{"title":"What can we learn from single molecule trajectories?","quality_controlled":"1","intvolume":"        12","citation":{"chicago":"Ruprecht, Verena, Markus Axmann, Stefan Wieser, and Gerhard Schuetz. “What Can We Learn from Single Molecule Trajectories?” <i>Current Protein &#38; Peptide Science</i>. Bentham Science Publishers, 2011. <a href=\"https://doi.org/10.2174/138920311798841753\">https://doi.org/10.2174/138920311798841753</a>.","apa":"Ruprecht, V., Axmann, M., Wieser, S., &#38; Schuetz, G. (2011). What can we learn from single molecule trajectories? <i>Current Protein &#38; Peptide Science</i>. Bentham Science Publishers. <a href=\"https://doi.org/10.2174/138920311798841753\">https://doi.org/10.2174/138920311798841753</a>","ista":"Ruprecht V, Axmann M, Wieser S, Schuetz G. 2011. What can we learn from single molecule trajectories? Current Protein &#38; Peptide Science. 12(8), 714–724.","mla":"Ruprecht, Verena, et al. “What Can We Learn from Single Molecule Trajectories?” <i>Current Protein &#38; Peptide Science</i>, vol. 12, no. 8, Bentham Science Publishers, 2011, pp. 714–24, doi:<a href=\"https://doi.org/10.2174/138920311798841753\">10.2174/138920311798841753</a>.","short":"V. Ruprecht, M. Axmann, S. Wieser, G. Schuetz, Current Protein &#38; Peptide Science 12 (2011) 714–724.","ama":"Ruprecht V, Axmann M, Wieser S, Schuetz G. What can we learn from single molecule trajectories? <i>Current Protein &#38; Peptide Science</i>. 2011;12(8):714-724. doi:<a href=\"https://doi.org/10.2174/138920311798841753\">10.2174/138920311798841753</a>","ieee":"V. Ruprecht, M. Axmann, S. Wieser, and G. Schuetz, “What can we learn from single molecule trajectories?,” <i>Current Protein &#38; Peptide Science</i>, vol. 12, no. 8. Bentham Science Publishers, pp. 714–724, 2011."},"date_published":"2011-12-01T00:00:00Z","month":"12","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","year":"2011","day":"01","publisher":"Bentham Science Publishers","department":[{"_id":"CaHe"},{"_id":"MiSi"}],"date_created":"2018-12-11T12:02:28Z","status":"public","author":[{"orcid":"0000-0003-4088-8633","first_name":"Verena","last_name":"Ruprecht","full_name":"Ruprecht, Verena","id":"4D71A03A-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Axmann, Markus","last_name":"Axmann","first_name":"Markus"},{"orcid":"0000-0002-2670-2217","first_name":"Stefan","last_name":"Wieser","full_name":"Wieser, Stefan","id":"355AA5A0-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Schuetz","first_name":"Gerhard","full_name":"Schuetz, Gerhard"}],"oa_version":"None","publist_id":"3358","date_updated":"2021-01-12T07:42:24Z","abstract":[{"lang":"eng","text":"Diffusing membrane constituents are constantly exposed to a variety of forces that influence their stochastic path. Single molecule experiments allow for resolving trajectories at extremely high spatial and temporal accuracy, thereby offering insights into en route interactions of the tracer. In this review we discuss approaches to derive information about the underlying processes, based on single molecule tracking experiments. In particular, we focus on a new versatile way to analyze single molecule diffusion in the absence of a full analytical treatment. The method is based on comprehensive comparison of an experimental data set against the hypothetical outcome of multiple experiments performed on the computer. Since Monte Carlo simulations can be easily and rapidly performed even on state-of-the-art PCs, our method provides a simple way for testing various - even complicated - diffusion models. We describe the new method in detail, and show the applicability on two specific examples: firstly, kinetic rate constants can be derived for the transient interaction of mobile membrane proteins; secondly, residence time and corral size can be extracted for confined diffusion."}],"volume":12,"scopus_import":1,"_id":"3287","page":"714 - 724","type":"journal_article","doi":"10.2174/138920311798841753","issue":"8","publication_status":"published","publication":"Current Protein & Peptide Science","language":[{"iso":"eng"}]}