Author

Elizaveta Bobkova

Max Planck Institute for Terrestrial MicrobiologyORCID 0000-0002-8779-6069Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Synthetic Biology· 3

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Tobias J. ErbiD· 3 shared Amir PandiiD· 2 shared David Adam· 2 shared Frank AbendrothiD· 2 shared Gerhard HummeriD· 2 shared Olalla VázqueziD· 2 shared Scott A. ScholziD· 2 shared Yeganeh ForoughijabbariiD· 2 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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3 shared papersSynthetic Biology

Tobias J. ErbORCID

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3 papers

Recent·Most cited

Chemically tunable permeability of engineered alpha-Hemolysin in synthetic cells

Elisabeth Bobkova, Anastasia Goetz, Frank Abendroth, Olalla Vázquez, Zakarya Benayad, Viktorija Dujmović, Luis F. Gutiérrez-Mondragón, Scott A. Scholz, Gerhard Hummer, Tobias J. Erb

This paper develops a method to tune nanopore permeability using chemically modified alpha-hemolysin in synthetic cells.

bioRxiv · May 26, 2026 · PreprintSynthetic Biology

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Amir Pandi, David Adam, Amir Zare, Van Tuan Trinh, Stefan L. Schaefer, Marie Burt, Björn Klabunde, Elizaveta Bobkova, Manish Kushwaha, Yeganeh Foroughijabbari, Peter R. Braun, Christoph K. Spahn, Christian Preußer, Elke Pogge von Strandmann, Helge B. Bode, , Tobias J. Erb

Autoencoder design plus a roughly 24-hour cell-free screen tested 500 candidates over five rounds, finding 30 functional AMPs; six were broad-spectrum with no resistance emerging in E. coli.

Nature Communications · Nov 8, 2023Peer-reviewedSynthetic Biology✦ In-depth digest

A versatile active learning workflow for optimization of genetic and metabolic networks

Amir Pandi, Christoph Diehl, Ali Yazdizadeh Kharrazi, Scott A. Scholz, Elizaveta Bobkova, Léon Faure, Maren Nattermann, David Adam, Nils Chapin, Yeganeh Foroughijabbari, Charles Moritz, Nicole Paczia, Niña Socorro Cortina, Jean‐Loup Faulon, Tobias J. Erb

The paper introduces METIS, an active learning workflow for optimizing genetic and metabolic networks, demonstrating significant improvements in efficiency with minimal experiments.

Nature Communications · Jul 5, 2022Peer-reviewedSynthetic Biology