A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions

Satya Samal, Dima Grigoriev, Holger Fröhlich, Andreas Weber und Ovidiu Radulescu
In: Bulletin of Mathematical Biology (Dez. 2015), 77:12(2180-2211)
 

Abstract

Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We provide a geometric method, based on the Newton polytope, to identify slow variables of a biochemical network with polynomial rate functions. The gist of the method is the notion of tropical equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic and utilizes orders of magnitude instead of precise values of the model parameters. Application of this method to a large collection of biochemical network models supports the idea that the number of dynamical variables in minimal models of cell physiology can be small, in spite of the large number of molecular regulatory actors.

Stichwörter: Algebraic systems biology, Complexity, model reduction

Bibtex

@ARTICLE{SamalEtAl2015b,
    author = {Samal, Satya and Grigoriev, Dima and Fr{\"o}hlich, Holger and Weber, Andreas and Radulescu, Ovidiu},
     pages = {2180--2211},
     title = {A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions},
   journal = {Bulletin of Mathematical Biology},
    volume = {77},
    number = {12},
      year = {2015},
     month = dec,
  keywords = {Algebraic systems biology, Complexity, model reduction},
  abstract = {Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We
              provide a geometric method, based on the Newton polytope, to identify slow variables of a
              biochemical network with polynomial rate functions. The gist of the method is the notion of tropical
              equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant
              numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic
              and utilizes orders of magnitude instead of precise values of the model parameters. Application of
              this method to a large collection of biochemical network models supports the idea that the number of
              dynamical variables in minimal models of cell physiology can be small, in spite of the large number
              of molecular regulatory actors.},
       doi = {10.1007/s11538-015-0118-0}
}