CLUE：通过微分方程的约束集总实现动力学模型的精确最大化约简
CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations
摘要：
动机：生物过程的详细机理模型可能会给分析和参数估计带来巨大的挑战，因为用于跟踪所有不同构型的动力学的大量方程（其中每个涉及的生化物种都可以被发现）。结果：我们提出了CLUE，一个算法的多项式微分方程系统的约束线性集总精确模型降阶。它将最小降维计算为状态空间的线性映射，使得降维模型保留原始变量的用户指定线性组合的动态。尽管CLUE可以处理非线性微分方程，但它是基于线性代数工具的，这使得它适用于高维模型。使用文献中的案例研究，我们展示了如何CLUE可以大大降低模型的维数，并帮助提取生物学上可理解的见解减少。供货情况：该算法的实现和复制本文报道的实验的相关资源可在www.example.com免费下载https://github.com/pogudingleb/CLUE。补充资料：随函附上。
英文摘要：
Motivation: 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. Results: 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. Availability: 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. Supplementary information: enclosed.
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