TY - JOUR
T1 - Dynamic models of immune responses
T2 - What is the ideal level of detail?
AU - Thakar, Juilee
AU - Poss, Mary
AU - Albert, Réka
AU - Long, Grinne H.
AU - Zhang, Ranran
N1 - Funding Information:
This opinion is an outcome of the discussions at the workshop organized in June 2008 at the Center for Infectious Disease Dynamics. We want to thank all the speakers for their contributions; the list of the speakers can be found at http://www.cidd.psu.edu/calendar/workshops/multi-scale-modeling-of-immune-responses. JT is thankful to the Cancer Research Institute for a postdoctoral fellowship. We are also thankful to the three anonymous reviewers whose comments made this manuscript better in many ways.
PY - 2010
Y1 - 2010
N2 - Background. One of the goals of computational immunology is to facilitate the study of infectious diseases. Dynamic modeling is a powerful tool to integrate empirical data from independent sources, make novel predictions, and to foresee the gaps in the current knowledge. Dynamic models constructed to study the interactions between pathogens and hosts' immune responses have revealed key regulatory processes in the infection. Optimum complexity and dynamic modeling. We discuss the usability of various deterministic dynamic modeling approaches to study the progression of infectious diseases. The complexity of these models is dependent on the number of components and the temporal resolution in the model. We comment on the specific use of simple and complex models in the study of the progression of infectious diseases. Conclusions. Models of sub-systems or simplified immune response can be used to hypothesize phenomena of host-pathogen interactions and to estimate rates and parameters. Nevertheless, to study the pathogenesis of an infection we need to develop models describing the dynamics of the immune components involved in the progression of the disease. Incorporation of the large number and variety of immune processes involved in pathogenesis requires tradeoffs in modeling.
AB - Background. One of the goals of computational immunology is to facilitate the study of infectious diseases. Dynamic modeling is a powerful tool to integrate empirical data from independent sources, make novel predictions, and to foresee the gaps in the current knowledge. Dynamic models constructed to study the interactions between pathogens and hosts' immune responses have revealed key regulatory processes in the infection. Optimum complexity and dynamic modeling. We discuss the usability of various deterministic dynamic modeling approaches to study the progression of infectious diseases. The complexity of these models is dependent on the number of components and the temporal resolution in the model. We comment on the specific use of simple and complex models in the study of the progression of infectious diseases. Conclusions. Models of sub-systems or simplified immune response can be used to hypothesize phenomena of host-pathogen interactions and to estimate rates and parameters. Nevertheless, to study the pathogenesis of an infection we need to develop models describing the dynamics of the immune components involved in the progression of the disease. Incorporation of the large number and variety of immune processes involved in pathogenesis requires tradeoffs in modeling.
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U2 - 10.1186/1742-4682-7-35
DO - 10.1186/1742-4682-7-35
M3 - Review article
C2 - 20727155
AN - SCOPUS:77955707911
SN - 1742-4682
VL - 7
JO - Theoretical Biology and Medical Modelling
JF - Theoretical Biology and Medical Modelling
IS - 1
M1 - 35
ER -