Author

D. C. Zielinski

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· 1 Evolutionary Biology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

B. O. Palsson· 3 shared R. Szubin· 2 shared A. Feist· 1 shared A. V. Sastry· 1 shared C. Dalldorf· 1 shared D. Heckmann· 1 shared E. Ozdemir· 1 shared H. Bergsten· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersMicrobiology

B. O. Palsson

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MicrobiologybioRxivAug 4, 2022

Elucidation of independently modulated genes in Streptococcus pyogenes reveals carbon sources that control its expression of hemolytic toxins

Y. Hirose, S. Poudel, A. V. Sastry…·V. Nizet

The paper identifies carbon sources that regulate hemolytic toxin expression in Streptococcus pyogenes using transcriptome-wide independent component analysis.

DOI 10.1128/msystems.00247-23

bacteria (other)bulk RNA-seq

𝕏 PostBluesky

BioengineeringbioRxivJul 19, 2021

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

P. V. Phaneuf, D. C. Zielinski, J. T. Yurkovich…·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.

DOI 10.1021/acssynbio.1c00337

E. colistatistical model

𝕏 PostBluesky

Evolutionary BiologybioRxivMay 10, 2018

Modeling genome-wide evolution of catalytic turnover rates: Strong epistasis shaped modern enzyme kinetics

D. Heckmann, D. C. Zielinski, B. O. Palsson

The paper models the evolution of enzyme turnover rates using genome-scale metabolic and population genetics models, finding strong epistasis shapes kcat evolution and limits adaptation.

DOI 10.1038/s41467-018-07649-1

statistical model

𝕏 PostBluesky