AI Article Synopsis
- Mathematical models in immunology help quantify biological processes but need careful parameterization and uncertainty assessment for accurate predictions.
- The review discusses various methods and software tools for parameter estimation, including gradient-based, gradient-free methods, and techniques like profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification.
- It highlights recent applications and suggests future potential for these methods in modeling immune-related systems.
Article Abstract
Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quantification before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384601 | PMC |
| http://dx.doi.org/10.1016/j.coisb.2019.10.006 | DOI Listing |
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