The inverse problem in mathematical biology

Biological systems present particular challengers to model for the purposes of formulating predictions of generating biological insight. These systems are typically multi-scale, complex, and empirical observations are often sparse and subject to variability and uncertainty. This manuscript will revi...

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Bibliographic Details
Published inMathematical biosciences Vol. 260; pp. 11 - 15
Main Authors Clermont, Gilles, Zenker, Sven
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.02.2015
Subjects
Biological systems
Ensemble models
Humans
Inverse problem
Models, Biological
Models, Theoretical
Parameter estimation
Ensemble models
Parameter estimation
Biological systems
Inverse problem
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Summary:Biological systems present particular challengers to model for the purposes of formulating predictions of generating biological insight. These systems are typically multi-scale, complex, and empirical observations are often sparse and subject to variability and uncertainty. This manuscript will review some of these specific challenges and introduce current methods used by modelers to construct meaningful solutions, in the context of preserving biological relevance. Opportunities to expand these methods are also discussed. •Estimation of computational models of biological systems poses formidable challenges.•Mechanistic inference usually requires practical parameter identifiability.•Prediction of system evolution may not require parameter identifiability.•A satisfactory solution to the inverse problem awaits further theoretical progress.
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ISSN:0025-5564
1879-3134
1879-3134
DOI:10.1016/j.mbs.2014.09.001