Michael T. Cooling

1.1k total citations
19 papers, 474 citations indexed

About

Michael T. Cooling is a scholar working on Molecular Biology, Biophysics and Cell Biology. According to data from OpenAlex, Michael T. Cooling has authored 19 papers receiving a total of 474 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 6 papers in Biophysics and 3 papers in Cell Biology. Recurrent topics in Michael T. Cooling's work include Gene Regulatory Network Analysis (13 papers), Cell Image Analysis Techniques (6 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Michael T. Cooling is often cited by papers focused on Gene Regulatory Network Analysis (13 papers), Cell Image Analysis Techniques (6 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Michael T. Cooling collaborates with scholars based in New Zealand, United Kingdom and United States. Michael T. Cooling's co-authors include Peter Hunter, Edmund J. Crampin, James R. Lawson, David Nickerson, Poul M. F. Nielsen, Catherine M. Lloyd, Alan Garny, Randall D. Britten, Andrew Miller and Anil Wipat and has published in prestigious journals such as Bioinformatics, The Journal of Physiology and Biophysical Journal.

In The Last Decade

Michael T. Cooling

19 papers receiving 463 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael T. Cooling New Zealand	13	345	83	54	51	43	19	474
Autumn Cuellar New Zealand	4	176 0.5×	45 0.5×	34 0.6×	27 0.5×	24 0.6×	6	237
Blanca Rodríguez United Kingdom	8	133 0.4×	212 2.6×	29 0.5×	18 0.4×	60 1.4×	34	391
Andrew E. Bruno United States	11	260 0.8×	20 0.2×	17 0.3×	13 0.3×	8 0.2×	21	407
Rajaram Kaliyaperumal Netherlands	12	254 0.7×	29 0.3×	151 2.8×	7 0.1×	23 0.5×	32	487
Jim Bosley United States	8	157 0.5×	30 0.4×	5 0.1×	16 0.3×	57 1.3×	18	345
Marco Donizelli United Kingdom	4	370 1.1×	4 0.0×	38 0.7×	17 0.3×	36 0.8×	8	445
Tomasz Adamusiak United States	11	425 1.2×	6 0.1×	56 1.0×	22 0.4×	37 0.9×	17	594
Frank Meineke Germany	7	127 0.4×	5 0.1×	25 0.5×	9 0.2×	52 1.2×	21	340
Mesude Bicak United States	9	134 0.4×	51 0.6×	63 1.2×	2 0.0×	10 0.2×	18	325
Hans‐Michael Kaltenbach Switzerland	11	255 0.7×	4 0.0×	7 0.1×	13 0.3×	73 1.7×	32	396

Countries citing papers authored by Michael T. Cooling

Since Specialization

Citations

This map shows the geographic impact of Michael T. Cooling'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 T. Cooling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael T. Cooling more than expected).

Fields of papers citing papers by Michael T. Cooling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael T. Cooling

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

All Works

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19 of 19 papers shown

Clerx, Michael, Michael T. Cooling, Jonathan Cooper, et al.. (2020). CellML 2.0. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 17(2-3). 18 indexed citations

Cooling, Michael T., David Nickerson, Poul M. F. Nielsen, & Peter Hunter. (2016). Modular modelling with Physiome standards. The Journal of Physiology. 594(23). 6817–6831. 8 indexed citations

McGlashan, Sue R., et al.. (2015). Culture and detection of primary cilia in endothelial cell models. PubMed. 4(1). 11–11. 27 indexed citations

Cooling, Michael T. & Peter Hunter. (2015). The CellML Metadata Framework 2.0 Specification. PubMed. 12(2). 260–260. 10 indexed citations

Cooling, Michael T. & Peter Hunter. (2015). The CellML Metadata Framework 2.0 Specification. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 12(2). 86–103. 7 indexed citations

Neal, Maxwell L., Michael T. Cooling, Lucian P. Smith, et al.. (2014). A Reappraisal of How to Build Modular, Reusable Models of Biological Systems. PLoS Computational Biology. 10(10). e1003849–e1003849. 31 indexed citations

Cooling, Michael T., et al.. (2014). Computational models of the primary cilium and endothelial mechanotransmission. Biomechanics and Modeling in Mechanobiology. 14(3). 665–678. 9 indexed citations

Ho, Harvey, et al.. (2011). Multiscale Modeling of Intracranial Aneurysms: Cell Signaling, Hemodynamics, and Remodeling. IEEE Transactions on Biomedical Engineering. 58(10). 2974–2977. 12 indexed citations

Miller, Andrew, Randall D. Britten, Michael T. Cooling, et al.. (2011). Revision history aware repositories of computational models of biological systems. BMC Bioinformatics. 12(1). 22–22. 12 indexed citations

10.

Lloyd, Catherine M., David Nickerson, Michael T. Cooling, et al.. (2011). The Physiome Model Repository 2. Bioinformatics. 27(5). 743–744. 99 indexed citations

11.

Mısırlı, Göksel, Jennifer Hallinan, James R. Lawson, et al.. (2011). Model annotation for synthetic biology: automating model to nucleotide sequence conversion. Bioinformatics. 27(7). 973–979. 22 indexed citations

12.

Sher, Anna, Michael T. Cooling, Colin Enticott, et al.. (2010). A global sensitivity tool for cardiac cell modeling: Application to ionic current balance and hypertrophic signaling. PubMed. 291. 1498–1502. 2 indexed citations

13.

Cooling, Michael T., Vincent Rouilly, Göksel Mısırlı, et al.. (2010). Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics. 26(7). 925–931. 63 indexed citations

14.

Wimalaratne, Sarala, et al.. (2009). Facilitating modularity and reuse: guidelines for structuring CellML 1.1 models by isolating common biophysical concepts. Experimental Physiology. 94(5). 472–485. 12 indexed citations

15.

Cooling, Michael T., Peter Hunter, & Edmund J. Crampin. (2009). Sensitivity of NFAT Cycling to Cytosolic Calcium Concentration: Implications for Hypertrophic Signals in Cardiac Myocytes. Biophysical Journal. 96(6). 2095–2104. 28 indexed citations

16.

Wimalaratne, Sarala, Matt Halstead, Catherine M. Lloyd, et al.. (2009). A method for visualizing CellML models. Bioinformatics. 25(22). 3012–3019. 7 indexed citations

17.

Beard, Daniel, Randall D. Britten, Michael T. Cooling, et al.. (2009). CellML metadata standards, associated tools and repositories. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 367(1895). 1845–1867. 44 indexed citations

18.

Cooling, Michael T., Peter Hunter, & Edmund J. Crampin. (2008). Modelling biological modularity with CellML. IET Systems Biology. 2(2). 73–79. 26 indexed citations

19.

Cooling, Michael T., Peter Hunter, & Edmund J. Crampin. (2007). Modeling Hypertrophic IP3 Transients in the Cardiac Myocyte. Biophysical Journal. 93(10). 3421–3433. 37 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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