Arend, M.; Yuan, Y.; Ruiz-Sola, M.Á.; Omranian, N.; Nikoloski, Z.; Petroutsos, D.: Widening the landscape of transcriptional regulation of green algal photoprotection. Nature Communications 14 (1), 2687 (2023)

Langary, D.; Küken, A.; Nikoloski, Z.: The unraveling of balanced complexes in metabolic networks. Scientific Reports 13 (1), 5712 (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 (1), 986 (2023)

Küken, A.; Treves, H.; 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, 1140829 (2023)

Moura Ferreira, M.A.D.; Wendering, P.; Arend, M.; Batista da Silveira, W.; Nikoloski, Z.: Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning. Metabolic Engineering 80, S. 184 - 192 (2023)

Pratx, L.; Wendering, P.; Kappel, C.; Nikoloski, Z.; Bäurle, I.: Histone retention preserves epigenetic marks during heat stress-induced transcriptional memory in plants. The EMBO Journal 42, e113595 (2023)

von Bismarck, T.; Wendering, P.; de Souza, L. P.; Ruß, J.; Strandberg, L.; Heyneke, E.; Walker, B. J.; Schöttler, M. A.; Fernie, A. R.; Nikoloski, Z. et al.; Armbruster, U.: Growth in fluctuating light buffers plants against photorespiratory perturbations. Nature Communications 14 (1), 7052 (2023)

Ferreira, M. A. d. M.; Silveira, W. B. d.; Nikoloski, Z.: PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. PLoS Computational Biology 19 (10), e1011549 (2023)

Tong, H.; Laitinen, R.A.E.; Nikoloski, Z.: Predicting plasticity of rosette growth and metabolic fluxes in Arabidopsis thaliana. New Phytologist 240 (1), S. 426 - 438 (2023)

Bezold, F.; Scheffer, J.; Wendering, P.; Razaghi-Moghadam, Z.; Trauth, J.; Pook, B.; Nußhär, H.; Hasenjäger, S.; Nikoloski, Z.; Essen, L.-O. et al.; Taxis, C.: Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast. Metabolic Engineering 79, S. 97 - 107 (2023)

Hashemi, S.; 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 (9), e1011489 (2023)

Langary, D.; Küken, A.; Nikoloski, Z.: The effective deficiency of biochemical networks. Scientific Reports 13, 14589 (2023)

Arend, M.; Zimmer, D.; Xu, R.; Sommer, F.; Mühlhaus, T.; Nikoloski, Z.: Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale. Nature Communications 14 (1), 4781 (2023)

Correa Cordoba, S.; Tong, H.; Burgos, A.; Zhu, F.; 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 (1), 4897 (2023)

Babadi, F. S.; Razaghi-Moghadam, Z.; Zare-Mirakabad, F.; Nikoloski, Z.: Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling. Bioinformatics advances 3 (1), vbad098 (2023)

Mbebi, A. J.; Nikoloski, Z.: Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection. PLoS Computational Biology 19 (7), e1010832 (2023)

Hashemi, S.; Laitinen, R.; Nikoloski, Z.: Models and molecular mechanisms for trade-offs in the context of metabolism. Molecular Ecology 33 (10), e16879 (2023)

Wendering, P.; Nikoloski, Z.: Toward mechanistic modeling and rational engineering of plant respiration. Plant Physiology 191 (4), S. 2150 - 2166 (2023)

Campana, P.; Nikoloski, Z.: Self- and cross-attention accurately predicts metabolite–protein interactions. NAR: genomics and bioinformatics 5 (1), lqad008 (2023)

Wendering, P.; Arend, M.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications 14, 1485 (2023)