Hybrid Reductions of Computational Models of Ion Channels Coupled to Cellular Biochemistry

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9859)


Computational models of cellular physiology are often too complex to be analyzed with currently available tools. By model reduction we produce simpler models with less variables and parameters, that can be more easily simulated and analyzed. We propose a reduction method that applies to ordinary differential equations models of voltage and ligand gated ion channels coupled to signaling and metabolism. These models are used for studying various biological functions such as neuronal and cardiac activity, or insulin production by pancreatic beta-cells. Models of ion channels coupled to cell biochemistry share a common structure. For such models we identify fast and slow sub-processes, driving and slaved variables, as well as a set of reduced models. Various reduced models are valid locally and can change on a trajectory. The resulting reduction is hybrid, implying transitions from one reduced model (mode) to another one.


Outer Layer Singular Perturbation Invariant Manifold Fast Variable Matched Asymptotic Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the University of Chicago and by the FACCTS (France and Chicago Collaborating in The Sciences) program. The authors express their gratitude to the reviewers for their many helpful comments.


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.DIMNP UMR CNRS 5235, University of MontpellierMontpellierFrance
  2. 2.Department of StatisticsUniversity of ChicagoChicagoUSA
  3. 3.Department of Ecology and Evolution, Department of Molecular Genetics and Cell BiologyUniversity of ChicagoChicagoUSA
  4. 4.Department of Medicine and PediatricsUniversity of ChicagoChicagoUSA

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