Author

David Adam

Max Planck Institute for Terrestrial Microbiology

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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Synthetic Biology· 2

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Amir PandiiD· 2 shared Elizaveta BobkovaiD· 2 shared Tobias J. ErbiD· 2 shared Yeganeh ForoughijabbariiD· 2 shared Ali Yazdizadeh KharraziiD· 1 shared Amir ZareiD· 1 shared Anna Lena JungiD· 1 shared Bernd SchmeckiD· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersSynthetic Biology

Amir PandiORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

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