Daniel Zielinski
Affiliations: | 2015 | Bioengineering | University of California, San Diego, La Jolla, CA |
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"Daniel Zielinski"
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| Josephs-Spaulding J, Rajput A, Hefner Y, et al. (2024) Reconstructing the transcriptional regulatory network of probiotic is enabled by transcriptomics and machine learning. Msystems. e0125723 |
| Qiu S, Wan X, Liang Y, et al. (2024) Inferred regulons are consistent with regulator binding sequences in E. coli. Plos Computational Biology. 20: e1011824 |
| Mattei G, Gan Z, Ramazzotti M, et al. (2023) Differential Expression Analysis Utilizing Condition-Specific Metabolic Pathways. Metabolites. 13 |
| Lamoureux CR, Decker KT, Sastry AV, et al. (2023) A multi-scale expression and regulation knowledge base for Escherichia coli. Nucleic Acids Research |
| Chowdhury S, Zielinski DC, Dalldorf C, et al. (2023) Empowering drug off-target discovery with metabolic and structural analysis. Nature Communications. 14: 3390 |
| Hirose Y, Poudel S, Sastry AV, et al. (2023) Elucidation of independently modulated genes in reveals carbon sources that control its expression of hemolytic toxins. Msystems. e0024723 |
| Dalldorf C, Rychel K, Szubin R, et al. (2023) The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions. Research Square |
| Phaneuf PV, Zielinski DC, Yurkovich JT, et al. (2021) Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data. Acs Synthetic Biology |
| Akbari A, Yurkovich JT, Zielinski DC, et al. (2021) The quantitative metabolome is shaped by abiotic constraints. Nature Communications. 12: 3178 |
| Haiman ZB, Zielinski DC, Koike Y, et al. (2021) MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics. Plos Computational Biology. 17: e1008208 |