CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations. Ovchinnikov, A., Verona, I. C. P., Pogudin, G., & Tribastone, M. arXiv:2004.11961 [cs, eess, q-bio], April, 2020. arXiv: 2004.11961![CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables. We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with nonlinear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction. An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE.
@article{ovchinnikov_clue_2020,
title = {{CLUE}: {Exact} maximal reduction of kinetic models by constrained lumping of differential equations},
shorttitle = {{CLUE}},
url = {http://arxiv.org/abs/2004.11961},
abstract = {Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables. We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with nonlinear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction. An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE.},
urldate = {2020-05-03},
journal = {arXiv:2004.11961 [cs, eess, q-bio]},
author = {Ovchinnikov, Alexey and Verona, Isabel Christina Pérez and Pogudin, Gleb and Tribastone, Mirco},
month = apr,
year = {2020},
note = {arXiv: 2004.11961},
keywords = {Electrical Engineering and Systems Science - Systems and Control, Quantitative Biology - Molecular Networks, mentions sympy, symbolic computation},
}
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