Author

E. Ozdemir

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 2 Biophysics· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Approach:experimental assay· 2 Approach:statistical model· 2 Research Focus:cell signaling & trafficking· 1 Organism:E. coli· 1 Organism:human· 1 Research Focus:protein folding & dynamics· 1 Assay:proteomics· 1

Frequent Co-Authors

D. Wirth· 2 shared K. Hristova· 2 shared A. Feist· 1 shared A. S. L. Hansen· 1 shared B. O. Palsson· 1 shared C. Dalldorf· 1 shared C. Lieven· 1 shared D. C. Zielinski· 1 shared

Activity by Year

2017-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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2 shared papersPharmacology & Toxicology

D. Wirth

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Quantification of ligand and mutation-induced bias in EGFR phosphorylation in direct response to ligand binding

D. Wirth, E. Ozdemir, K. Hristova

The paper quantifies ligand and mutation-induced bias in EGFR phosphorylation directly after ligand binding, showing that both factors influence RTK signaling without feedback loop contributions.

bioRxiv · Feb 13, 2023Pharmacology & Toxicology

Plasticity of transmembrane helix interactions in EphA2 dimers and oligomers

D. Wirth, E. Ozdemir, W. C. Wimley, E. B. Pasquale, K. Hristova

The paper investigates how EphA2 transmembrane helix mutations affect receptor-receptor interactions using FRET and FIF, finding plasticity in dimer interfaces rather than a binary switch between active/inactive states.

bioRxiv · Jun 6, 2022 · PreprintBiophysics

Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

P. V. Phaneuf, D. C. Zielinski, J. T. Yurkovich, J. Johnsen, R. Szubin, L. Yang, S. H. Kim, S. Schulz, M. Wu, C. Dalldorf, E. Ozdemir, B. O. Palsson, A. Feist

This paper presents a data-driven workflow to design microbial strains using aggregated ALE mutational data, revealing gene-centric mutations that significantly alter cellular states.

bioRxiv · Jul 19, 2021Bioengineering

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

J. Cardoso, K. Jensen, C. Lieven, A. S. L. Hansen, S. Galkina, M. E. Beber, E. Ozdemir, M. Herrgard, H. Redestig, N. Sonnenschein

Cameo provides an open-source Python platform for in silico design of cell factories, offering tools for metabolic engineering strategies and optimization.

bioRxiv · Jun 9, 2017Bioengineering