Author

D. Surujon

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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Microbiology· 3

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Organism:Arabidopsis· 1 Research Focus:chromatin & epigenetic regulation· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Approach:genetic perturbation / CRISPR· 1

Frequent Co-Authors

T. van Opijnen· 3 shared A. Farinha· 1 shared A. Pavao· 1 shared A. Y. Tang· 1 shared C. D. Johnston· 1 shared C. Rock· 1 shared D. S. Jones· 1 shared E. Geisinger· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersMicrobiology

T. van Opijnen

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Author profile →

3 papers

Recent·Most cited

A bacterial pan-genome makes gene essentiality strain-dependent and evolvable

F. Rosconi, E. Rudmann, J. Li, D. Surujon, J. Anthony, M. Frank, D. S. Jones, C. Rock, J. W. Rosch, C. D. Johnston, T. van Opijnen

The paper investigates how a bacterial pan-genome affects gene essentiality and shows that essential genes can evolve to become non-essential through genetic changes and mechanisms like functional redundancy and pathway rewiring.

bioRxiv · Feb 8, 2022Microbiology

Essential gene analysis in Acinetobacter baumannii by high-density transposon mutagenesis and CRISPR interference

J. Bai, Y. Dai, A. Farinha, A. Y. Tang, S. Syal, G. Vargas-Cuebas, D. Surujon, R. R. Isberg, T. van Opijnen, E. Geisinger

This study identifies essential genes in Acinetobacter baumannii using transposon mutagenesis and a CRISPRi system, revealing new factors and conditionally essential processes.

bioRxiv · Sep 15, 2020Microbiology

Forecasting bacterial survival-success and adaptive evolution through multi-omics stress response-mapping, network analyses and machine learning

Z. Zhu, D. Surujon, A. Pavao, J. Bento, T. van Opijnen

This paper predicts bacterial survival and adaptive evolution using multi-omics data, network analysis, and machine learning to identify coordinated responses and gene adaptation.

bioRxiv · Aug 9, 2018 · PreprintMicrobiology