Michael Chevalier

1.6k total citations · 1 hit paper
15 papers, 1.1k citations indexed

About

Michael Chevalier is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, Michael Chevalier has authored 15 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 3 papers in Cellular and Molecular Neuroscience and 2 papers in Cell Biology. Recurrent topics in Michael Chevalier's work include Gene Regulatory Network Analysis (7 papers), Receptor Mechanisms and Signaling (4 papers) and Single-cell and spatial transcriptomics (3 papers). Michael Chevalier is often cited by papers focused on Gene Regulatory Network Analysis (7 papers), Receptor Mechanisms and Signaling (4 papers) and Single-cell and spatial transcriptomics (3 papers). Michael Chevalier collaborates with scholars based in United States, Denmark and Italy. Michael Chevalier's co-authors include Hana El‐Samad, Mark von Zastrow, Søren G. F. Rasmussen, Jacob P. Mahoney, Roshanak Irannejad, Roger K. Sunahara, Jan Steyaert, Bo Huang, Eelco van Anken and Tomás Aragón and has published in prestigious journals such as Nature, The Journal of Chemical Physics and Journal of Neuroscience.

In The Last Decade

Michael Chevalier

15 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Conformational biosensors reveal GPCR signalling from endosomes

2013 608 citations Roshanak Irannejad, Michael Chevalier et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael Chevalier United States	9	867	379	327	124	114	15	1.1k
Braden T. Lobingier United States	11	830 1.0×	609 1.6×	269 0.8×	85 0.7×	83 0.7×	18	1.2k
Stefania Mariggiò Italy	22	1.1k 1.3×	355 0.9×	332 1.0×	57 0.5×	34 0.3×	41	1.5k
Miriam Stoeber Switzerland	12	872 1.0×	639 1.7×	252 0.8×	109 0.9×	65 0.6×	16	1.2k
Sebastian Breuer Germany	15	749 0.9×	171 0.5×	268 0.8×	72 0.6×	48 0.4×	25	1.3k
Michelle Illing Canada	9	1.3k 1.5×	284 0.7×	734 2.2×	72 0.6×	59 0.5×	10	1.6k
Negin P. Martin United States	21	865 1.0×	204 0.5×	287 0.9×	39 0.3×	139 1.2×	40	1.2k
Fritz Benseler Germany	20	1.6k 1.8×	258 0.7×	166 0.5×	52 0.4×	61 0.5×	42	1.8k
Changwook Lee South Korea	19	947 1.1×	428 1.1×	66 0.2×	102 0.8×	69 0.6×	43	1.3k
Alex Laude United Kingdom	15	809 0.9×	221 0.6×	150 0.5×	52 0.4×	28 0.2×	25	1.1k
Baruch Z. Harris United States	10	833 1.0×	352 0.9×	153 0.5×	78 0.6×	22 0.2×	11	1.2k

Countries citing papers authored by Michael Chevalier

Since Specialization

Citations

This map shows the geographic impact of Michael Chevalier'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 Michael Chevalier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Chevalier more than expected).

Fields of papers citing papers by Michael Chevalier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Chevalier. 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 Michael Chevalier. The network helps show where Michael Chevalier may publish in the future.

Co-authorship network of co-authors of Michael Chevalier

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Chevalier. A scholar is included among the top collaborators of Michael Chevalier 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 Michael Chevalier. Michael Chevalier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Chevalier, Michael, Salvatore Cataldo, Alberto Pettignano, et al.. (2025). Harnessing redox reactions for anticancer effects: A copper(II) Schiff base complex induces apoptosis in HepG2 liver cancer cells via ROS generation. Journal of Inorganic Biochemistry. 270. 112938–112938. 6 indexed citations

Aslankoohi, Elham, et al.. (2022). Analysis of localized cAMP perturbations within a tissue reveal the effects of a local, dynamic gap junction state on ERK signaling. PLoS Computational Biology. 18(3). e1009873–e1009873. 5 indexed citations

Chevalier, Michael, Lukasz J. Bugaj, Taylor H. Nguyen, et al.. (2020). Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics. Cell Systems. 11(4). 336–353.e24. 30 indexed citations

Chevalier, Michael, Mariana Gómez-Schiavon, Andrew H. Ng, & Hana El‐Samad. (2019). Design and Analysis of a Proportional-Integral-Derivative Controller with Biological Molecules. Cell Systems. 9(4). 338–353.e10. 61 indexed citations

Chevalier, Michael, Ophelia S. Venturelli, & Hana El‐Samad. (2015). The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks. PLoS Computational Biology. 11(10). e1004462–e1004462. 8 indexed citations

Chevalier, Michael, et al.. (2014). Imaging of kiss-and-run exocytosis of surface receptors in neuronal cultures. Frontiers in Cellular Neuroscience. 8. 363–363. 13 indexed citations

Stewart-Ornstein, Jacob, et al.. (2014). Using Dynamic Noise Propagation to Infer Causal Regulatory Relationships in Biochemical Networks. ACS Synthetic Biology. 4(3). 258–264. 13 indexed citations

Chevalier, Michael & Hana El‐Samad. (2014). A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions. The Journal of Chemical Physics. 141(21). 5 indexed citations

Irannejad, Roshanak, Michael Chevalier, Jacob P. Mahoney, et al.. (2013). Conformational biosensors reveal GPCR signalling from endosomes. Nature. 495(7442). 534–538. 608 indexed citations breakdown →

10.

Chevalier, Michael & Hana El‐Samad. (2012). Towards a minimal stochastic model for a large class of diffusion-reactions on biological membranes. The Journal of Chemical Physics. 137(8). 84103–84103. 3 indexed citations

11.

Chevalier, Michael & Hana El‐Samad. (2011). A data-integrated method for analyzing stochastic biochemical networks. The Journal of Chemical Physics. 135(21). 214110–214110. 5 indexed citations

12.

Pincus, David, Michael Chevalier, Tomás Aragón, et al.. (2010). BiP Binding to the ER-Stress Sensor Ire1 Tunes the Homeostatic Behavior of the Unfolded Protein Response. PLoS Biology. 8(7). e1000415–e1000415. 339 indexed citations

13.

Yu, Yang, et al.. (2010). Rapid Delivery of Internalized Signaling Receptors to the Somatodendritic Surface by Sequence-Specific Local Insertion. Journal of Neuroscience. 30(35). 11703–11714. 30 indexed citations

14.

Chevalier, Michael & Hana El‐Samad. (2009). A rigorous framework for multiscale simulation of stochastic cellular networks. The Journal of Chemical Physics. 131(5). 54102–54102. 9 indexed citations

15.

Chevalier, Michael. (1997). Occupational Therapy and the Search for Meaning. British Journal of Occupational Therapy. 60(12). 539–540. 2 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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