Application of minimum description length criterion to assess the complexity of models in mathematical immunology
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Dmitry S. Grebennikov
,
Valerya V. Zheltkova
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.
Funding statement: This work was financed by the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Centers ‘Digital biodesign and personalized healthcare’ No. 075-15-2022-304.
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Articles in the same Issue
- Contents
- Application of minimum description length criterion to assess the complexity of models in mathematical immunology
- Computational mimicking of surgical leaflet suturing for virtual aortic valve neocuspidization
- Personalized computational estimation of relative change in coronary blood flow after percutaneous coronary intervention in short-term and long-term perspectives
- Algorithms and methodological challenges in the development and application of quantitative systems pharmacology models: a case study in type 2 diabetes
- Computational analysis of the impact of aortic bifurcation geometry to AAA haemodynamics
