Application of minimum description length criterion to assess the complexity of models in mathematical immunology

Abstract

Abstract Mathematical models in immunology differ enormously in the dimensionality of the state space, the number of parameters and the parameterizations used to describe the immune processes. The ongoing diversification of the models needs to be complemented by rigorous ways to evaluate their complexity and select the parsimonious ones in relation to the data available/used for their calibration. A broadly applied metrics for ranking the models in mathematical immunology with respect to their complexity/parsimony is provided by the Akaike information criterion. In the present study, a computational framework is elaborated to characterize the complexity of mathematical models in immunology using a more general approach, namely, the Minimum Description Length criterion. It balances the model goodness-of-fit with the dimensionality and geometrical complexity of the model. Four representative models of the immune response to acute viral infection formulated with either ordinary or delay differential equations are studied. Essential numerical details enabling the assessment and ranking of the viral infection models include: (1) the optimization of the likelihood function, (2) the computation of the model sensitivity functions, (3) the evaluation of the Fisher information matrix and (4) the estimation of multidimensional integrals over the model parameter space.