TY - JOUR A1 - Yu, Yanjun A1 - Wu, Shenjie A1 - Nowak, Jacqueline A1 - Wang, Guangda A1 - Han, Libo A1 - Feng, Zhidi A1 - Mendrinna, Amelie A1 - Ma, Yinping A1 - Wang, Huan A1 - Zhang, Xiaxia A1 - Tian, Juan A1 - Dong, Li A1 - Nikoloski, Zoran A1 - Persson, Staffan A1 - Kong, Zhaosheng T1 - Live-cell imaging of the cytoskeleton in elongating cotton fibres JF - Nature plants N2 - Cotton (Gossypium hirsutum) fibres consist of single cells that grow in a highly polarized manner, assumed to be controlled by the cytoskeleton(1-3). However, how the cytoskeletal organization and dynamics underpin fibre development remains unexplored. Moreover, it is unclear whether cotton fibres expand via tip growth or diffuse growth(2-4). We generated stable transgenic cotton plants expressing fluorescent markers of the actin and microtubule cytoskeleton. Live-cell imaging revealed that elongating cotton fibres assemble a cortical filamentous actin network that extends along the cell axis to finally form actin strands with closed loops in the tapered fibre tip. Analyses of F-actin network properties indicate that cotton fibres have a unique actin organization that blends features of both diffuse and tip growth modes. Interestingly, typical actin organization and endosomal vesicle aggregation found in tip-growing cell apices were not observed in fibre tips. Instead, endomembrane compartments were evenly distributed along the elongating fibre cells and moved bi-directionally along the fibre shank to the fibre tip. Moreover, plus-end tracked microtubules transversely encircled elongating fibre shanks, reminiscent of diffusely growing cells. Collectively, our findings indicate that cotton fibres elongate via a unique tip-biased diffuse growth mode. Y1 - 2019 U6 - https://doi.org/10.1038/s41477-019-0418-8 SN - 2055-026X SN - 2055-0278 VL - 5 IS - 5 SP - 498 EP - 504 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Xu, Rudan A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli JF - Bioinformatics N2 - Motivation: Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms. Results: Here, we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes. Y1 - 2021 U6 - https://doi.org/10.1093/bioinformatics/btab575 SN - 1367-4803 SN - 1460-2059 VL - 37 IS - 21 SP - 3848 EP - 3855 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Wendering, Philipp A1 - Nikoloski, Zoran T1 - COMMIT BT - Consideration of metabolite leakage and community composition improves microbial community reconstructions JF - PLoS Computational Biology : a new community journal / publ. by the Public Library of Science (PLoS) in association with the International Society for Computational Biology (ISCB) N2 - Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
Author summaryMicrobial communities are important in ecology, human health, and crop productivity. However, detailed information on the interactions within natural microbial communities is hampered by the community size, lack of detailed information on the biochemistry of single organisms, and the complexity of interactions between community members. Metabolic models are comprised of biochemical reaction networks based on the genome annotation, and can provide mechanistic insights into community functions. Previous analyses of microbial community models have been performed with high-quality reference models or models generated using a single reconstruction pipeline. However, these models do not contain information on the composition of the community that determines the metabolites exchanged between the community members. In addition, the quality of metabolic models is affected by the reconstruction approach used, with direct consequences on the inferred interactions between community members. Here, we use fully automated consensus reconstructions from four approaches to arrive at functional models with improved genomic support while considering the community composition. We applied our pipeline to two soil communities from the Arabidopsis thaliana culture collection, providing only genome sequences. Finally, we show that the obtained models have 90% genomic support and demonstrate that the derived interactions are corroborated by independent computational predictions. Y1 - 2022 U6 - https://doi.org/10.1371/journal.pcbi.1009906 SN - 1553-734X SN - 1553-7358 VL - 18 IS - 3 PB - Public Library of Science CY - San Fransisco ER - TY - JOUR A1 - Tong, Hao A1 - Nikoloski, Zoran T1 - Machine learning approaches for crop improvement BT - leveraging phenotypic and genotypic big data JF - Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants N2 - Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement. KW - genomic selection KW - genomic prediction KW - machine learning KW - multiple KW - traits KW - multi-omics KW - GxE interaction Y1 - 2020 U6 - https://doi.org/10.1016/j.jplph.2020.153354 SN - 0176-1617 SN - 1618-1328 VL - 257 PB - Elsevier CY - München ER - TY - JOUR A1 - Tong, Hao A1 - Nankar, Amol N. A1 - Liu, Jintao A1 - Todorova, Velichka A1 - Ganeva, Daniela A1 - Grozeva, Stanislava A1 - Tringovska, Ivanka A1 - Pasev, Gancho A1 - Radeva-Ivanova, Vesela A1 - Gechev, Tsanko A1 - Kostova, Dimitrina A1 - Nikoloski, Zoran T1 - Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits JF - Horticulture research N2 - Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding. Y1 - 2022 U6 - https://doi.org/10.1093/hr/uhac072 SN - 2052-7276 VL - 9 PB - Oxford Univ. Press CY - Cary ER - TY - JOUR A1 - Tong, Hao A1 - Küken, Anika A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - Characterization of effects of genetic variants via genome-scale metabolic modelling JF - Cellular and molecular life sciences : CMLS N2 - Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism. KW - Single-nucleotide polymorphisms KW - Metabolic models KW - Genome-wide KW - association studies KW - Genomic selection Y1 - 2021 U6 - https://doi.org/10.1007/s00018-021-03844-4 SN - 1420-682X SN - 1420-9071 VL - 78 IS - 12 SP - 5123 EP - 5138 PB - Springer International Publishing AG CY - Cham ER - TY - JOUR A1 - Tong, Hao A1 - Küken, Anika A1 - Nikoloski, Zoran T1 - Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth JF - Nature Communications N2 - The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops. An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments. Y1 - 2020 U6 - https://doi.org/10.1038/s41467-020-16279-5 SN - 2041-1723 VL - 11 IS - 1 PB - Nature Publishing Group UK CY - London ER - TY - GEN A1 - Szymanski, Jedrzej A1 - Jozefczuk, Szymon A1 - Nikoloski, Zoran A1 - Selbig, Joachim A1 - Nikiforova, Victoria A1 - Catchpole, Gareth A1 - Willmitzer, Lothar T1 - Stability of metabolic correlations under changing environmental conditions in Escherichia coli : a systems approach N2 - Background: Biological systems adapt to changing environments by reorganizing their cellula r and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underl ying metabolic network. Methodology/Principal Findings: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic conditiondependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple ob servation s about the changes of metabolic concentrations. The approach was tested with Escherichia colias a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diau xie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical path ways, and (3) ind ependently of the response scale, based on their importance in the reorganization of the cor relation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. Conclusions/Significance: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-ba sed approach does not rely on major changes in concentration to identify metabolites important for st ress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - paper 147 KW - Small-world networks KW - saccharomyces-cerevisiae KW - trehalose synthesis KW - gene-expression KW - stress-response Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-45253 ER - TY - JOUR A1 - Song, Yu A1 - Li, Gang A1 - Nowak, Jacqueline A1 - Zhang, Xiaoqing A1 - Xu, Dongbei A1 - Yang, Xiujuan A1 - Huang, Guoqiang A1 - Liang, Wanqi A1 - Yang, Litao A1 - Wang, Canhua A1 - Bulone, Vincent A1 - Nikoloski, Zoran A1 - Hu, Jianping A1 - Persson, Staffan A1 - Zhang, Dabing T1 - The Rice Actin-Binding Protein RMD Regulates Light-Dependent Shoot Gravitropism JF - Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants N2 - Light and gravity are two key determinants in orientating plant stems for proper growth and development. The organization and dynamics of the actin cytoskeleton are essential for cell biology and critically regulated by actin-binding proteins. However, the role of actin cytoskeleton in shoot negative gravitropism remains controversial. In this work, we report that the actin-binding protein Rice Morphology Determinant (RMD) promotes reorganization of the actin cytoskeleton in rice (Oryza sativa) shoots. The changes in actin organization are associated with the ability of the rice shoots to respond to negative gravitropism. Here, light-grown rmd mutant shoots exhibited agravitropic phenotypes. By contrast, etiolated rmd shoots displayed normal negative shoot gravitropism. Furthermore, we show that RMD maintains an actin configuration that promotes statolith mobility in gravisensing endodermal cells, and for proper auxin distribution in light-grown, but not dark-grown, shoots. RMD gene expression is diurnally controlled and directly repressed by the phytochrome-interacting factor-like protein OsPIL16. Consequently, overexpression of OsPIL16 led to gravisensing and actin patterning defects that phenocopied the rmd mutant. Our findings outline a mechanism that links light signaling and gravity perception for straight shoot growth in rice. Y1 - 2019 U6 - https://doi.org/10.1104/pp.19.00497 SN - 0032-0889 SN - 1532-2548 VL - 181 IS - 2 SP - 630 EP - 644 PB - American Society of Plant Physiologists CY - Rockville ER - TY - JOUR A1 - Smith, Sarah R. A1 - Dupont, Chris L. A1 - McCarthy, James K. A1 - Broddrick, Jared T. A1 - Obornik, Miroslav A1 - Horak, Ales A1 - Füssy, Zoltán A1 - Cihlar, Jaromir A1 - Kleessen, Sabrina A1 - Zheng, Hong A1 - McCrow, John P. A1 - Hixson, Kim K. A1 - Araujo, Wagner L. A1 - Nunes-Nesi, Adriano A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran A1 - Palsson, Bernhard O. A1 - Allen, Andrew E. T1 - Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom JF - Nature Communications N2 - Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa. KW - Biochemistry KW - Computational biology and bioinformatics KW - Evolution KW - Microbiology KW - Molecular biology Y1 - 2019 U6 - https://doi.org/10.1038/s41467-019-12407-y SN - 2041-1723 VL - 10 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Schwahn, Kevin A1 - Nikoloski, Zoran T1 - Data reduction approaches for dissecting transcriptional effects on metabolism JF - Frontiers in plant science N2 - The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana. KW - E. coil KW - S. cerevisiae KW - A. thaliana KW - partial correlation KW - principal component analysis KW - metabolomics KW - data reduction KW - regulation Y1 - 2018 U6 - https://doi.org/10.3389/fpls.2018.00538 SN - 1664-462X VL - 9 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Scheunemann, Michael A1 - Brady, Siobhan M. A1 - Nikoloski, Zoran T1 - Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models JF - Scientific reports N2 - Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art constraint-based modeling approach with all publicly available transcriptomics and metabolomics data from this system to date. We integrate these models into a multi-cell root model which we investigate with respect to network structure, distribution of fluxes, and concordance to transcriptomics and proteomics data. From a methodological point, we show that the coupling of tissue-specific models in a multi-tissue model yields a higher specificity of the interconnected models with respect to network structure and flux distributions. We use the extracted models to predict and investigate the flux of the growth hormone indole-3-actetate and its antagonist, trans-Zeatin, through the root. While some of predictions are in line with experimental evidence, constraints other than those coming from the metabolic level may be necessary to replicate the flow of indole-3-actetate from other simulation studies. Therefore, our work provides the means for data-driven multi-tissue metabolic model extraction of other Arabidopsis organs in the constraint-based modeling framework. Y1 - 2018 U6 - https://doi.org/10.1038/s41598-018-26232-8 SN - 2045-2322 VL - 8 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Rodriguez Cubillos, Andres Eduardo A1 - Tong, Hao A1 - Alseekh, Saleh A1 - de Abreu e Lima, Francisco Anastacio A1 - Yu, Jing A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran A1 - Laitinen, Roosa A. E. T1 - Inheritance patterns in metabolism and growth in diallel crosses of Arabidopsis thaliana from a single growth habitat JF - Heredity N2 - Metabolism is a key determinant of plant growth and modulates plant adaptive responses. Increased metabolic variation due to heterozygosity may be beneficial for highly homozygous plants if their progeny is to respond to sudden changes in the habitat. Here, we investigate the extent to which heterozygosity contributes to the variation in metabolism and size of hybrids of Arabidopsis thaliana whose parents are from a single growth habitat. We created full diallel crosses among seven parents, originating from Southern Germany, and analysed the inheritance patterns in primary and secondary metabolism as well as in rosette size in situ. In comparison to primary metabolites, compounds from secondary metabolism were more variable and showed more pronounced non-additive inheritance patterns which could be attributed to epistasis. In addition, we showed that glucosinolates, among other secondary metabolites, were positively correlated with a proxy for plant size. Therefore, our study demonstrates that heterozygosity in local A. thaliana population generates metabolic variation and may impact several tasks directly linked to metabolism. Y1 - 2018 U6 - https://doi.org/10.1038/s41437-017-0030-5 SN - 0018-067X SN - 1365-2540 VL - 120 IS - 5 SP - 463 EP - 473 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data JF - npj Systems biology and applications N2 - Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data fromEscherichia coliandSaccharomyces cerevisiaeas well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs fromE. coliandS. cerevisiaeto validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein-protein and protein-metabolite interactions. Y1 - 2020 U6 - https://doi.org/10.1038/s41540-020-0140-1 SN - 2056-7189 VL - 6 IS - 1 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - GeneReg BT - a constraint-based approach for design of feasible metabolic engineering strategies at the gene level JF - Bioinformatics N2 - Motivation Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies. Results Here, we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies. Y1 - 2020 U6 - https://doi.org/10.1093/bioinformatics/btaa996 SN - 1367-4803 SN - 1460-2059 VL - 37 IS - 12 SP - 1717 EP - 1723 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Pandey, Prashant K. A1 - Yu, Jing A1 - Omranian, Nooshin A1 - Alseekh, Saleh A1 - Vaid, Neha A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran A1 - Laitinen, Roosa A. E. T1 - Plasticity in metabolism underpins local responses to nitrogen in Arabidopsis thaliana populations JF - Plant Direct N2 - Nitrogen (N) is central for plant growth, and metabolic plasticity can provide a strategy to respond to changing N availability. We showed that two local A. thaliana populations exhibited differential plasticity in the compounds of photorespiratory and starch degradation pathways in response to three N conditions. Association of metabolite levels with growth-related and fitness traits indicated that controlled plasticity in these pathways could contribute to local adaptation and play a role in plant evolution. KW - Arabidopsis thaliana KW - natural variation KW - nitrogen availability KW - photorespiration KW - plasticity Y1 - 2019 U6 - https://doi.org/10.1002/pld3.186 SN - 2475-4455 VL - 3 IS - 11 PB - John Wiley & sonst LTD CY - Chichester ER - TY - JOUR A1 - Omranian, Sara A1 - Nikoloski, Zoran A1 - Grimm, Dominik G. T1 - Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward BT - present state, challenges, and the way forward JF - Computational and structural biotechnology journal N2 - Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). KW - Protein Complex Prediction KW - Protein-Protein interaction network KW - Network KW - Clustering Algorithms KW - Network embedding Y1 - 2022 U6 - https://doi.org/10.1016/j.csbj.2022.05.049 SN - 2001-0370 VL - 20 SP - 2699 EP - 2712 PB - Research Network of Computational and Structural Biotechnology (RNCSB) CY - Gotenburg ER - TY - JOUR A1 - Omranian, Sara A1 - Angeleska, Angela A1 - Nikoloski, Zoran T1 - Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient JF - Computational and structural biotechnology journal N2 - Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of similar to 35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. KW - Protein complexes KW - Protein-protein interaction KW - Network clustering KW - Species comparison Y1 - 2021 U6 - https://doi.org/10.1016/j.csbj.2021.09.014 SN - 2001-0370 VL - 19 SP - 5255 EP - 5263 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Omranian, Sara A1 - Angeleska, Angela A1 - Nikoloski, Zoran T1 - PC2P BT - parameter-free network-based prediction of protein complexes JF - Bioinformatics N2 - Motivation: Prediction of protein complexes from protein-protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction. Results: To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks. Y1 - 2021 U6 - https://doi.org/10.1093/bioinformatics/btaa1089 SN - 1367-4811 VL - 37 IS - 1 SP - 73 EP - 81 PB - Oxford Univ. Press CY - Oxford ER - TY - GEN A1 - Omidbakhshfard, Mohammad Amin A1 - Neerakkal, Sujeeth A1 - Gupta, Saurabh A1 - Omranian, Nooshin A1 - Guinan, Kieran J. A1 - Brotman, Yariv A1 - Nikoloski, Zoran A1 - Fernie, Alisdair R. A1 - Mueller-Roeber, Bernd A1 - Gechev, Tsanko S. T1 - A Biostimulant Obtained from the Seaweed Ascophyllum nodosum Protects Arabidopsis thaliana from Severe Oxidative Stress T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Abiotic stresses cause oxidative damage in plants. Here, we demonstrate that foliar application of an extract from the seaweed Ascophyllum nodosum, SuperFifty (SF), largely prevents paraquat (PQ)-induced oxidative stress in Arabidopsis thaliana. While PQ-stressed plants develop necrotic lesions, plants pre-treated with SF (i.e., primed plants) were unaffected by PQ. Transcriptome analysis revealed induction of reactive oxygen species (ROS) marker genes, genes involved in ROS-induced programmed cell death, and autophagy-related genes after PQ treatment. These changes did not occur in PQ-stressed plants primed with SF. In contrast, upregulation of several carbohydrate metabolism genes, growth, and hormone signaling as well as antioxidant-related genes were specific to SF-primed plants. Metabolomic analyses revealed accumulation of the stress-protective metabolite maltose and the tricarboxylic acid cycle intermediates fumarate and malate in SF-primed plants. Lipidome analysis indicated that those lipids associated with oxidative stress-induced cell death and chloroplast degradation, such as triacylglycerols (TAGs), declined upon SF priming. Our study demonstrated that SF confers tolerance to PQ-induced oxidative stress in A. thaliana, an effect achieved by modulating a range of processes at the transcriptomic, metabolic, and lipid levels. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 823 KW - Ascophyllum nodosum KW - Arabidopsis thaliana KW - biostimulant KW - paraquat KW - priming KW - oxidative stress tolerance KW - reactive oxygen species Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-445093 SN - 1866-8372 IS - 823 ER - TY - JOUR A1 - Omidbakhshfard, Mohammad Amin A1 - Neerakkal, Sujeeth A1 - Gupta, Saurabh A1 - Omranian, Nooshin A1 - Guinan, Kieran J. A1 - Brotman, Yariv A1 - Nikoloski, Zoran A1 - Fernie, Alisdair R. A1 - Mueller-Roeber, Bernd A1 - Gechev, Tsanko S. T1 - A Biostimulant Obtained from the Seaweed Ascophyllum nodosum Protects Arabidopsis thaliana from Severe Oxidative Stress JF - International Journal of Molecular Sciences N2 - Abiotic stresses cause oxidative damage in plants. Here, we demonstrate that foliar application of an extract from the seaweed Ascophyllum nodosum, SuperFifty (SF), largely prevents paraquat (PQ)-induced oxidative stress in Arabidopsis thaliana. While PQ-stressed plants develop necrotic lesions, plants pre-treated with SF (i.e., primed plants) were unaffected by PQ. Transcriptome analysis revealed induction of reactive oxygen species (ROS) marker genes, genes involved in ROS-induced programmed cell death, and autophagy-related genes after PQ treatment. These changes did not occur in PQ-stressed plants primed with SF. In contrast, upregulation of several carbohydrate metabolism genes, growth, and hormone signaling as well as antioxidant-related genes were specific to SF-primed plants. Metabolomic analyses revealed accumulation of the stress-protective metabolite maltose and the tricarboxylic acid cycle intermediates fumarate and malate in SF-primed plants. Lipidome analysis indicated that those lipids associated with oxidative stress-induced cell death and chloroplast degradation, such as triacylglycerols (TAGs), declined upon SF priming. Our study demonstrated that SF confers tolerance to PQ-induced oxidative stress in A. thaliana, an effect achieved by modulating a range of processes at the transcriptomic, metabolic, and lipid levels. KW - Ascophyllum nodosum KW - Arabidopsis thaliana KW - biostimulant KW - paraquat KW - priming KW - oxidative stress tolerance KW - reactive oxygen species Y1 - 2019 U6 - https://doi.org/10.3390/ijms21020474 SN - 1422-0067 VL - 21 IS - 2 PB - Molecular Diversity Preservation International CY - Basel ER - TY - JOUR A1 - Nunes-Nesi, Adriano A1 - Alseekh, Saleh A1 - de Oliveira Silva, Franklin Magnum A1 - Omranian, Nooshin A1 - Lichtenstein, Gabriel A1 - Mirnezhad, Mohammad A1 - Romero Gonzalez, Roman R. A1 - Sabio y Garcia, Julia A1 - Conte, Mariana A1 - Leiss, Kirsten A. A1 - Klinkhamer, Peter Gerardus Leonardus A1 - Nikoloski, Zoran A1 - Carrari, Fernando A1 - Fernie, Alisdair R. T1 - Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds JF - Metabolomics N2 - IntroductionTo date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism.ObjectiveThis study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues.MethodsThe analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses.ResultsChanges in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism.ConclusionsOverall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits. KW - Metabolite QTL KW - Tomato KW - Leaf metabolism KW - Metabolite network Y1 - 2019 U6 - https://doi.org/10.1007/s11306-019-1503-8 SN - 1573-3882 SN - 1573-3890 VL - 15 IS - 46 PB - Springer CY - New York ER - TY - JOUR A1 - Nowak, Jacqueline A1 - Gennermann, Kristin A1 - Persson, Staffan A1 - Nikoloski, Zoran T1 - CytoSeg 2.0 BT - automated extraction of actin filaments JF - Bioinformatics N2 - Motivation: Actin filaments (AFs) are dynamic structures that substantially change their organization over time. The dynamic behavior and the relatively low signal-to-noise ratio during live-cell imaging have rendered the quantification of the actin organization a difficult task. Results: We developed an automated image-based framework that extracts AFs from fluorescence microscopy images and represents them as networks, which are automatically analyzed to identify and compare biologically relevant features. Although the source code is freely available, we have now implemented the framework into a graphical user interface that can be installed as a Fiji plugin, thus enabling easy access by the research community. Y1 - 2020 U6 - https://doi.org/10.1093/bioinformatics/btaa035 SN - 1367-4803 SN - 1460-2059 VL - 36 IS - 9 SP - 2950 EP - 2951 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Mbebi, Alain J. A1 - Tong, Hao A1 - Nikoloski, Zoran T1 - L-2,L-1-norm regularized multivariate regression model with applications to genomic prediction JF - Bioinformatics N2 - Motivation: Genomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP). Results: Here, we propose a L-2,L-1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L-2,L-1-joint, applicable in multi-trait GS. The usage of the L-2,L-1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with datasets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model. Y1 - 2021 U6 - https://doi.org/10.1093/bioinformatics/btab212 SN - 1367-4803 SN - 1460-2059 VL - 37 IS - 18 SP - 2896 EP - 2904 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Mbebi, Alain J. A1 - Breitler, Jean-Christophe A1 - Bordeaux, M'elanie A1 - Sulpice, Ronan A1 - McHale, Marcus A1 - Tong, Hao A1 - Toniutti, Lucile A1 - Castillo, Jonny Alonso A1 - Bertrand, Benoit A1 - Nikoloski, Zoran T1 - A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids JF - G3: Genes, genomes, genetics N2 - Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops. KW - genomic prediction KW - phenomic prediction KW - 3-way coffee hybrids KW - chlorophyll a fluorescence KW - GenPred KW - Shared Data Resource Y1 - 2022 U6 - https://doi.org/10.1093/g3journal/jkac170 SN - 2160-1836 VL - 12 IS - 9 PB - Genetics Soc. of America CY - Pittsburgh, PA ER - TY - JOUR A1 - Lyall, Rafe A1 - Nikoloski, Zoran A1 - Gechev, Tsanko T1 - Comparative analysis of ROS network genes in extremophile Eukaryotes JF - International journal of molecular sciences N2 - The reactive oxygen species (ROS) gene network, consisting of both ROS-generating and detoxifying enzymes, adjusts ROS levels in response to various stimuli. We performed a cross-kingdom comparison of ROS gene networks to investigate how they have evolved across all Eukaryotes, including protists, fungi, plants and animals. We included the genomes of 16 extremotolerant Eukaryotes to gain insight into ROS gene evolution in organisms that experience extreme stress conditions. Our analysis focused on ROS genes found in all Eukaryotes (such as catalases, superoxide dismutases, glutathione reductases, peroxidases and glutathione peroxidase/peroxiredoxins) as well as those specific to certain groups, such as ascorbate peroxidases, dehydroascorbate/monodehydroascorbate reductases in plants and other photosynthetic organisms. ROS-producing NADPH oxidases (NOX) were found in most multicellular organisms, although several NOX-like genes were identified in unicellular or filamentous species. However, despite the extreme conditions experienced by extremophile species, we found no evidence for expansion of ROS-related gene families in these species compared to other Eukaryotes. Tardigrades and rotifers do show ROS gene expansions that could be related to their extreme lifestyles, although a high rate of lineage-specific horizontal gene transfer events, coupled with recent tetraploidy in rotifers, could explain this observation. This suggests that the basal Eukaryotic ROS scavenging systems are sufficient to maintain ROS homeostasis even under the most extreme conditions. KW - ROS KW - extremotolerance KW - resurrection plants Y1 - 2020 U6 - https://doi.org/10.3390/ijms21239131 SN - 1422-0067 VL - 21 IS - 23 PB - Molecular Diversity Preservation International (MDPI) CY - Basel ER - TY - JOUR A1 - Langary, Damoun A1 - Nikoloski, Zoran T1 - Inference of chemical reaction networks based on concentration profiles using an optimization framework JF - Chaos : an interdisciplinary journal of nonlinear science N2 - Understanding the structure of reaction networks along with the underlying kinetics that lead to particular concentration readouts of the participating components is the first step toward optimization and control of (bio-)chemical processes. Yet, solutions to the problem of inferring the structure of reaction networks, i.e., characterizing the stoichiometry of the participating reactions provided concentration profiles of the participating components, remain elusive. Here, we present an approach to infer the stoichiometric subspace of a chemical reaction network from steady-state concentration data profiles obtained from a continuous isothermal reactor. The subsequent problem of finding reactions consistent with the observed subspace is cast as a series of mixed-integer linear programs whose solution generates potential reaction vectors together with a measure of their likelihood. We demonstrate the efficiency and applicability of the proposed approach using data obtained from synthetic reaction networks and from a well-established biological model for the Calvin-Benson cycle. Furthermore, we investigate the effect of missing information, in the form of unmeasured species or insufficient diversity within the data set, on the ability to accurately reconstruct the network reactions. The proposed framework is, in principle, applicable to many other reaction systems, thus providing future extensions to understanding reaction networks guiding chemical reactors and complex biological mixtures. (C) 2019 Author(s). Y1 - 2019 U6 - https://doi.org/10.1063/1.5120598 SN - 1054-1500 SN - 1089-7682 VL - 29 IS - 11 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Laitinen, Roosa A. E. A1 - Nikoloski, Zoran T1 - Genetic basis of plasticity in plants JF - Journal of experimental botany N2 - The ability of an organism to change its phenotype in response to different environments, termed plasticity, is a particularly important characteristic to enable sessile plants to adapt to rapid changes in their surroundings. Plasticity is a quantitative trait that can provide a fitness advantage and mitigate negative effects due to environmental perturbations. Yet, its genetic basis is not fully understood. Alongside technological limitations, the main challenge in studying plasticity has been the selection of suitable approaches for quantification of phenotypic plasticity. Here, we propose a categorization of the existing quantitative measures of phenotypic plasticity into nominal and relative approaches. Moreover, we highlight the recent advances in the understanding of the genetic architecture underlying phenotypic plasticity in plants. We identify four pillars for future research to uncover the genetic basis of phenotypic plasticity, with emphasis on development of computational approaches and theories. These developments will allow us to perform specific experiments to validate the causal genes for plasticity and to discover their role in plant fitness and evolution. KW - Genetic architecture KW - GWA KW - GxE interaction KW - hub genes KW - plant adaptation KW - plasticity KW - variance Y1 - 2018 U6 - https://doi.org/10.1093/jxb/ery404 SN - 0022-0957 SN - 1460-2431 VL - 70 IS - 3 SP - 739 EP - 745 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Küken, Anika A1 - Nikoloski, Zoran T1 - Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways JF - Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants N2 - Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology. Y1 - 2019 U6 - https://doi.org/10.1104/pp.18.01273 SN - 0032-0889 SN - 1532-2548 VL - 179 IS - 3 SP - 894 EP - 906 PB - American Society of Plant Physiologists CY - Rockville ER - TY - JOUR A1 - Küken, Anika A1 - Langary, Damoun A1 - Nikoloski, Zoran T1 - The hidden simplicity of metabolic networks is revealed by multireaction dependencies JF - Science Advances N2 - Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone. Y1 - 2022 U6 - https://doi.org/10.1126/sciadv.abl6962 SN - 2375-2548 VL - 8 IS - 13 PB - American Assoc. for the Advancement of Science CY - Washington ER - TY - GEN A1 - Kleessen, Sabrina A1 - Nikoloski, Zoran T1 - Dynamic regulatory on/off minimization for biological systems under internal temporal perturbations T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Background: Flux balance analysis (FBA) together with its extension, dynamic FBA, have proven instrumental for analyzing the robustness and dynamics of metabolic networks by employing only the stoichiometry of the included reactions coupled with adequately chosen objective function. In addition, under the assumption of minimization of metabolic adjustment, dynamic FBA has recently been employed to analyze the transition between metabolic states. Results: Here, we propose a suite of novel methods for analyzing the dynamics of (internally perturbed) metabolic networks and for quantifying their robustness with limited knowledge of kinetic parameters. Following the biochemically meaningful premise that metabolite concentrations exhibit smooth temporal changes, the proposed methods rely on minimizing the significant fluctuations of metabolic profiles to predict the time-resolved metabolic state, characterized by both fluxes and concentrations. By conducting a comparative analysis with a kinetic model of the Calvin-Benson cycle and a model of plant carbohydrate metabolism, we demonstrate that the principle of regulatory on/off minimization coupled with dynamic FBA can accurately predict the changes in metabolic states. Conclusions: Our methods outperform the existing dynamic FBA-based modeling alternatives, and could help in revealing the mechanisms for maintaining robustness of dynamic processes in metabolic networks over time. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 852 KW - metabolic network KW - metabolite concentration KW - flux rate KW - flux balance analysis KW - qualitative comparative analysis Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-431128 SN - 1866-8372 IS - 852 ER - TY - JOUR A1 - Jose Clemente-Moreno, Maria A1 - Omranian, Nooshin A1 - Saez, Patricia A1 - Maria Figueroa, Carlos A1 - Del-Saz, Nestor A1 - Elso, Mhartyn A1 - Poblete, Leticia A1 - Orf, Isabel A1 - Cuadros-Inostroza, Alvaro A1 - Cavieres, Lohengrin A1 - Bravo, Leon A1 - Fernie, Alisdair R. A1 - Ribas-Carbo, Miquel A1 - Flexas, Jaume A1 - Nikoloski, Zoran A1 - Brotman, Yariv A1 - Gago, Jorge T1 - Cytochrome respiration pathway and sulphur metabolism sustain stress tolerance to low temperature in the Antarctic species Colobanthus quitensis JF - New phytologist : international journal of plant science N2 - Understanding the strategies employed by plant species that live in extreme environments offers the possibility to discover stress tolerance mechanisms. We studied the physiological, antioxidant and metabolic responses to three temperature conditions (4, 15, and 23 degrees C) of Colobanthus quitensis (CQ), one of the only two native vascular species in Antarctica. We also employed Dianthus chinensis (DC), to assess the effects of the treatments in a non-Antarctic species from the same family. Using fused LASSO modelling, we associated physiological and biochemical antioxidant responses with primary metabolism. This approach allowed us to highlight the metabolic pathways driving the response specific to CQ. Low temperature imposed dramatic reductions in photosynthesis (up to 88%) but not in respiration (sustaining rates of 3.0-4.2 mu mol CO2 m(-2) s(-1)) in CQ, and no change in the physiological stress parameters was found. Its notable antioxidant capacity and mitochondrial cytochrome respiratory activity (20 and two times higher than DC, respectively), which ensure ATP production even at low temperature, was significantly associated with sulphur-containing metabolites and polyamines. Our findings potentially open new biotechnological opportunities regarding the role of antioxidant compounds and respiratory mechanisms associated with sulphur metabolism in stress tolerance strategies to low temperature. KW - Antarctica KW - antioxidant capacity KW - low temperature KW - photosynthesis KW - respiration KW - stress tolerance KW - sulphur metabolism Y1 - 2019 U6 - https://doi.org/10.1111/nph.16167 SN - 0028-646X SN - 1469-8137 VL - 225 IS - 2 SP - 754 EP - 768 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Hempel, Sabrina A1 - Koseska, Aneta A1 - Nikoloski, Zoran A1 - Kurths, Jürgen T1 - Unraveling gene regulatory networks from time-resolved gene expression data BT - a measures comparison study N2 - Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 371 KW - unferring cellular networks KW - mutual information KW - Escherichia-coli KW - cluster-analysis KW - series KW - algorithms KW - inference KW - models KW - recognition KW - variables Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-400924 ER - TY - JOUR A1 - Hansen, Bjoern Oest A1 - Meyer, Etienne H. A1 - Ferrari, Camilla A1 - Vaid, Neha A1 - Movahedi, Sara A1 - Vandepoele, Klaas A1 - Nikoloski, Zoran A1 - Mutwil, Marek T1 - Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana JF - New phytologist : international journal of plant science N2 - Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists. KW - Arabidopsis thaliana KW - co-function network KW - complex I KW - ensemble prediction KW - gene function prediction Y1 - 2017 U6 - https://doi.org/10.1111/nph.14921 SN - 0028-646X SN - 1469-8137 VL - 217 IS - 4 SP - 1521 EP - 1534 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Ferrari, Camilla A1 - Proost, Sebastian A1 - Janowski, Marcin Andrzej A1 - Becker, Jörg A1 - Nikoloski, Zoran A1 - Bhattacharya, Debashish A1 - Price, Dana A1 - Tohge, Takayuki A1 - Bar-Even, Arren A1 - Fernie, Alisdair R. A1 - Stitt, Mark A1 - Mutwil, Marek T1 - Kingdom-wide comparison reveals the evolution of diurnal gene expression in Archaeplastida JF - Nature Communications N2 - Plants have adapted to the diurnal light-dark cycle by establishing elaborate transcriptional programs that coordinate many metabolic, physiological, and developmental responses to the external environment. These transcriptional programs have been studied in only a few species, and their function and conservation across algae and plants is currently unknown. We performed a comparative transcriptome analysis of the diurnal cycle of nine members of Archaeplastida, and we observed that, despite large phylogenetic distances and dramatic differences in morphology and lifestyle, diurnal transcriptional programs of these organisms are similar. Expression of genes related to cell division and the majority of biological pathways depends on the time of day in unicellular algae but we did not observe such patterns at the tissue level in multicellular land plants. Hence, our study provides evidence for the universality of diurnal gene expression and elucidates its evolutionary history among different photosynthetic eukaryotes. Y1 - 2019 U6 - https://doi.org/10.1038/s41467-019-08703-2 SN - 2041-1723 VL - 10 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Willmitzer, Lothar A1 - Nikoloski, Zoran T1 - Classification-driven framework to predict maize hybrid field performance from metabolic profiles of young parental roots JF - PLoS one N2 - Maize (Zea mays L.) is a staple food whose production relies on seed stocks that largely comprise hybrid varieties. Therefore, knowledge about the molecular determinants of hybrid performance (HP) in the field can be used to devise better performing hybrids to address the demands for sustainable increase in yield. Here, we propose and test a classification-driven framework that uses metabolic profiles from in vitro grown young roots of parental lines from the Dent x Flint maize heterotic pattern to predict field HP. We identify parental analytes that best predict the metabolic inheritance patterns in 328 hybrids. We then demonstrate that these analytes are also predictive of field HP (0.64 >= r >= 0.79) and discriminate hybrids of good performance (accuracy of 87.50%). Therefore, our approach provides a cost-effective solution for hybrid selection programs. Y1 - 2018 U6 - https://doi.org/10.1371/journal.pone.0196038 SN - 1932-6203 VL - 13 IS - 4 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Li, Kun A1 - Wen, Weiwei A1 - Yan, Jianbing A1 - Nikoloski, Zoran A1 - Willmitzer, Lothar A1 - Brotman, Yariv T1 - Unraveling lipid metabolism in maize with time-resolved multi-omics data JF - The plant journal N2 - Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid-transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 x By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species. KW - Zea mays KW - lipid metabolism KW - omics KW - GFLASSO KW - QTL Y1 - 2018 U6 - https://doi.org/10.1111/tpj.13833 SN - 0960-7412 SN - 1365-313X VL - 93 IS - 6 SP - 1102 EP - 1115 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Leifels, Lydia A1 - Nikoloski, Zoran T1 - Regression-based modeling of complex plant traits based on metabolomics data JF - Plant Metabolomics N2 - Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets. KW - Metabolomics KW - Plants KW - Trait KW - Regression KW - Prediction KW - Modeling KW - R programing language KW - R software packages Y1 - 2018 SN - 978-1-4939-7819-9 SN - 978-1-4939-7818-2 U6 - https://doi.org/10.1007/978-1-4939-7819-9_23 SN - 1064-3745 SN - 1940-6029 VL - 1778 SP - 321 EP - 327 PB - Humana Press Inc. CY - New York ER - TY - JOUR A1 - Córdoba, Sandra Correa A1 - Tong, Hao A1 - Burgos, Asdrubal A1 - Zhu, Feng A1 - Alseekh, Saleh A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism JF - Nature Communications N2 - The use of automated tools to reconstruct lipid metabolic pathways is not warranted in plants. Here, the authors construct Plant Lipid Module for Arabidopsis rosette using constraint-based modeling, demonstrate its integration in other plant metabolic models, and use it to dissect the genetic architecture of lipid metabolism. Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism. KW - Biochemical networks KW - Biochemical reaction networks KW - Genetic models KW - Plant molecular biology Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-40644-9 SN - 2041-1723 VL - 14 IS - 1 PB - Springer Nature CY - London ER - TY - GEN A1 - Childs, Liam H. A1 - Nikoloski, Zoran A1 - May, Patrick A1 - Walther, Dirk T1 - Identification and classification of ncRNA molecules using graph properties N2 - The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - paper 145 KW - RNA secondary structure KW - Noncoding RNAs KW - Structure prediction KW - Gene-expression KW - Structured RNAs Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-45192 ER - TY - JOUR A1 - Calderan-Rodrigues, Maria Juliana A1 - Luzarowski, Marcin A1 - Monte-Bello, Carolina Cassano A1 - Minen, Romina Ines A1 - Zühlke, Boris M. A1 - Nikoloski, Zoran A1 - Skirycz, Aleksandra A1 - Caldana, Camila T1 - Proteogenic dipeptides are characterized by diel fluctuations and target of rapamycin complex-signaling dependency in the model plant Arabidopsis thaliana JF - Frontiers in plant science : FPLS N2 - As autotrophic organisms, plants capture light energy to convert carbon dioxide into ATP, nicotinamide adenine dinucleotide phosphate (NADPH), and sugars, which are essential for the biosynthesis of building blocks, storage, and growth. At night, metabolism and growth can be sustained by mobilizing carbon (C) reserves. In response to changing environmental conditions, such as light-dark cycles, the small-molecule regulation of enzymatic activities is critical for reprogramming cellular metabolism. We have recently demonstrated that proteogenic dipeptides, protein degradation products, act as metabolic switches at the interface of proteostasis and central metabolism in both plants and yeast. Dipeptides accumulate in response to the environmental changes and act via direct binding and regulation of critical enzymatic activities, enabling C flux distribution. Here, we provide evidence pointing to the involvement of dipeptides in the metabolic rewiring characteristics for the day-night cycle in plants. Specifically, we measured the abundance of 13 amino acids and 179 dipeptides over short- (SD) and long-day (LD) diel cycles, each with different light intensities. Of the measured dipeptides, 38 and eight were characterized by day-night oscillation in SD and LD, respectively, reaching maximum accumulation at the end of the day and then gradually falling in the night. Not only the number of dipeptides, but also the amplitude of the oscillation was higher in SD compared with LD conditions. Notably, rhythmic dipeptides were enriched in the glucogenic amino acids that can be converted into glucose. Considering the known role of Target of Rapamycin (TOR) signaling in regulating both autophagy and metabolism, we subsequently investigated whether diurnal fluctuations of dipeptides levels are dependent on the TOR Complex (TORC). The Raptor1b mutant (raptor1b), known for the substantial reduction of TOR kinase activity, was characterized by the augmented accumulation of dipeptides, which is especially pronounced under LD conditions. We were particularly intrigued by the group of 16 dipeptides, which, based on their oscillation under SD conditions and accumulation in raptor1b, can be associated with limited C availability or photoperiod. By mining existing protein-metabolite interaction data, we delineated putative protein interactors for a representative dipeptide Pro-Gln. The obtained list included enzymes of C and amino acid metabolism, which are also linked to the TORC-mediated metabolic network. Based on the obtained results, we speculate that the diurnal accumulation of dipeptides contributes to its metabolic adaptation in response to changes in C availability. We hypothesize that dipeptides would act as alternative respiratory substrates and by directly modulating the activity of the focal enzymes. KW - dipeptide KW - diel cycle KW - metabolism KW - TOR signaling KW - protein-metabolite KW - interactions KW - carbon limitation KW - amino acid Y1 - 2021 U6 - https://doi.org/10.3389/fpls.2021.758933 SN - 1664-462X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Basler, Georg A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Advances in metabolic flux analysis toward genome-scale profiling of higher organisms JF - Bioscience reports : communications and reviews in molecular and cellular biology N2 - Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies. Y1 - 2018 U6 - https://doi.org/10.1042/BSR20170224 SN - 0144-8463 SN - 1573-4935 VL - 38 PB - Portland Press (London) CY - London ER - TY - JOUR A1 - Arend, Marius A1 - Zimmer, David A1 - Xu, Rudan A1 - Sommer, Frederik A1 - Mühlhaus, Timo A1 - Nikoloski, Zoran T1 - Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale JF - Nature Communications N2 - Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas. KW - Computational models KW - Enzymes KW - Proteomics Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-40498-1 SN - 2041-1723 VL - 14 IS - 1 PB - Springer Nature CY - London ER - TY - JOUR A1 - Angeleska, Angela A1 - Nikoloski, Zoran T1 - Coherent network partitions JF - Discrete applied mathematics N2 - Graph clustering is widely applied in the analysis of cellular networks reconstructed from large-scale data or obtained from experimental evidence. Here we introduce a new type of graph clustering based on the concept of coherent partition. A coherent partition of a graph G is a partition of the vertices of G that yields only disconnected subgraphs in the complement of G. The coherence number of G is then the size of the smallest edge cut inducing a coherent partition. A coherent partition of G is optimal if the size of the inducing edge cut is the coherence number of G. Given a graph G, we study coherent partitions and the coherence number in connection to (bi)clique partitions and the (bi)clique cover number. We show that the problem of finding the coherence number is NP-hard, but is of polynomial time complexity for trees. We also discuss the relation between coherent partitions and prominent graph clustering quality measures. KW - Graph partitions KW - Network clustering KW - Coherence number KW - Coherent partition Y1 - 2019 U6 - https://doi.org/10.1016/j.dam.2019.02.048 SN - 0166-218X SN - 1872-6771 VL - 266 SP - 283 EP - 290 PB - Elsevier CY - Amsterdam ER -