{"page":"145-161","citation":{"chicago":"Dos Santos Caldas, Paulo R, Philipp Radler, Christoph M Sommer, and Martin Loose. “Computational Analysis of Filament Polymerization Dynamics in Cytoskeletal Networks.” In <i>Methods in Cell Biology</i>, edited by Phong  Tran, 158:145–61. Elsevier, 2020. <a href=\"https://doi.org/10.1016/bs.mcb.2020.01.006\">https://doi.org/10.1016/bs.mcb.2020.01.006</a>.","short":"P.R. Dos Santos Caldas, P. Radler, C.M. Sommer, M. Loose, in:, P. Tran (Ed.), Methods in Cell Biology, Elsevier, 2020, pp. 145–161.","ista":"Dos Santos Caldas PR, Radler P, Sommer CM, Loose M. 2020.Computational analysis of filament polymerization dynamics in cytoskeletal networks. In: Methods in Cell Biology. Methods in Cell Biology, vol. 158, 145–161.","ieee":"P. R. Dos Santos Caldas, P. Radler, C. M. Sommer, and M. Loose, “Computational analysis of filament polymerization dynamics in cytoskeletal networks,” in <i>Methods in Cell Biology</i>, vol. 158, P. Tran, Ed. Elsevier, 2020, pp. 145–161.","ama":"Dos Santos Caldas PR, Radler P, Sommer CM, Loose M. Computational analysis of filament polymerization dynamics in cytoskeletal networks. In: Tran P, ed. <i>Methods in Cell Biology</i>. Vol 158. Elsevier; 2020:145-161. doi:<a href=\"https://doi.org/10.1016/bs.mcb.2020.01.006\">10.1016/bs.mcb.2020.01.006</a>","mla":"Dos Santos Caldas, Paulo R., et al. “Computational Analysis of Filament Polymerization Dynamics in Cytoskeletal Networks.” <i>Methods in Cell Biology</i>, edited by Phong  Tran, vol. 158, Elsevier, 2020, pp. 145–61, doi:<a href=\"https://doi.org/10.1016/bs.mcb.2020.01.006\">10.1016/bs.mcb.2020.01.006</a>.","apa":"Dos Santos Caldas, P. R., Radler, P., Sommer, C. M., &#38; Loose, M. (2020). Computational analysis of filament polymerization dynamics in cytoskeletal networks. In P. Tran (Ed.), <i>Methods in Cell Biology</i> (Vol. 158, pp. 145–161). Elsevier. <a href=\"https://doi.org/10.1016/bs.mcb.2020.01.006\">https://doi.org/10.1016/bs.mcb.2020.01.006</a>"},"_id":"7572","month":"02","title":"Computational analysis of filament polymerization dynamics in cytoskeletal networks","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"MaLo"}],"external_id":{"isi":["000611826500008"]},"publication":"Methods in Cell Biology","oa_version":"Preprint","ec_funded":1,"status":"public","year":"2020","scopus_import":"1","project":[{"name":"Self-Organization of the Bacterial Cell","_id":"2595697A-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"679239"},{"_id":"260D98C8-B435-11E9-9278-68D0E5697425","name":"Reconstitution of Bacterial Cell Division Using Purified Components"}],"volume":158,"date_updated":"2023-10-04T09:50:24Z","alternative_title":["Methods in Cell Biology"],"date_published":"2020-02-27T00:00:00Z","publication_status":"published","quality_controlled":"1","author":[{"full_name":"Dos Santos Caldas, Paulo R","orcid":"0000-0001-6730-4461","id":"38FCDB4C-F248-11E8-B48F-1D18A9856A87","last_name":"Dos Santos Caldas","first_name":"Paulo R"},{"orcid":"0000-0001-9198-2182 ","full_name":"Radler, Philipp","id":"40136C2A-F248-11E8-B48F-1D18A9856A87","last_name":"Radler","first_name":"Philipp"},{"full_name":"Sommer, Christoph M","orcid":"0000-0003-1216-9105","first_name":"Christoph M","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87","last_name":"Sommer"},{"orcid":"0000-0001-7309-9724","full_name":"Loose, Martin","last_name":"Loose","id":"462D4284-F248-11E8-B48F-1D18A9856A87","first_name":"Martin"}],"type":"book_chapter","intvolume":"       158","publication_identifier":{"issn":["0091679X"]},"related_material":{"record":[{"relation":"part_of_dissertation","id":"8358","status":"public"}]},"abstract":[{"text":"The polymerization–depolymerization dynamics of cytoskeletal proteins play essential roles in the self-organization of cytoskeletal structures, in eukaryotic as well as prokaryotic cells. While advances in fluorescence microscopy and in vitro reconstitution experiments have helped to study the dynamic properties of these complex systems, methods that allow to collect and analyze large quantitative datasets of the underlying polymer dynamics are still missing. Here, we present a novel image analysis workflow to study polymerization dynamics of active filaments in a nonbiased, highly automated manner. Using treadmilling filaments of the bacterial tubulin FtsZ as an example, we demonstrate that our method is able to specifically detect, track and analyze growth and shrinkage of polymers, even in dense networks of filaments. We believe that this automated method can facilitate the analysis of a large variety of dynamic cytoskeletal systems, using standard time-lapse movies obtained from experiments in vitro as well as in the living cell. Moreover, we provide scripts implementing this method as supplementary material.","lang":"eng"}],"language":[{"iso":"eng"}],"date_created":"2020-03-08T23:00:47Z","publisher":"Elsevier","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/839571"}],"doi":"10.1016/bs.mcb.2020.01.006","article_processing_charge":"No","day":"27","isi":1,"editor":[{"full_name":"Tran, Phong ","last_name":"Tran","first_name":"Phong "}]}