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Maximal aggregation of polynomial dynamical systems

  1. Andrea Vandinc,1
  1. aMicrosoft Research, Cambridge CB1 2FB, United Kingdom;
  2. bDepartment of Computing, University of Oxford, Oxford OX1 3QD, United Kingdom;
  3. cScuola IMT Alti Studi Lucca, 55100 Lucca, Italy
  1. Edited by Moshe Y. Vardi, Rice University, Houston, TX, and approved July 28, 2017 (received for review February 16, 2017)

Significance

Large-scale dynamical models hinder our capability of effectively analyzing them and interpreting their behavior. We present an algorithm for the simplification of polynomial ordinary differential equations by aggregating their variables. The reduction can preserve observables of interest and yields a physically intelligible reduced model, since each aggregate corresponds to the exact sum of original variables.

Abstract

Ordinary differential equations (ODEs) with polynomial derivatives are a fundamental tool for understanding the dynamics of systems across many branches of science, but our ability to gain mechanistic insight and effectively conduct numerical evaluations is critically hindered when dealing with large models. Here we propose an aggregation technique that rests on two notions of equivalence relating ODE variables whenever they have the same solution (backward criterion) or if a self-consistent system can be written for describing the evolution of sums of variables in the same equivalence class (forward criterion). A key feature of our proposal is to encode a polynomial ODE system into a finitary structure akin to a formal chemical reaction network. This enables the development of a discrete algorithm to efficiently compute the largest equivalence, building on approaches rooted in computer science to minimize basic models of computation through iterative partition refinements. The physical interpretability of the aggregation is shown on polynomial ODE systems for biochemical reaction networks, gene regulatory networks, and evolutionary game theory.

Footnotes

  • ?1L.C., M. Tribastone, M. Tschaikowski, and A.V. contributed equally to this work.

  • ?2To whom correspondence should be addressed. Email: mirco.tribastone{at}imtlucca.it.

Online Impact

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