Modeling the adaptive immune system: predictions and simulations.

@article{citeulike:2048824, abstract = {MOTIVATION: Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered. SUMMARY: Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state of the art prediction tools for proteasomal cleavage and TAP binding. By comparison, Class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.}, address = {Center for biological sequence analysis, CBS, Kemitorvet 208, Technical University of Denmark, DK-2800 Lyngby, Denmark and Theoretical biology/bioinformatics, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.}, author = {Lundegaard, Claus and Lund, Ole and Kesmir, Can and Brunak, Søren and Nielsen, Morten }, citeulike-article-id = {2048824}, doi = {10.1093/bioinformatics/btm471}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {adaptive, immunity, immunology}, month = {November}, posted-at = {2007-12-09 07:19:35}, priority = {2}, title = {Modeling the adaptive immune system: predictions and simulations.}, url = {http://dx.doi.org/10.1093/bioinformatics/btm471}, year = {2007} }

TY - JOUR ID - citeulike:2048824 L3 - citeulike-article-id:2048824 TI - Modeling the adaptive immune system: predictions and simulations. JF - Bioinformatics AD - Center for biological sequence analysis, CBS, Kemitorvet 208, Technical University of Denmark, DK-2800 Lyngby, Denmark and Theoretical biology/bioinformatics, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands. SN - 1460-2059 N2 - MOTIVATION: Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered. SUMMARY: Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state of the art prediction tools for proteasomal cleavage and TAP binding. By comparison, Class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions. KW - adaptive KW - immunity KW - immunology AU - Lundegaard, Claus AU - Lund, Ole AU - Kesmir, Can AU - Brunak, Søren AU - Nielsen, Morten PY - 2007/11/28/ UR - http://dx.doi.org/10.1093/bioinformatics/btm471 ER -