Paul Macklin

6.4k total citations
57 papers, 2.9k citations indexed

About

Paul Macklin is a scholar working on Modeling and Simulation, Oncology and Molecular Biology. According to data from OpenAlex, Paul Macklin has authored 57 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Modeling and Simulation, 21 papers in Oncology and 20 papers in Molecular Biology. Recurrent topics in Paul Macklin's work include Mathematical Biology Tumor Growth (28 papers), Cancer Cells and Metastasis (16 papers) and Cellular Mechanics and Interactions (11 papers). Paul Macklin is often cited by papers focused on Mathematical Biology Tumor Growth (28 papers), Cancer Cells and Metastasis (16 papers) and Cellular Mechanics and Interactions (11 papers). Paul Macklin collaborates with scholars based in United States, United Kingdom and France. Paul Macklin's co-authors include John Lowengrub, Vittorio Cristini, Randy Heiland, Thomas S. Deisboeck, Zhihui Wang, Samuel H. Friedman, Ahmadreza Ghaffarizadeh, Hermann B. Frieboes, Steven M. Wise and John Metzcar and has published in prestigious journals such as Science, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Paul Macklin

52 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Paul Macklin United States	24	1.7k	1.0k	868	865	453	57	2.9k
Hermann B. Frieboes United States	33	1.9k 1.1×	1.1k 1.1×	1.2k 1.4×	860 1.0×	1.0k 2.3×	118	4.4k
Andreas Deutsch Germany	35	1.2k 0.7×	1.4k 1.4×	555 0.6×	1.1k 1.2×	885 2.0×	127	4.4k
Dirk Drasdo Germany	33	1.3k 0.8×	1.1k 1.1×	749 0.9×	1.4k 1.6×	792 1.7×	61	3.3k
Thomas S. Deisboeck United States	35	2.0k 1.2×	1.8k 1.8×	1.1k 1.3×	1.0k 1.2×	525 1.2×	88	4.4k
Katarzyna A. Rejniak United States	24	936 0.5×	588 0.6×	726 0.8×	734 0.8×	465 1.0×	52	2.0k
Trachette L. Jackson United States	25	950 0.6×	1.0k 1.0×	779 0.9×	530 0.6×	240 0.5×	70	2.5k
Haralampos Hatzikirou Germany	23	754 0.4×	520 0.5×	360 0.4×	411 0.5×	234 0.5×	69	1.8k
Robyn P. Araujo Australia	18	638 0.4×	1.1k 1.1×	395 0.5×	380 0.4×	232 0.5×	48	2.0k
Yao-Li Chuang United States	16	823 0.5×	409 0.4×	323 0.4×	326 0.4×	263 0.6×	28	1.9k
Heiko Enderling United States	34	1.6k 0.9×	792 0.8×	1.4k 1.6×	525 0.6×	213 0.5×	118	3.2k

Countries citing papers authored by Paul Macklin

Since Specialization

Citations

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

Fields of papers citing papers by Paul Macklin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Macklin

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shmulevich, Ilya, et al.. (2024). A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins. npj Systems Biology and Applications. 10(1). 144–144. 6 indexed citations

Heiland, Randy, et al.. (2024). PhysiCell Studio: a graphical tool to make agent-based modeling more accessible. SHILAP Revista de lepidopterología. 2024. 1–19. 4 indexed citations

Heiland, Randy, et al.. (2024). Building multiscale models with PhysiBoSS, an agent-based modeling tool. Briefings in Bioinformatics. 25(6). 1 indexed citations

Laubenbacher, Reinhard, Gary An, Filippo Castiglione, et al.. (2024). Forum on immune digital twins: a meeting report. npj Systems Biology and Applications. 10(1). 19–19. 6 indexed citations

Sarabipour, Sarvenaz, Paul Macklin, & Natalie M. Niemi. (2024). Improving academic mentorship practices. Nature Human Behaviour. 8(7). 1228–1231. 1 indexed citations

Macklin, Paul, et al.. (2023). An agent-based modeling approach for lung fibrosis in response to COVID-19. PLoS Computational Biology. 19(12). e1011741–e1011741. 10 indexed citations

Macklin, Paul, et al.. (2022). OpenACC Acceleration of an Agent-Based Biological Simulation Framework. Computing in Science & Engineering. 24(5). 53–63. 4 indexed citations

Madamanchi, Aasakiran, et al.. (2021). Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a Mathematical Oncology Research Lab. PRIMUS. 32(3). 446–467. 1 indexed citations

Godet, Inês, et al.. (2021). A persistent invasive phenotype in post-hypoxic tumor cells is revealed by fate mapping and computational modeling. iScience. 24(9). 102935–102935. 23 indexed citations

10.

Wang, Yafei, et al.. (2021). Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach. Scientific Reports. 11(1). 1710–1710. 20 indexed citations

11.

Fertig, Elana J., Elizabeth M. Jaffee, Paul Macklin, Vered Stearns, & Chenguang Wang. (2021). Forecasting cancer: from precision to predictive medicine. Med. 2(9). 1004–1010. 12 indexed citations

12.

Heiland, Randy, et al.. (2019). xml2jupyter: Mapping parameters between XML and Jupyter widgets. The Journal of Open Source Software. 4(39). 1408–1408. 10 indexed citations

13.

Letort, Gaëlle, Arnau Montagud, Gautier Stoll, et al.. (2018). PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling. Bioinformatics. 35(7). 1188–1196. 73 indexed citations

14.

Ozik, Jonathan, Nicholson Collier, Justin M. Wozniak, et al.. (2018). High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. BMC Bioinformatics. 19(S18). 483–483. 44 indexed citations

15.

Macklin, Paul, Hermann B. Frieboes, Jessica L. Sparks, et al.. (2016). Progress Towards Computational 3-D Multicellular Systems Biology. Advances in experimental medicine and biology. 936. 225–246. 26 indexed citations

16.

Poleszczuk, Jan, Paul Macklin, & Heiko Enderling. (2016). Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth. Methods in molecular biology. 1516. 335–346. 35 indexed citations

17.

Macklin, Paul, Mary E. Edgerton, Alastair M. Thompson, & Vittorio Cristini. (2012). Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression. Journal of Theoretical Biology. 301. 122–140. 160 indexed citations

18.

D’Antonio, Gianluca, et al.. (2012). An agent-based model for elasto-plastic mechanical interactions between cells, basement membrane and extracellular matrix. Mathematical Biosciences & Engineering. 10(1). 75–101. 29 indexed citations

19.

Macklin, Paul & John Lowengrub. (2008). A New Ghost Cell/Level Set Method for Moving Boundary Problems: Application to Tumor Growth. Journal of Scientific Computing. 35(2-3). 266–299. 60 indexed citations

20.

Macklin, Paul & John Lowengrub. (2006). Nonlinear simulation of the effect of microenvironment on tumor growth. Journal of Theoretical Biology. 245(4). 677–704. 140 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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