Publikationen von Z. Nikoloski
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Zeitschriftenartikel (195)
1.
Zeitschriftenartikel
Gupta, S., , , Mueller-Roeber, B., Fernie, A. R., Nikoloski, Z. & The genome of Haberlea rhodopensis provides insights into the mechanisms for tolerance to multiple extreme environments. Cellular and Molecular Life Sciences 81, (2024).
2.
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Razaghi-Moghadam, Z. & Nikoloski, Z. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metabolic Engineering 82, 216–224 (2024).
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Arend, M., , , Omranian, N., Nikoloski, Z. & Widening the landscape of transcriptional regulation of green algal photoprotection. Nature Communications 14, (2023).
4.
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Langary, D., & Nikoloski, Z. The unraveling of balanced complexes in metabolic networks. Scientific Reports 13, (2023).
5.
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Arend, M., Nikoloski, Z. & Cross-stress gene expression atlas of Marchantia polymorpha reveals the hierarchy and regulatory principles of abiotic stress responses. Nature Communications 14, (2023).
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Küken, A., & Nikoloski, Z. A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae. Frontiers in Plant Science 14, (2023).
7.
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Wendering, P., Arend, M., & Nikoloski, Z. Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning. Metabolic Engineering 80, 184–192 (2023).
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Wendering, P., , Nikoloski, Z. & Histone retention preserves epigenetic marks during heat stress-induced transcriptional memory in plants. The EMBO Journal 42, (2023).
, 9.
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von Bismarck, T., Wendering, P., de Souza, L. P., Ruß, J., Strandberg, L., Heyneke, E., , Schöttler, M. A., Fernie, A. R., Nikoloski, Z. & Armbruster, U. Growth in fluctuating light buffers plants against photorespiratory perturbations. Nature Communications 14, (2023).
10.
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Nikoloski, Z. PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. PLoS Computational Biology 19, (2023).
, & 11.
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Tong, H., & Nikoloski, Z. Predicting plasticity of rosette growth and metabolic fluxes in Arabidopsis thaliana. New Phytologist 240, 426–438 (2023).
12.
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Wendering, P., Razaghi-Moghadam, Z., , , , , Nikoloski, Z., & Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast. Metabolic Engineering 79, 97–107 (2023).
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Razaghi-Moghadam, Z. & Nikoloski, Z. Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony. PLoS Computational Biology 19, (2023).
, 14.
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Langary, D., & Nikoloski, Z. The effective deficiency of biochemical networks. Scientific Reports 13, (2023).
15.
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Arend, M., , Xu, R., , & Nikoloski, Z. Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale. Nature Communications 14, (2023).
16.
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Correa Cordoba, S., Tong, H., , , Alseekh, S., Fernie, A. R. & Nikoloski, Z. Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism. Nature Communications 14, (2023).
17.
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Mbebi, A. J. & Nikoloski, Z. Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection. PLoS Computational Biology 19, (2023).
18.
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Nikoloski, Z. Self- and cross-attention accurately predicts metabolite–protein interactions. NAR: genomics and bioinformatics 5, (2023).
& 19.
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Wendering, P., Arend, M., Razaghi-Moghadam, Z. & Nikoloski, Z. Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications 14, (2023).
20.
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Küken, A., , , , Arend, M., , , , Nikoloski, Z., , & Light-independent regulation of algal photoprotection by CO2 availability. Nature Communications 14, (2023).
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