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A Mathematical Programming Formulation for the Budding Yeast Cell Cycle

机译:酵母发酵周期的数学编程公式

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The budding yeast cell cycle can be modeled by a set of ordinary differential equations with 143 rate constant parameters. The quality of the model (and an associated vector of parameter settings) is measured by comparing simulation results to the experimental data derived from observing the cell cycles of over 100 selected mutated forms. Unfortunately, determining whether the simulated phenotype matches experimental data is difficult since the experimental data tend to be qualitative in nature (i.e., whether the mutation is viable, or which development phase it died in). Because of this, previous methods for automatically comparing simulation results to experimental data used a discontinuous penalty function, which limits the range of techniques available for automated estimation of the differential equation parameters. This paper presents a system of smooth inequality constraints that will be satisfied if and only if the model matches the experimental data. Results are presented for evaluating the mutants with the two most frequent phenotypes. This nonlinear inequality formulation is the first step toward solving a large-scale feasibility problem to determine the ordinary differential equation model parameters.
机译:可以通过一组具有143个速率常数参数的常微分方程对酵母芽的细胞周期进行建模。通过将仿真结果与通过观察100多种选定突变形式的细胞周期而得出的实验数据进行比较,可以测量模型的质量(以及参数设置的相关向量)。不幸的是,由于实验数据本质上趋于定性(即,突变是否可行,或其死于哪个发育阶段),因此很难确定模拟表型是否与实验数据匹配。因此,用于自动将仿真结果与实验数据进行比较的先前方法使用了不连续罚函数,这限制了可用于自动估计微分方程参数的技术范围。本文提出了一个光滑不等式约束的系统,当且仅当模型与实验数据匹配时,该系统才会得到满足。提供了评估具有两种最常见表型的突变体的结果。这种非线性不等式公式化是解决大型可行性问题以确定常微分方程模型参数的第一步。

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