angle conformations given by the discretized eta/theta-pseudo-torsional space. Predictions on balanced sets with four to six conformational classes using local sequence information yielded average accuracies of up to 55\%, thus significantly better than expected by chance (17\%-25\%). Bases close to the central nucleotide appear to be most tightly linked to its conformation. Our results suggest that RNA loop structure does not only depend on long-range base-pairing interactions; instead, it appears that local sequence context exerts a significant influence on the formation of the local loop structure.}, language = {en} } @article{LarhlimiDavidSelbigetal.2012, author = {Larhlimi, Abdelhalim and David, Laszlo and Selbig, Joachim and Bockmayr, Alexander}, title = {F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks}, series = {BMC bioinformatics}, volume = {13}, journal = {BMC bioinformatics}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {10.1186/10.1186/1471-2105-13-57}, pages = {9}, year = {2012}, abstract = {Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.}, language = {en} } @article{LarhlimiBaslerGrimbsetal.2012, author = {Larhlimi, Abdelhalim and Basler, Georg and Grimbs, Sergio and Selbig, Joachim and Nikoloski, Zoran}, title = {Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks}, series = {Bioinformatics}, volume = {28}, journal = {Bioinformatics}, number = {18}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/bts381}, pages = {I502 -- I508}, year = {2012}, abstract = {Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases. Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called 'stoichiometric capacitance'. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes.}, language = {en} } @misc{LarhlimiBlachonSelbigetal.2011, author = {Larhlimi, Abdelhalim and Blachon, Sylvain and Selbig, Joachim and Nikoloski, Zoran}, title = {Robustness of metabolic networks a review of existing definitions}, series = {Biosystems : journal of biological and information processing sciences}, volume = {106}, journal = {Biosystems : journal of biological and information processing sciences}, number = {1}, publisher = {Elsevier}, address = {Oxford}, issn = {0303-2647}, doi = {10.1016/j.biosystems.2011.06.002}, pages = {1 -- 8}, year = {2011}, abstract = {Describing the determinants of robustness of biological systems has become one of the central questions in systems biology. Despite the increasing research efforts, it has proven difficult to arrive at a unifying definition for this important concept. We argue that this is due to the multifaceted nature of the concept of robustness and the possibility to formally capture it at different levels of systemic formalisms (e.g, topology and dynamic behavior). Here we provide a comprehensive review of the existing definitions of robustness pertaining to metabolic networks. As kinetic approaches have been excellently reviewed elsewhere, we focus on definitions of robustness proposed within graph-theoretic and constraint-based formalisms.}, language = {en} } @misc{LarhlimiDavidSelbigetal.2012, author = {Larhlimi, Abdelhalim and David, Laszlo and Selbig, Joachim and Bockmayr, Alexander}, title = {F2C2}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {921}, issn = {1866-8372}, doi = {10.25932/publishup-43243}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-432431}, pages = {11}, year = {2012}, abstract = {Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.}, language = {en} }