Zeitschriftenartikel (195)

34.
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Wendering, P.; Nikoloski, Z.: COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions. PLoS Computational Biology 18 (3), e1009906 (2022)
35.
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Wendering, P.; Nikoloski, Z.: Genome-Scale Modeling Specifies the Metabolic Capabilities of Rhizophagus irregularis. mSystems 7 (1), e01216-21 (2022)
36.
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Calderan-Rodrigues, M.J.; Luzarowski, M.; Monte Bello, C.C.; Minen, R.I.; Zühlke, B. M.; Nikoloski, Z.; Skirycz, A.; Caldana, C.: Proteogenic Dipeptides Are Characterized by Diel Fluctuations and Target of Rapamycin Complex-Signaling Dependency in the Model Plant Arabidopsis thaliana. Frontiers in Plant Science 12, 758933 (2021)
37.
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Treves, H.; Küken, A.; Arrivault, S.; Ishihara, H.; Hoppe, I.; Erban, A.; Höhne, M.; Moraes, T. A.; Kopka, J.; Szymanski, J. et al.; Nikoloski, Z.; Stitt, M.: Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C-3 and C-4 plants. Nature Plants 8, S. 78 - 91 (2021)
38.
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David, Z.; Swart, C.; Graf, A.; Arrivault, S.; Tillich, M.; Proost, S.; Nikoloski, Z.; Stitt, M.; Bock, R.; Mühlhaus, M. et al.; Boulouis, A.: Topology of the redox network during induction of photosynthesis as revealed by time-resolved proteomics in tobacco. Science Advances 7 (51), eabi8307 (2021)
39.
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Hashemi, S.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Identification of flux trade-offs in metabolic networks. Scientific Reports 11 (1), 23776 (2021)
40.
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Angeleska, A.; Omranian, N.; Nikoloski, Z.: Coherent network partitions: Characterizations with cographs and prime graphs. Theoretical computer science 894, S. 3 - 11 (2021)
41.
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Zhu, F.; Alseekh, S.; Koper, K.; Tong, H.; Nikoloski, Z.; Naake, T.; Liu, H.; Yan, J.; Brotman, Y.; Wen, W. et al.; Maeda, H.; Cheng, Y.; Fernie, A. R.: Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis. The Plant Cell 34 (1), S. 557 - 578 (2021)
42.
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Omranian, N.; Angeleska, A.; Nikoloski, Z.: Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient. Computational and Structural Biotechnology Journal 19, S. 5255 - 5263 (2021)
43.
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Aarabi, F.; Rakpenthai, A.; Barahimipour, R.; Gorka, M.; Alseekh, S.; Zhang, Y.; Salem, M.A.; Brückner, F.; Omranian, N.; Watanabe, M. et al.; Nikoloski, Z.; Giavalisco, P.; Tohge, T.; Graf, A.; Fernie, A. R.; Hoefgen, R.: Sulfur Deficiency Induced genes affect seed protein accumulation and composition under sulfate deprivation. Plant Physiology 187 (4), S. 2419 - 2434 (2021)
44.
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Xu, R.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics 37 (21), S. 3848 - 3855 (2021)
45.
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Duarte, G. T.; Pandey, P. K.; Vaid, N.; Alseekh, S.; Fernie, A. R.; Nikoloski, Z.; Laitinen, R.: Plasticity of rosette size in response to nitrogen availability is controlled by an RCC1-family protein. Plant, Cell and Environment 44 (10), S. 3398 - 3411 (2021)
46.
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Moreno, J.C.; Rojas, B.E.; Vicente, R.; Gorka, M.; Matz, T.; Kosmacz, M.; Peralta-Ariza, J.S.; Zhang, Y.; Alseekh, S.; Childs, D. et al.; Luzarowski, M.; Nikoloski, Z.; Zarivach, R.; Walther, D.; Hartman, M.D.; Figueroa, C.M.; Iglesias, A.A.; Fernie, A. R.; Skirycz, A.: Tyr-Asp inhibition of glyceraldehyde 3-phosphate dehydrogenase affects plant redox metabolism. EMBO Journal 40 (15), e106800 (2021)
47.
Zeitschriftenartikel
Razaghi-Moghadam, Z.; Nikoloski, Z.: GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics 37 (12), S. 1717 - 1723 (2021)
48.
Zeitschriftenartikel
Eng, R.C.; Schneider, R.; Matz, T.; Carter, R.; Ehrhardt, D. W.; Jönsson, H.; Nikoloski, Z.; Sampathkumar, A.: KATANIN and CLASP function at different spatial scales to mediate microtubule response to mechanical stress in Arabidopsis cotyledons. Current Biology 31 (15), S. 3262 - 3274 (2021)
49.
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Nowak, J.; Eng, R.C.; Matz, T.; Waack, M.; Persson, S.; Sampathkumar, A.; Nikoloski, Z.: A network-based framework for shape analysis enables accurate characterization of leaf epidermal cells. Nature Communications 12 (1), 458 (2021)
50.
Zeitschriftenartikel
Beleggia, R.; Omranian, N.; Holtz, Y.; Gioia, T.; Fiorani, F.; Nigro, F.M.; Pecchioni, N.; De Vita, P.; Schurr, U.; David, J.L. et al.; Nikoloski, Z.; Papa, R.: Comparative analysis based on transcriptomics and metabolomics data reveal differences between emmer and durum wheat in response to nitrogen starvation. International Journal of Molecular Sciences 22 (9), 4790 (2021)
51.
Zeitschriftenartikel
Küken, A.; Wendering, P.; Langary, D.; Nikoloski, Z.: A structural property for reduction of biochemical networks. Scientific Reports 11 (1), 17415 (2021)
52.
Zeitschriftenartikel
Mbebi, A. J.; Tong, H.; Nikoloski, Z.: L2,1-norm regularized multivariate regression model with applications to genomic prediction. Bioinformatics 37 (18), S. 2896 - 2904 (2021)
53.
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Omranian, N.; Angeleska, A.; Nikoloski, Z.: PC2P: parameter-free network-based prediction of protein complexes. Bioinformatics 37 (1), S. 73 - 81 (2021)
54.
Zeitschriftenartikel
Pries, C.; Razaghi-Moghadam, Z.; Kopka, J.; Nikoloski, Z.: Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Scientific Reports 11 (1), 4787 (2021)
55.
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Razaghi-Moghadam, Z.; Sokolowska, E.; Sowa, M.A.; Skirycz, A.; Nikoloski, Z.: Combination of network and molecule structure accurately predicts competitive inhibitory interactions. Computational and Structural Biotechnology Journal 19, S. 2170 - 2178 (2021)
56.
Zeitschriftenartikel
Seep, L.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Scientific Reports 11, 8544 (2021)
57.
Zeitschriftenartikel
Tong, H.; Nikoloski, Z.: Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. Journal of Plant Physiology 257, 153354 (2021)
58.
Zeitschriftenartikel
Nowak, J.; Gennermann, K.; Persson, S.; Nikoloski, Z.: CytoSeg 2.0: automated extraction of actin filaments. Bioinformatics 36 (9), S. 2950 - 2951 (2020)
59.
Zeitschriftenartikel
Clemente-Moreno, M.; Omranian, N.; Sáez, P.; Figueroa, C.; Del-Saz, N.; Elso, M.; Poblete, L.; Orf, I.; Cuadros-Inostroza, A.; Cavieres, L.A. et al.; Bravo, L.; Fernie, A. R.; Ribas-Carbó, M.; Flexas, J.; Nikoloski, Z.; Brotman, Y.; Gago, J.: Low temperature tolerance of the Antarctic species Deschampsia antarctica: a complex metabolic response associated with nutrient remobilization. Plant, Cell and Environment 43 (6), S. 1376 - 1393 (2020)
60.
Zeitschriftenartikel
Correa, S. M.; Alseekh, S.; Atehortúa, L.; Brotman, Y.; Ríos-Estepa, R.; Fernie, A. R.; Nikoloski, Z.: Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways. The Plant Journal (angenommen)
61.
Zeitschriftenartikel
Correa, S. M.; Fernie, A. R.; Nikoloski, Z.; Brotman, Y.: Towards model-driven characterization and manipulation of plant lipid metabolism. Progress in Lipid Research 80, 101051 (2020)
62.
Zeitschriftenartikel
Küken, A.; Gennermann, K.; Nikoloski, Z.: Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana. The Plant Journal 103 (6), S. 2168 - 2177 (2020)
63.
Zeitschriftenartikel
Lyall, R.; Nikoloski, Z.; Gechev, T.: Comparative analysis of ros network genes in extremophile eukaryotes. International Journal of Molecular Sciences 21 (23), S. 1 - 27 (2020)
64.
Zeitschriftenartikel
Razaghi-Moghadam, Z.; Nikoloski, Z.: Supervised Learning of Gene Regulatory Networks. Current Protocols in Plant Biology 5 (2), e20106 (2020)
65.
Zeitschriftenartikel
Robaina-Estévez, S.; Nikoloski, Z.: Flux-based hierarchical organization of Escherichia coli’s metabolic network. PLoS Computational Biology 16 (4), e1007832 (2020)
66.
Zeitschriftenartikel
Tong, H.; Küken, A.; Nikoloski, Z.: Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth. Nature Communications 11 (1), 2410 (2020)
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