Publications of Z. Nikoloski

Langary, D., Küken, A. & Nikoloski, Z. The unraveling of balanced complexes in metabolic networks. Scientific Reports 13, (2023).

Tan, Q. W., Lim, P. K., Chen, Z., Pasha, A., Provart, N., Arend, M., Nikoloski, Z. & Mutwil, M. Cross-stress gene expression atlas of Marchantia polymorpha reveals the hierarchy and regulatory principles of abiotic stress responses. Nature Communications 14, (2023).

Campana, P. A. & Nikoloski, Z. Self- and cross-attention accurately predicts metabolite–protein interactions. NAR: genomics and bioinformatics 5, (2023).

Wendering, P., Arend, M., Razaghi-Moghadam, Z. & Nikoloski, Z. Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications 14, (2023).

Águila Ruiz-Sola, M., Flori, S., Yuan, Y., Villain, G., Sanz-Luque, E., Redekop, P., Tokutsu, R., Küken, A., Tsichla, A., Kepesidis, G., Allorent, G., Arend, M., Iacono, F., Finazzi, G., Hippler, M., Nikoloski, Z., Minagawa, J., Grossman, A. R. & Petroutsos, D. Light-independent regulation of algal photoprotection by CO2 availability. Nature Communications 14, (2023).

Andreou, G. M., Messer, M., Tong, H., Nikoloski, Z. & Laitinen, R. A. E. Heritability of temperature-mediated flower size plasticity in Arabidopsis thaliana. Quantitative Plant Biology 4, (2023).

Hashemi, S., Laitinen, R. & Nikoloski, Z. Models and molecular mechanisms for trade-offs in the context of metabolism. Molecular Ecology (2023). doi:10.1111/mec.16879

Omranian, N. & Nikoloski, Z. CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs. Applied Network Science 7, (2022).

Wiszniewski, A. A. G., Uberegui, E., Messer, M., Sultanova, G., Borghi, M., Duarte, G. T., Vicente, R., Sageman-Furnas, K., Fernie, A. R., Nikoloski, Z. & Laitinen, R. A. E. Temperature-mediated flower size plasticity in Arabidopsis. iScience 25, (2022).

Laitinen, R. A. E. & Nikoloski, Z. Strategies to identify and dissect trade-offs in plants. Molecular Ecology (2022). doi:10.1111/mec.16780

Orf, I., Tenenboim, H., Omranian, N., Nikoloski, Z., Fernie, A. R., Lisec, J., Brotman, Y. & Bromke, M. A. Transcriptomic and Metabolomic Analysis of a Pseudomonas-Resistant versus a Susceptible Arabidopsis Accession. International Journal of Molecular Sciences 23, (2022).

Huß, S., Judd, R. S., Koper, K., Maeda, H. A. & Nikoloski, Z. An automated workflow that generates atom mappings for large scale metabolic models and its application to Arabidopsis thaliana. The Plant Journal 111, 1486–1500 (2022).

Mbebi, A. J., Breitler, J.-C., Bordeaux, M., Sulpice, R., McHale, M., Tong, H., Toniutti, L., Castillo, J. A., Bertrand, B. & Nikoloski, Z. A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids. G3: Genes, Genomes, Genetics 12, (2022).

Matz, T., Wang, Y., Kulshreshtha, R., Sampathkumar, A. & Nikoloski, Z. Topological properties accurately predict cell division events and organization of shoot apical meristem in Arabidopsis thaliana. Development 149, (2022).

Hashemi, S., Razaghi-Moghadam, Z., Laitinen, R. A. E. & Nikoloski, Z. Relative flux trade-offs and optimization of metabolic network functionalities. Computational and Structural Biotechnology Journal 20, 3963–3971 (2022).

Liu, Z., Østerlund, I., Ruhnow, F., Cao, Y., Huang, G., Cai, W., Zhang, J., Liang, W., Nikoloski, Z., Persson, S. & Zhang, D. Fluorescent cytoskeletal markers reveal associations between the actin and microtubule cytoskeleton in rice cells. Development 149, (2022).

Küken, A., Langary, D. & Nikoloski, Z. The hidden simplicity of metabolic networks is revealed by multireaction dependencies. Science Advances 8, (2022).

Tong, H., Nankar, A. N., Liu, J., Todorova, V., Ganeva, D., Grozeva, S., Tringovska, I., Pasev, G., Radeva-Ivanova, V., Gechev, T., Kostova, D. & Nikoloski, Z. Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits. Horticulture Research 9, (2022).

Wendering, P. & Nikoloski, Z. COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions. PLoS Computational Biology 18, (2022).

Wendering, P. & Nikoloski, Z. Genome-Scale Modeling Specifies the Metabolic Capabilities of Rhizophagus irregularis. mSystems 7, (2022).