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Scalable inference of ordinary differential equation models of biochemical processes.

Methods Mol. Biol. 1883, 385-422 (2019)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Large-scale Models ; Ordinary Differential Equations ; Parameter Estimation ; Uncertainty Analysis
Reviewing status