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Motivation: Detailed mechanistic models of biological processes can posesignificant challenges for analysis and parameter estimations due to the largenumber of equations used to track the dynamics of all distinct configurationsin which each involved biochemical species can be found. Model reduction canhelp tame such complexity by providing a lower-dimensional model in which eachmacro-variable can be directly related to the original variables.  Results: We present CLUE, an algorithm for exact model reduction of systemsof polynomial differential equations by constrained linear lumping. It computesthe smallest dimensional reduction as a linear mapping of the state space suchthat the reduced model preserves the dynamics of user-specified linearcombinations of the original variables. Even though CLUE works with nonlineardifferential equations, it is based on linear algebra tools, which makes itapplicable to high-dimensional models. Using case studies from the literature,we show how CLUE can substantially lower model dimensionality and help extractbiologically intelligible insights from the reduction.  Availability: An implementation of the algorithm and relevant resources toreplicate the experiments herein reported are freely available for download athttps://github.com/pogudingleb/CLUE.  Supplementary information: enclosed.

CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations