A large-scale assessment of exact lumping of quantitative models in the BioModels repository

Isabel Cristina Perez-Verona, Mirco Tribastone*, Andrea Vandin

*Corresponding author for this work

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Chemical reaction networks are a popular formalism for modeling biological processes which supports both a deterministic and a stochastic interpretation based on ordinary differential equations and continuous-time Markov chains, respectively. In most cases, these models do not enjoy analytical solution, thus typically requiring expensive computational methods based on numerical solvers or stochastic simulations. Exact model reduction techniques can be used as an aid to lower the analysis cost by providing reduced networks that preserve the dynamics of interest to the modeler without incurring any approximation error. We hereby consider a family of techniques for both deterministic and stochastic networks which are based on equivalence relations over the species in the network, leading to a coarse graining which provides the exact aggregate time-course evolution for each equivalence class. We present a large-scale empirical assessment on the BioModels repository by measuring their compression capability over 579 models. Through a number of selected case studies, we also show their ability in yielding physically interpretable reductions that can reveal dynamical patterns of the bio-molecular processes under consideration, independently of the values of the kinetic parameters used in the models.

Original languageEnglish
JournalTheoretical Computer Science
Pages (from-to)41-59
Publication statusPublished - 2021

Bibliographical note

Funding Information:
The authors are grateful to Andreas Dräger (Institut für Informatik Zentrum für Bioinformatik Tübingen) for his support with JSBML. Partially supported by the Independent Research Fund Denmark DFF Research Project 9040-00224B REDUCTO , and the PRIN project “SEDUCE” no. 2017TWRCNB .

Publisher Copyright:
© 2021 Elsevier B.V.


  • Chemical reaction networks
  • Equivalence relations
  • Model reduction


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