A. Zelezniak

发表

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

J. Nielsen, Gang Li, Yating Hu, 2020, Nature Communications.

Learning the Regulatory Code of Gene Expression

A. Zelezniak, Jan Zrimec, Filip Buric, 2021, Frontiers in Molecular Biosciences.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

V. Verendel, V. Siewers, A. Zelezniak, 2020, Nature communications.

Biochemical principles enabling metabolic cooperativity and phenotypic heterogeneity at the single cell level

A. Zelezniak, Kate Campbell, M. Ralser, 2018 .

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

Christoph B. Messner, M. Keller, B. Klaus, 2018, Cell systems.

Functional Metabolomics Describes the Yeast Biosynthetic Regulome

A. Zelezniak, M. Ralser, M. Mülleder, 2016, Cell.

The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization

M. Keller, B. Luisi, A. Zelezniak, 2017, Nature Communications.

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

Benjamín J. Sánchez, J. Nielsen, A. Zelezniak, 2020, bioRxiv.

Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies

P. Bork, G. Zeller, Michael Kuhn, 2018, Nature Microbiology.

Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

Alison S. Waller, P. Bork, J. Raes, 2012, Molecular systems biology.

Metabolic dependencies drive species co-occurrence in diverse microbial communities

Peer Bork, Kiran Raosaheb Patil, Aleksej Zelezniak, 2015, Proceedings of the National Academy of Sciences.

Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity

P. Bork, F. Hildebrand, D. Allainé, 2019, Current Biology.

Transcriptional regulation and steady-state modeling of metabolic networks

K. Patil, A. Zelezniak, M. Kielland-Brandt, 2013 .

Resource Functional Metabolomics Describes the Yeast Biosynthetic Regulome

A. Zelezniak, M. Ralser, M. Mülleder, 2016 .

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

Christoph B. Messner, R. Marioni, M. Keller, 2020, medRxiv.

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

K. Patil, A. Zelezniak, Francisco Zorrilla, 2021, bioRxiv.

Precise label-free quantitative proteomes in high-throughput by microLC and data-independent SWATH acquisition

Aleksej Zelezniak, Roland Bruderer, Lukas Reiter, 2016, bioRxiv.

Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends

A. Zelezniak, Jan Zrimec, Mariia Kokina, 2020, bioRxiv.

Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells.

Jonathan L. Robinson, F. Edfors, Johan Rockberg, 2022, Cell reports.

Transcriptome analysis of EPO and GFP HEK293 Cell-lines Reveal Shifts in Energy and ER Capacity Support Improved Erythropoietin Production in HEK293F Cells

Jonathan L. Robinson, J. Nielsen, Johan Rockberg, 2020, bioRxiv.

Controlling gene expression with deep generative design of regulatory DNA

Devdatt P. Dubhashi, Morteza Haghir Chehreghani, Nora K. Speicher, 2022, Nature Communications.

Bridging the gap between Transcriptome and Metabolome by using Genome-scale Metabolic Models

K. Patil, A. Zelezniak, 2009 .

Review Process File

Peer Bork, Kiran Raosaheb Patil, Jeroen Raes, 2012 .

Data mining of Saccharomyces cerevisiae mutants engineered for increased tolerance towards inhibitors in lignocellulosic hydrolysates.

L. Olsson, A. Zelezniak, Jan Zrimec, 2022, Biotechnology advances.

Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes

Kiran Raosaheb Patil, Aleksej Zelezniak, Steven Sheridan, 2014, PLoS Comput. Biol..