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Báo cáo y học: "Numerical modelling of label-structured cell population growth using CFSE distribution data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: " Numerical modelling of label-structured cell population growth using CFSE distribution data | Theoretical Biology and Medical Modelling BioMed Central Research Open Access Numerical modelling of label-structured cell population growth using CFSE distribution data Tatyana Luzyanina1 Dirk Roose2 Tim Schenkel3 Martina Sester4 Stephan Ehl5 Andreas Meyerhans3 and Gennady Bocharov 6 Address Unstitute of Mathematical Problems in Biology RAS Pushchino Russia 2Department of Computer Science Katholieke Universiteit Leuven Belgium 3Department of Virology University of the Saarland Homburg Germany 4Department of Internal Medicine University of the Saarland Homburg Germany 5Children s Hospital University of Freiburg Freiburg Germany and institute of Numerical Mathematics RAS Moscow Russia Email Tatyana Luzyanina - luzyanina@impb.psn.ru Dirk Roose - Dirk.Roose@cs.kuleuven.be Tim Schenkel - vitsch@uniklinikum-saarland.de Martina Sester - martina.sester@uniklinikum-saarland.de Stephan Ehl - stephan.ehl@uniklinik-freiburg.de Andreas Meyerhans - Andreas.Meyerhans@uniklinik-saarland.de Gennady Bocharov - bocharov@inm.ras.ru Corresponding author Published 24 July 2007 Received 10 April 2007 Theoretical Biology and Medical Modelling 2007 4 26 doi 10.1186 1742-4682-4-26 Accepted 24 July 2007 This article is available from http www.tbiomed.eom content 4 1 26 2007 Luzyanina et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The flow cytometry analysis of CFSE-labelled cells is currently one of the most informative experimental techniques for studying cell proliferation in immunology. The quantitative interpretation and understanding of such heterogenous cell population data requires the development of distributed parameter mathematical models and computational techniques for data assimilation. Methods and Results The