John J. Tyson

22.4k total citations · 3 hit papers
263 papers, 15.3k citations indexed

About

John J. Tyson is a scholar working on Molecular Biology, Cell Biology and Computer Networks and Communications. According to data from OpenAlex, John J. Tyson has authored 263 papers receiving a total of 15.3k indexed citations (citations by other indexed papers that have themselves been cited), including 176 papers in Molecular Biology, 74 papers in Cell Biology and 43 papers in Computer Networks and Communications. Recurrent topics in John J. Tyson's work include Gene Regulatory Network Analysis (123 papers), Microtubule and mitosis dynamics (69 papers) and Fungal and yeast genetics research (51 papers). John J. Tyson is often cited by papers focused on Gene Regulatory Network Analysis (123 papers), Microtubule and mitosis dynamics (69 papers) and Fungal and yeast genetics research (51 papers). John J. Tyson collaborates with scholars based in United States, United Kingdom and Hungary. John J. Tyson's co-authors include Béla Novák, Katherine Chen, James P. Keener, Attila Csikász‐Nagy, Paul C. Fife, Andrea Ciliberto, Kathy Chen, Béla Györffy, Robert Clarke and William T. Baumann and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

John J. Tyson

255 papers receiving 14.8k citations

Hit Papers

Sniffers, buzzers, toggle... 1988 2026 2000 2013 2003 2008 1988 250 500 750 1000

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
John J. Tyson 10.0k 3.0k 2.6k 2.0k 1.4k 263 15.3k
Albert Goldbeter 8.0k 0.8× 2.4k 0.8× 1.5k 0.6× 1.8k 0.9× 930 0.6× 180 12.6k
Lee A. Segel 4.4k 0.4× 1.5k 0.5× 1.9k 0.7× 908 0.4× 1.4k 1.0× 142 11.8k
Hans G. Othmer 4.0k 0.4× 1.2k 0.4× 1.9k 0.7× 965 0.5× 859 0.6× 139 8.2k
Stanislas Leibler 16.2k 1.6× 676 0.2× 3.0k 1.1× 1.9k 0.9× 5.3k 3.7× 118 23.9k
Michael B. Elowitz 18.8k 1.9× 690 0.2× 1.1k 0.4× 1.3k 0.6× 5.3k 3.7× 94 21.9k
Shalev Itzkovitz 11.1k 1.1× 628 0.2× 1.4k 0.5× 2.5k 1.2× 2.2k 1.6× 106 20.4k
James E. Ferrell 12.3k 1.2× 416 0.1× 3.4k 1.3× 571 0.3× 1.4k 1.0× 129 16.7k
Lev S. Tsimring 5.5k 0.5× 3.4k 1.1× 406 0.2× 4.1k 2.0× 1.5k 1.0× 175 14.5k
Daniel T. Gillespie 11.3k 1.1× 760 0.2× 640 0.2× 3.0k 1.5× 3.5k 2.5× 78 19.0k
Peter G. Wolynes 26.5k 2.6× 464 0.2× 2.9k 1.1× 3.2k 1.6× 1.9k 1.3× 439 43.0k

Countries citing papers authored by John J. Tyson

Since Specialization
Citations

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

Fields of papers citing papers by John J. Tyson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Tyson. 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 John J. Tyson. The network helps show where John J. Tyson may publish in the future.

Co-authorship network of co-authors of John J. Tyson

This figure shows the co-authorship network connecting the top 25 collaborators of John J. Tyson. A scholar is included among the top collaborators of John J. Tyson 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 John J. Tyson. John J. Tyson 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.
Tyson, John J., et al.. (2024). Stochastic Boolean model of normal and aberrant cell cycles in budding yeast. npj Systems Biology and Applications. 10(1). 121–121.
2.
Tyson, John J., et al.. (2024). Newton's cradle: Cell cycle regulation by two mutually inhibitory oscillators. Mathematical Biosciences. 377. 109291–109291.
3.
Novák, Béla & John J. Tyson. (2024). The bistable mitotic switch in fission yeast. Molecular Biology of the Cell. 35(6). ar77–ar77. 1 indexed citations
4.
Alizadeh, Elaheh, Janani Ravi, Arminja N. Kettenbach, et al.. (2023). Feedback in the β-catenin destruction complex imparts bistability and cellular memory. Proceedings of the National Academy of Sciences. 120(2). 9 indexed citations
5.
Chen, Jing, et al.. (2022). Mathematical analysis of robustness of oscillations in models of the mammalian circadian clock. PLoS Computational Biology. 18(3). e1008340–e1008340. 12 indexed citations
6.
Mosig, Rebecca, Nobuya Koike, John J. Tyson, et al.. (2021). Natural antisense transcript of Period2, Per2AS, regulates the amplitude of the mouse circadian clock. Genes & Development. 35(11-12). 899–913. 8 indexed citations
7.
Gotoh, Tetsuya, Jae Kyoung Kim, Jingjing Liu, et al.. (2016). Model-driven experimental approach reveals the complex regulatory distribution of p53 by the circadian factor Period 2. Proceedings of the National Academy of Sciences. 113(47). 13516–13521. 72 indexed citations
8.
Zhang, Tongli, John J. Tyson, & Béla Novák. (2013). Role for regulated phosphatase activity in generating mitotic oscillations in Xenopus cell-free extracts. Proceedings of the National Academy of Sciences. 110(51). 20539–20544. 5 indexed citations
9.
Chen, Chun, William T. Baumann, Robert Clarke, & John J. Tyson. (2013). Modeling the estrogen receptor to growth factor receptor signaling switch in human breast cancer cells. FEBS Letters. 587(20). 3327–3334. 21 indexed citations
10.
Kapuy, Orsolya, et al.. (2009). System‐level feedbacks control cell cycle progression. FEBS Letters. 583(24). 3992–3998. 28 indexed citations
11.
Randhawa, Ranjit, Clifford A. Shaffer, & John J. Tyson. (2007). Fusing and composing macromolecular regulatory network models. Spring Simulation Multiconference. 337–344. 3 indexed citations
12.
Sauro, Herbert M., David Harel, Adelinde M. Uhrmacher, et al.. (2006). Challenges for modeling and simulation methods in systems biology. Winter Simulation Conference. 1720–1730. 13 indexed citations
13.
Shaffer, Clifford A., Ranjit Randhawa, & John J. Tyson. (2006). The role of composition and aggregation in modeling macromolecular regulatory networks. Winter Simulation Conference. 1628–1636. 6 indexed citations
14.
Chen, Katherine, Laurence Calzone, Attila Csikász‐Nagy, et al.. (2004). Integrative Analysis of Cell Cycle Control in Budding Yeast. Molecular Biology of the Cell. 15(8). 3841–3862. 444 indexed citations
15.
Gulati, Ranjay, et al.. (2004). Cómo los CEO gestionan sus agendas de crecimiento. Harvard business review. 82(7). 76–85. 14 indexed citations
16.
Sveiczer, Ákos, Attila Csikász‐Nagy, John J. Tyson, & Béla Novák. (2003). A deterministic molecular model of the fission yeast cell cycle.. Yeast. 20. 1 indexed citations
17.
Tyson, John J., et al.. (2001). CyberYeast: A computational model of cell cycle regulation in budding yeast. The FASEB Journal. 15. 1 indexed citations
18.
Tyson, John J., et al.. (1998). Bifurcation Analysis of a Model of Mitotic Control in Frog Eggs. Journal of Theoretical Biology. 195(1). 69–85. 82 indexed citations
19.
Novák, Béla, Attila Csikász‐Nagy, Béla Györffy, & John J. Tyson. (1997). Model scenarios for evolution of the eukaryotic cell cycle. European Journal of Cell Biology. 72. 12–12. 1 indexed citations
20.
Martell, Charles & John J. Tyson. (1983). QWL Strategies: Quality Circles.. The Journal of Academic Librarianship. 9(5). 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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