Eugenio Cinquemani

959 total citations
51 papers, 610 citations indexed

About

Eugenio Cinquemani is a scholar working on Molecular Biology, Control and Systems Engineering and Genetics. According to data from OpenAlex, Eugenio Cinquemani has authored 51 papers receiving a total of 610 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 13 papers in Control and Systems Engineering and 11 papers in Genetics. Recurrent topics in Eugenio Cinquemani's work include Gene Regulatory Network Analysis (33 papers), Microbial Metabolic Engineering and Bioproduction (22 papers) and Bioinformatics and Genomic Networks (12 papers). Eugenio Cinquemani is often cited by papers focused on Gene Regulatory Network Analysis (33 papers), Microbial Metabolic Engineering and Bioproduction (22 papers) and Bioinformatics and Genomic Networks (12 papers). Eugenio Cinquemani collaborates with scholars based in France, Switzerland and Italy. Eugenio Cinquemani's co-authors include John Lygeros, Debasish Chatterjee, Mayank Agarwal, Giancarlo Ferrari‐Trecate, Hidde de Jong, Peter Hokayem, Federico Ramponi, Jean‐Luc Gouzé, Delphine Ropers and Daniel Kahn and has published in prestigious journals such as Nature Communications, Bioinformatics and IEEE Transactions on Automatic Control.

In The Last Decade

Eugenio Cinquemani

50 papers receiving 589 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eugenio Cinquemani France 15 287 260 65 43 39 51 610
King Wai Lau United Kingdom 11 282 1.0× 42 0.2× 49 0.8× 23 0.5× 110 2.8× 20 581
Robert Flassig Germany 13 202 0.7× 62 0.2× 15 0.2× 9 0.2× 34 0.9× 24 410
Maria Davidich Germany 6 395 1.4× 40 0.2× 62 1.0× 5 0.1× 31 0.8× 7 548
De-Shuang Huang China 15 682 2.4× 56 0.2× 34 0.5× 6 0.1× 173 4.4× 36 981
Jacek Mazurkiewicz Poland 10 322 1.1× 29 0.1× 21 0.3× 13 0.3× 33 0.8× 40 596
Federica Ciocchetta United Kingdom 9 259 0.9× 15 0.1× 31 0.5× 45 1.0× 42 1.1× 21 379
Wook-Dong Kim South Korea 13 203 0.7× 59 0.2× 44 0.7× 8 0.2× 152 3.9× 39 539
Sean T. McQuade United States 8 42 0.1× 238 0.9× 9 0.1× 10 0.2× 8 0.2× 19 414
Cheng‐Wei Hsieh Taiwan 7 203 0.7× 19 0.1× 66 1.0× 9 0.2× 188 4.8× 14 521

Countries citing papers authored by Eugenio Cinquemani

Since Specialization
Citations

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

Fields of papers citing papers by Eugenio Cinquemani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eugenio Cinquemani

This figure shows the co-authorship network connecting the top 25 collaborators of Eugenio Cinquemani. A scholar is included among the top collaborators of Eugenio Cinquemani 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 Eugenio Cinquemani. Eugenio Cinquemani 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.
Cinquemani, Eugenio, et al.. (2025). Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population. Nature Communications. 16(1). 285–285. 5 indexed citations
2.
Cinquemani, Eugenio, et al.. (2024). Kalman-based approaches for online estimation of bioreactor dynamics from fluorescent reporter measurements. SPIRE - Sciences Po Institutional REpository. 844–849. 2 indexed citations
3.
Martínez, Carlos A., Eugenio Cinquemani, Hidde de Jong, & Jean‐Luc Gouzé. (2023). Optimal protein production by a synthetic microbial consortium: coexistence, distribution of labor, and syntrophy. Journal of Mathematical Biology. 87(1). 23–23.
4.
Cinquemani, Eugenio, et al.. (2022). Maturation models of fluorescent proteins are necessary for unbiased estimates of promoter activity. Biophysical Journal. 121(21). 4179–4188. 3 indexed citations
5.
Cinquemani, Eugenio, et al.. (2021). Identification of stochastic gene expression models over lineage trees. IFAC-PapersOnLine. 54(7). 150–155. 1 indexed citations
6.
Gouzé, Jean‐Luc, et al.. (2020). Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs. PLoS Computational Biology. 16(4). e1007795–e1007795. 20 indexed citations
7.
Lavielle, Marc, et al.. (2019). Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data. Bioinformatics. 35(14). i586–i595. 6 indexed citations
8.
Jong, Hidde de, et al.. (2017). Mathematical modelling of microbes: metabolism, gene expression and growth. Journal of The Royal Society Interface. 14(136). 20170502–20170502. 40 indexed citations
9.
10.
Cinquemani, Eugenio, Valérie Laroute, Muriel Cocaign‐Bousquet, Hidde de Jong, & Delphine Ropers. (2017). Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data. Bioinformatics. 33(14). i301–i310. 8 indexed citations
11.
Versari, Cristian, et al.. (2016). What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast. PLoS Computational Biology. 12(2). e1004706–e1004706. 55 indexed citations
12.
13.
Brilli, Matteo, et al.. (2012). On the identifiability of metabolic network models. Journal of Mathematical Biology. 67(6-7). 1795–1832. 26 indexed citations
14.
Hokayem, Peter, Eugenio Cinquemani, Debasish Chatterjee, & John Lygeros. (2010). Stochastic MPC with output feedback and bounded control inputs. arXiv (Cornell University). 2 indexed citations
15.
Hokayem, Peter, John Lygeros, Debasish Chatterjee, & Eugenio Cinquemani. (2010). Stochastic MPC with Imperfect State Information and Bounded Controls. HAL (Le Centre pour la Communication Scientifique Directe). 429–434. 4 indexed citations
16.
Chatterjee, Debasish, et al.. (2009). On stochastic control up to a hitting time. 67. 531–536. 3 indexed citations
17.
Cinquemani, Eugenio, Andreas Milias‐Argeitis, & John Lygeros. (2008). Identification of Genetic Regulatory Networks: A Stochastic Hybrid Approach. IFAC Proceedings Volumes. 41(2). 301–306. 2 indexed citations
18.
Cinquemani, Eugenio, Andreas Milias‐Argeitis, Sean Summers, & John Lygeros. (2008). Stochastic dynamics of genetic networks: modelling and parameter identification. Bioinformatics. 24(23). 2748–2754. 18 indexed citations
19.
Cinquemani, Eugenio, et al.. (2007). A general framework for the identification of jump Markov linear systems. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2034. 5737–5742. 14 indexed citations
20.
Cinquemani, Eugenio, Mario Micheli, & Giorgio Picci. (2004). Fault detection in a class of stochastic hybrid systems. 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). 2623. 3197–3203 Vol.3. 6 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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