Maximal aggregation of polynomial dynamical systems

Luca Cardelli, Mirco Tribastone, Max Tschaikowski, Andrea Vandin

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article number201702697
JournalProceedings of the National Academy of Sciences of the United States of America
Number of pages6
ISSN0027-8424
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Polynomial dynamical systems
  • Partition refinement
  • Aggregation

Cite this

Cardelli, Luca ; Tribastone, Mirco ; Tschaikowski, Max ; Vandin, Andrea. / Maximal aggregation of polynomial dynamical systems. In: Proceedings of the National Academy of Sciences of the United States of America. 2017.
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Maximal aggregation of polynomial dynamical systems. / Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea.

In: Proceedings of the National Academy of Sciences of the United States of America, 2017.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Maximal aggregation of polynomial dynamical systems

AU - Cardelli, Luca

AU - Tribastone, Mirco

AU - Tschaikowski, Max

AU - Vandin, Andrea

PY - 2017

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N2 - 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.

AB - 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.

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