Author

Marius Arend

University of PotsdamORCID 0000-0002-9608-4960Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:E. coli· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Organism:yeast· 2 Research Focus:benchmarking· 1 Organism:C. elegans· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Zoran NikoloskiiD· 3 shared Philipp WenderingiD· 2 shared David M. ZimmeriD· 1 shared Frederik SommeriD· 1 shared Maurício Alexander de Moura FerreiraiD· 1 shared Rudan XuiD· 1 shared Timo MühlhausiD· 1 shared Wendel Batista da SilveiraiD· 1 shared

Activity by Year

2023-2023 · peak 3/yr

Co-Author Collaboration Network

9 top collaborators · 18 mutual co-author links

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3 shared papersBioinformatics

Zoran NikoloskiORCID

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3 papers

Recent·Most cited

Accurate prediction of in vivo protein abundances by coupling constraint-based modeling and machine learning

Maurício Alexander de Moura Ferreira, Philipp Wendering, Marius Arend, Wendel Batista da Silveira, Zoran Nikoloski

CAMEL predicts protein abundances by combining constraint-based modeling and machine learning, achieving high accuracy in E. coli and moderate performance in S. cerevisiae.

Metabolic Engineering · Oct 5, 2023Peer-reviewedBioinformatics

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale

Marius Arend, David M. Zimmer, Rudan Xu, Frederik Sommer, Timo Mühlhaus, Zoran Nikoloski

The paper integrates proteomics and constraint-based modeling to estimate in vivo enzyme turnover numbers for Chlamydomonas reinhardtii, improving metabolic model predictions.

Nature Communications · Aug 8, 2023Peer-reviewedSystems Biology

Data integration across conditions improves turnover number estimates and metabolic predictions

Philipp Wendering, Marius Arend, Zahra Razaghi‐Moghadam, Zoran Nikoloski

The paper shows that integrating data improves turnover number estimates and metabolic predictions for E. coli and S. cerevisiae.

Nature Communications · Mar 17, 2023Peer-reviewedBioinformatics