Mary J. Dunlop

3.8k total citations
60 papers, 2.3k citations indexed

About

Mary J. Dunlop is a scholar working on Molecular Biology, Genetics and Biophysics. According to data from OpenAlex, Mary J. Dunlop has authored 60 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Molecular Biology, 19 papers in Genetics and 16 papers in Biophysics. Recurrent topics in Mary J. Dunlop's work include Gene Regulatory Network Analysis (26 papers), Bacterial Genetics and Biotechnology (15 papers) and CRISPR and Genetic Engineering (13 papers). Mary J. Dunlop is often cited by papers focused on Gene Regulatory Network Analysis (26 papers), Bacterial Genetics and Biotechnology (15 papers) and CRISPR and Genetic Engineering (13 papers). Mary J. Dunlop collaborates with scholars based in United States, France and Germany. Mary J. Dunlop's co-authors include Imane El Meouche, Jean‐Baptiste Lugagne, Jay D. Keasling, Aindrila Mukhopadhyay, Michael B. Elowitz, Robert Sidney Cox, Haonan Lin, Hou Cheng Chu, Taek Soon Lee and Zain Y. Dossani and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Mary J. Dunlop

60 papers receiving 2.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mary J. Dunlop United States 24 1.8k 567 484 325 202 60 2.3k
David Karig United States 24 1.8k 1.0× 405 0.7× 551 1.1× 95 0.3× 141 0.7× 39 2.6k
Jonas Warringer Sweden 33 2.7k 1.5× 763 1.3× 276 0.6× 80 0.2× 134 0.7× 78 3.5k
Anat Bren Israel 23 2.0k 1.1× 1.2k 2.0× 235 0.5× 103 0.3× 101 0.5× 31 2.5k
Robin Ghosh Germany 21 2.2k 1.2× 501 0.9× 294 0.6× 50 0.2× 165 0.8× 84 2.9k
Teuta Piližota United Kingdom 21 771 0.4× 281 0.5× 405 0.8× 77 0.2× 77 0.4× 38 1.4k
Alexander Grünberger Germany 33 2.0k 1.1× 350 0.6× 1.3k 2.7× 169 0.5× 18 0.1× 106 3.0k
Dietrich Kohlheyer Germany 30 1.4k 0.8× 259 0.5× 1.4k 3.0× 166 0.5× 20 0.1× 82 2.7k
Cynthia H. Collins United States 20 2.2k 1.2× 637 1.1× 780 1.6× 115 0.4× 18 0.1× 29 2.8k
Sara Petersen Bjørn Denmark 13 1.4k 0.7× 324 0.6× 189 0.4× 136 0.4× 36 0.2× 17 1.6k
Zhongge Zhang United States 22 3.3k 1.8× 1.9k 3.4× 367 0.8× 77 0.2× 152 0.8× 50 4.1k

Countries citing papers authored by Mary J. Dunlop

Since Specialization
Citations

This map shows the geographic impact of Mary J. Dunlop's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mary J. Dunlop with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mary J. Dunlop more than expected).

Fields of papers citing papers by Mary J. Dunlop

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary J. Dunlop. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mary J. Dunlop. The network helps show where Mary J. Dunlop may publish in the future.

Co-authorship network of co-authors of Mary J. Dunlop

This figure shows the co-authorship network connecting the top 25 collaborators of Mary J. Dunlop. A scholar is included among the top collaborators of Mary J. Dunlop based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mary J. Dunlop. Mary J. Dunlop is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tague, Nathan, et al.. (2024). Light-inducible protein degradation in E. coli with the LOVdeg tag. eLife. 12. 7 indexed citations
2.
Lugagne, Jean‐Baptiste, et al.. (2024). Deep model predictive control of gene expression in thousands of single cells. Nature Communications. 15(1). 2148–2148. 10 indexed citations
3.
Tague, Nathan, et al.. (2023). Controlled Protein Activities with Viral Proteases, Antiviral Peptides, and Antiviral Drugs. ACS Chemical Biology. 18(5). 1228–1236. 3 indexed citations
4.
Stan, Guy‐Bart, et al.. (2022). Deep Learning Concepts and Applications for Synthetic Biology. PubMed. 1(4). 360–371. 13 indexed citations
5.
Andreani, Virgile, et al.. (2022). Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response. Proceedings of the National Academy of Sciences. 119(14). e2115032119–e2115032119. 42 indexed citations
6.
Lin, Haonan, Hyeon Jeong Lee, Nathan Tague, et al.. (2021). Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning. Nature Communications. 12(1). 3052–3052. 89 indexed citations
7.
Tague, Nathan, et al.. (2020). Programmable gene regulation for metabolic engineering using decoy transcription factor binding sites. Nucleic Acids Research. 49(2). 1163–1172. 28 indexed citations
8.
Wong, Wilson W., et al.. (2020). Light-Inducible Recombinases for Bacterial Optogenetics. ACS Synthetic Biology. 9(2). 227–235. 41 indexed citations
9.
Westbrook, Alexandra, Xun Tang, Ryan Marshall, et al.. (2019). Distinct timescales of RNA regulators enable the construction of a genetic pulse generator. Biotechnology and Bioengineering. 116(5). 1139–1151. 40 indexed citations
10.
Meouche, Imane El & Mary J. Dunlop. (2018). Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation. Science. 362(6415). 686–690. 164 indexed citations
11.
Agrawal, Deepak K., Xun Tang, Alexandra Westbrook, et al.. (2018). Mathematical Modeling of RNA-Based Architectures for Closed Loop Control of Gene Expression. ACS Synthetic Biology. 7(5). 1219–1228. 30 indexed citations
12.
Wen, Xi, et al.. (2018). Antibiotic export by efflux pumps affects growth of neighboring bacteria. Scientific Reports. 8(1). 15120–15120. 22 indexed citations
13.
Dunlop, Mary J., et al.. (2017). Stress Introduction Rate Alters the Benefit of AcrAB-TolC Efflux Pumps. Journal of Bacteriology. 200(1). 29 indexed citations
14.
García-Bernardo, Javier & Mary J. Dunlop. (2016). Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins. Biophysical Journal. 110(10). 2278–2287. 8 indexed citations
15.
García-Bernardo, Javier & Mary J. Dunlop. (2015). Noise and Low-Level Dynamics Can Coordinate Multicomponent Bet Hedging Mechanisms. Biophysical Journal. 108(1). 184–193. 12 indexed citations
16.
Batth, Tanveer S., William J. Turner, Harvey W. Blanch, et al.. (2014). Development of a Native Escherichia coli Induction System for Ionic Liquid Tolerance. PLoS ONE. 9(7). e101115–e101115. 30 indexed citations
17.
Dunlop, Mary J., et al.. (2012). Synthetic Feedback Loop Model for Increasing Microbial Biofuel Production Using a Biosensor. Frontiers in Microbiology. 3. 360–360. 33 indexed citations
18.
Dunlop, Mary J., et al.. (2012). Modeling suggests that gene circuit architecture controls phenotypic variability in a bacterial persistence network. BMC Systems Biology. 6(1). 47–47. 13 indexed citations
19.
Cox, Robert Sidney, Mary J. Dunlop, & Michael B. Elowitz. (2010). A synthetic three-color scaffold for monitoring genetic regulation and noise. Journal of Biological Engineering. 4(1). 10–10. 58 indexed citations
20.
Dunlop, Mary J., Robert Sidney Cox, Joseph Levine, Richard M. Murray, & Michael B. Elowitz. (2008). Regulatory activity revealed by dynamic correlations in gene expression noise. Nature Genetics. 40(12). 1493–1498. 168 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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