Ramon Grima

5.7k total citations
106 papers, 2.9k citations indexed

About

Ramon Grima is a scholar working on Molecular Biology, Genetics and Statistical and Nonlinear Physics. According to data from OpenAlex, Ramon Grima has authored 106 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Molecular Biology, 27 papers in Genetics and 15 papers in Statistical and Nonlinear Physics. Recurrent topics in Ramon Grima's work include Gene Regulatory Network Analysis (83 papers), Evolution and Genetic Dynamics (21 papers) and Single-cell and spatial transcriptomics (13 papers). Ramon Grima is often cited by papers focused on Gene Regulatory Network Analysis (83 papers), Evolution and Genetic Dynamics (21 papers) and Single-cell and spatial transcriptomics (13 papers). Ramon Grima collaborates with scholars based in United Kingdom, United States and China. Ramon Grima's co-authors include Santiago Schnell, Zhixing Cao, Philipp Thomas, Stephen Smith, Chen Jia, Arthur V. Straube, Nikola Popović, T. J. Newman, Philip K. Maini and Christian Fleck and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nucleic Acids Research.

In The Last Decade

Ramon Grima

104 papers receiving 2.9k citations

Author Peers

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

Author Last Decade Papers Cites
Ramon Grima 2.2k 601 408 287 264 106 2.9k
José M. G. Vilar 1.9k 0.8× 667 1.1× 944 2.3× 118 0.4× 306 1.2× 60 3.1k
Timothy C. Elston 5.2k 2.3× 1.2k 2.1× 654 1.6× 618 2.2× 554 2.1× 137 6.7k
Abhyudai Singh 3.8k 1.7× 1.1k 1.8× 299 0.7× 351 1.2× 245 0.9× 221 4.8k
Timothy S. Gardner 5.8k 2.6× 1.3k 2.1× 378 0.9× 361 1.3× 698 2.6× 28 6.9k
Radek Erban 1.3k 0.6× 279 0.5× 414 1.0× 128 0.4× 305 1.2× 71 2.2k
Brian Munsky 2.1k 0.9× 495 0.8× 150 0.4× 295 1.0× 137 0.5× 64 2.4k
Sandeep Krishna 1.7k 0.7× 672 1.1× 225 0.6× 78 0.3× 101 0.4× 87 2.5k
Annick Lesne 1.5k 0.7× 240 0.4× 372 0.9× 46 0.2× 112 0.4× 102 2.6k
Marco Cosentino Lagomarsino 1.7k 0.8× 823 1.4× 214 0.5× 101 0.4× 983 3.7× 115 3.4k
Mukund Thattai 3.7k 1.6× 1.5k 2.5× 387 0.9× 354 1.2× 292 1.1× 40 4.1k

Countries citing papers authored by Ramon Grima

Since Specialization
Citations

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

Fields of papers citing papers by Ramon Grima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramon Grima

This figure shows the co-authorship network connecting the top 25 collaborators of Ramon Grima. A scholar is included among the top collaborators of Ramon Grima 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 Ramon Grima. Ramon Grima 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.
Grima, Ramon, et al.. (2025). Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data. Nucleic Acids Research. 53(7). 3 indexed citations
2.
Szavits-Nossan, Juraj, et al.. (2025). Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression. Nature Communications. 16(1). 2833–2833. 3 indexed citations
3.
Jia, Chen & Ramon Grima. (2024). Holimap: an accurate and efficient method for solving stochastic gene network dynamics. Nature Communications. 15(1). 6557–6557. 8 indexed citations
4.
Wu, Bingjie, et al.. (2024). Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition. The Journal of Chemical Physics. 160(7). 4 indexed citations
5.
Szavits-Nossan, Juraj & Ramon Grima. (2023). Uncovering the effect of RNA polymerase steric interactions on gene expression noise: Analytical distributions of nascent and mature RNA numbers. Physical review. E. 108(3). 34405–34405. 6 indexed citations
6.
Öcal, Kaan, Guido Sanguinetti, & Ramon Grima. (2023). Model reduction for the Chemical Master Equation: An information-theoretic approach. The Journal of Chemical Physics. 158(11). 114113–114113. 4 indexed citations
7.
Szavits-Nossan, Juraj & Ramon Grima. (2023). Steady-state distributions of nascent RNA for general initiation mechanisms. Physical Review Research. 5(1). 17 indexed citations
8.
Singh, Abhyudai, et al.. (2023). The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. Science Advances. 9(32). eadh5138–eadh5138. 23 indexed citations
9.
Fu, Xiaoming, et al.. (2022). DelaySSAToolkit.jl: stochastic simulation of reaction systems with time delays in Julia. Bioinformatics. 38(17). 4243–4245. 7 indexed citations
10.
Fu, Xiaoming, Heta Patel, Stefano Coppola, et al.. (2022). Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions. eLife. 11. 32 indexed citations
11.
Jia, Chen, Abhyudai Singh, & Ramon Grima. (2022). Characterizing non-exponential growth and bimodal cell size distributions in fission yeast: An analytical approach. PLoS Computational Biology. 18(1). e1009793–e1009793. 12 indexed citations
12.
Perez‐Carrasco, Rubén, et al.. (2020). Effects of cell cycle variability on lineage and population measurements of messenger RNA abundance. Journal of The Royal Society Interface. 17(168). 20200360–20200360. 43 indexed citations
13.
Grima, Ramon, et al.. (2018). A stochastic model of corneal epithelium maintenance and recovery following perturbation. Journal of Mathematical Biology. 78(5). 1245–1276. 2 indexed citations
14.
Voliotis, Margaritis, Philipp Thomas, Ramon Grima, & Clive G. Bowsher. (2016). Stochastic Simulation of Biomolecular Networks in Dynamic Environments. PLoS Computational Biology. 12(6). e1004923–e1004923. 45 indexed citations
15.
Fröhlich, Fabian, et al.. (2016). Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion. PLoS Computational Biology. 12(7). e1005030–e1005030. 50 indexed citations
16.
Grima, Ramon. (2014). Anomalous fluctuation scaling laws in stochastic enzyme kinetics: Increase of noise strength with the mean concentration. Physical Review E. 89(1). 12710–12710. 1 indexed citations
17.
Wenden, Bénédicte, et al.. (2012). Spontaneous spatiotemporal waves of gene expression from biological clocks in the leaf. Proceedings of the National Academy of Sciences. 109(17). 6757–6762. 83 indexed citations
18.
Thomas, Philipp, Hannes Matuschek, & Ramon Grima. (2012). Intrinsic Noise Analyzer: A Software Package for the Exploration of Stochastic Biochemical Kinetics Using the System Size Expansion. PLoS ONE. 7(6). e38518–e38518. 29 indexed citations
19.
Grima, Ramon & Santiago Schnell. (2007). Can tissue surface tension drive somite formation?. Developmental Biology. 307(2). 248–257. 25 indexed citations
20.
Grima, Ramon. (2007). Multiscale Modeling of Biological Pattern Formation. Current topics in developmental biology. 81. 435–460. 26 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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