Veröffentlichungen 2021

Universität Potsdam - Institut für Biochemie und Biologie - AG Bioinformatik - Veröffentlichungen – 2021

Aarabi F, Rakpenthai A, Barahimipour R, Gorka M, Alseekh S, Zhang, Y, Salem M. A, Brückner F, Omranian N, Watanabe, M, 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 Physiol, 2021 Aug; kiab386

Angeleska A, Omranian N, Nikoloski Z.
Coherent network partitions: Characterizations with cographs and prime graphs.
Theoretical computer science, 2021 Oct.

Beleggia R, Omranian N, Holtz Y, Gioia T,Razaghi-Moghadam, Z, Nikoloski, Z.
GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level.
Bioinformatics, 2021 Jun; 37(12):1717-1723

Duarte G. T, Pandey P. K, Vaid N, Alseekh S, Fernie A. R, Nikoloski Z, Laitinen R. A. E.
Plasticity of rosette size in response to nitrogen availability is controlled by an R C C 1-family protein.
Plant Cell Environ, 2021 Oct; 44(10):3398--3411

Eng R. C, Schneider R, Matz T. W, 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.
Curr Biol, 2021 Aug; 31 (15):3262–3274

Fiorani F, Nigro FM, Pecchioni N, De Vita P, Schurr U, David JL, Nikoloski Z, Papa R .
Comparative Analysis Based on Transcriptomics and Metabolomics Data Reveal Differences between Emmer and Durum Wheat in Response to Nitrogen Starvation.
Int J Mol Sci, 2021 Apr; 22(9)

Hashemi S, Zahra Razaghi-Moghadam Z, Nikoloski Z.
Identification of flux trade-offs in metabolic networks
Scientific Reports. 2021 Dec; 11:23776

Küken A, Wendering P, Langary D,  Nikoloski, Z.
A structural property for reduction of biochemical networks.
Sci Rep, 2021 Aug; 11(1):17415

Mbebi AJ, Tong H, Nikoloski Z.
L2,1-norm regularized multivariate regression model with applications to genomic prediction.
Bioinformatics, 2021 Sep: 37(18):2896-2904

Moreno J. C, Rojas B. E, Vicente R, Gorka M, Matz T, Chodasiewicz M, Peralta-Ariza J. S,  Zhang, Y, Alseekh S, Childs D, 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.
MBO J, 2021 Aug; 40(15):e106800

Nowak J, Eng RC, Matz T, Waack M, Persson S, Sampathkumar A, Nikoloski Z.
A network-based framework for shape analysis enables accurate characterization of leaf epidermal cells.
Nat Commun, 2021 Jan; 12(1):458

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, pp. 5255 - 5263 (2021)

Omranian S, Angeleska A, Nikoloski, Z.
PC2P: Parameter-free network-based prediction of protein complexes.
Bioinformatics, 2021 Jan; 37(1):73-81

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.
Sci Rep, 2021 Feb; 11(1):4787

Razaghi-Moghadam Z, Sokolowska E. M, Sowa M. A, Skirycz A, Nikoloski Z.
Combination of network and molecule structure accurately predicts competitive inhibitory interactions.
Comput Struct Biotechnol J, 2021; 19:2170-2178

Seep L, Razaghi-Moghadam Z, Nikoloski Z.
Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis.
Sci Rep, 2021 Apr; 11(1):8544

Tong H, Küken A, Razaghi-Moghadam Z, Nikoloski Z.
Characterization of effects of genetic variants via genome-scale metabolic modelling.
Cell Mol Life Sci, 2021 Jun;78(12):5123-5138

Tong H, Nikoloski, Z.
Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data.
J Plant Physiol, 2021 Feb; 257:153354

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, 2021 Aug.

Zhu F, Alseekh S, Koper K, Tong H, Nikoloski Z, Naake T,  Liu H, Yan J, Brotman Y, Wen W, Maeda H, Cheng Y, Fernie A.R.
Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis.
The Plant Cell, 2021 Oct; koab251

Zimmer D, Swart C, Graf A, Arrivault S, Tillich M, Proost S, Nikoloski Z, Stitt M, Bock R, Mühlhaus T, Boulouis A.
Topology of the redox network during induction of photosynthesis as revealed by time-resolved proteomics in tobacco.
Science advances 7 (51), 2021 Dec; eabi8307