A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability

Journal article

Mathematical models of voltage-gated ion channels are used in basic research, industrial and clinical settings. These models range in complexity, but typically contain numerous variables representing the proportion of channels in a given state, and parameters describing the voltage-dependent rates of transition between states. An open problem is selecting the appropriate degree of complexity and structure for an ion channel model given data availability. Here, we simplify a model of the cardiac human Ether-à-go-go Related Gene (hERG) potassium ion channel, which carries cardiac IKr, using the manifold boundary approximation method (MBAM). The MBAM approximates high-dimensional model-output manifolds by reduced models describing their boundaries, resulting in models with fewer parameters (and often variables). We produced a series of models of reducing complexity starting from an established 5-state hERG model with 15 parameters. Models with up to 3 fewer states and 8 fewer parameters were shown to retain much of the predictive capability of the full model and were validated using experimental hERG1a data collected in HEK293 cells at 37°C. The method provides a way to simplify complex models of ion channels that improves parameter identifiability and will aid in future model development

Authors: Sher, A; Niederer, SA; Mirams, GR; Kirpichnikova, A; Allen, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Bulletin of Mathematical Biology
• Volume: 84
• Issue: 3
• Pages: 39-39
• Article number: 39
• Publication date: 2022-02-07
• DOI: 10.1007/s11538-021-00982-5
• EISSN: 1522-9602
• ISSN: 0092-8240
• Language: English
• Keywords: Management science; Machine learning; Engineering; Risk analysis (engineering); Artificial intelligence; Computer science; Medicine; Perspective (graphical); Confusion; Identifiability; Terminology; Inference