Aleksej Zelezniak
Aleksej Zelezniak
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Metabolic dependencies drive species co-occurrence in diverse microbial communities
A Zelezniak, S Andrejev, O Ponomarova, DR Mende, P Bork, KR Patil
Proceedings of the National Academy of Sciences 112 (20), 6449-6454, 2015
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection
CB Messner, V Demichev, D Wendisch, L Michalick, M White, A Freiwald, ...
Cell systems 11 (1), 11-24. e4, 2020
Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies
M Tramontano, S Andrejev, M Pruteanu, M Klünemann, M Kuhn, ...
Nature microbiology 3 (4), 514-522, 2018
Functional metabolomics describes the yeast biosynthetic regulome
M Mülleder, E Calvani, MT Alam, RK Wang, F Eckerstorfer, A Zelezniak, ...
Cell 167 (2), 553-565. e12, 2016
Expanding functional protein sequence spaces using generative adversarial networks
D Repecka, V Jauniskis, L Karpus, E Rembeza, I Rokaitis, J Zrimec, ...
Nature Machine Intelligence, 1-10, 2021
Ultra-fast proteomics with Scanning SWATH
CB Messner, V Demichev, N Bloomfield, JSL Yu, M White, M Kreidl, ...
Nature biotechnology 39 (7), 846-854, 2021
Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes
A Zelezniak, TH Pers, S Soares, ME Patti, KR Patil
PLoS computational biology 6 (4), e1000729, 2010
The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization
MT Alam, V Olin-Sandoval, A Stincone, MA Keller, A Zelezniak, BF Luisi, ...
Nature communications 8 (1), 16018, 2017
Designing and interpreting ‘multi-omic’experiments that may change our understanding of biology
R Haas, A Zelezniak, J Iacovacci, S Kamrad, SJ Townsend, M Ralser
Current Opinion in Systems Biology 6, 37-45, 2017
A time-resolved proteomic and prognostic map of COVID-19
V Demichev, P Tober-Lau, O Lemke, T Nazarenko, C Thibeault, ...
Cell systems 12 (8), 780-794. e7, 2021
The metabolic background is a global player in Saccharomyces gene expression epistasis
MT Alam, A Zelezniak, M Mülleder, P Shliaha, R Schwarz, F Capuano, ...
Nature Microbiology 1 (3), 1-10, 2016
Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803
A Montagud, A Zelezniak, E Navarro, PF de Córdoba, JF Urchueguía, ...
Biotechnology journal 6 (3), 330-342, 2011
Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes
A Zelezniak, S Sheridan, KR Patil
PLoS computational biology 10 (4), e1003572, 2014
Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts
A Zelezniak, J Vowinckel, F Capuano, CB Messner, V Demichev, ...
Cell systems 7 (3), 269-283. e6, 2018
Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition
J Vowinckel, A Zelezniak, R Bruderer, M Mülleder, L Reiter, M Ralser
Scientific reports 8 (1), 4346, 2018
Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
J Zrimec, CS Börlin, F Buric, AS Muhammad, R Chen, V Siewers, ...
Nature communications 11 (1), 1-16, 2020
Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours
T Yamada, AS Waller, J Raes, A Zelezniak, N Perchat, A Perret, ...
Molecular systems biology 8 (1), 581, 2012
Plastic-degrading potential across the global microbiome correlates with recent pollution trends
J Zrimec, M Kokina, S Jonasson, F Zorrilla, A Zelezniak
MBio 12 (5), e02155-21, 2021
Bayesian genome scale modelling identifies thermal determinants of yeast metabolism
G Li, Y Hu, J Zrimec, H Luo, H Wang, A Zelezniak, B Ji, J Nielsen
Nature Communications 12 (1), 190, 2021
metaGEM: reconstruction of genome scale metabolic models directly from metagenomes
F Zorrilla, F Buric, KR Patil, A Zelezniak
Nucleic acids research 49 (21), e126-e126, 2021
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