Mathematical Institute, University of Oxford, Oxford, UK.
Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK.
Bull Math Biol. 2022 Oct 23;84(12):137. doi: 10.1007/s11538-022-01088-2.
The MEK/ERK signalling pathway is involved in cell division, cell specialisation, survival and cell death (Shaul and Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773(8):1213-1226, 2007). Here we study a polynomial dynamical system describing the dynamics of MEK/ERK proposed by Yeung et al. (Curr Biol 2019, https://doi.org/10.1016/j.cub.2019.12.052 ) with their experimental setup, data and known biological information. The experimental dataset is a time-course of ERK measurements in different phosphorylation states following activation of either wild-type MEK or MEK mutations associated with cancer or developmental defects. We demonstrate how methods from computational algebraic geometry, differential algebra, Bayesian statistics and computational algebraic topology can inform the model reduction, identification and parameter inference of MEK variants, respectively. Throughout, we show how this algebraic viewpoint offers a rigorous and systematic analysis of such models.
MEK/ERK 信号通路参与细胞分裂、细胞特化、存活和细胞死亡 (Shaul 和 Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773(8):1213-1226, 2007)。在这里,我们研究了由 Yeung 等人提出的描述 MEK/ERK 动力学的多项式动力系统。(Curr Biol 2019, https://doi.org/10.1016/j.cub.2019.12.052 ),并使用他们的实验设置、数据和已知的生物学信息。实验数据集是 ERK 在不同磷酸化状态下的时间过程,这些状态是在激活野生型 MEK 或与癌症或发育缺陷相关的 MEK 突变后测量的。我们展示了计算代数几何、微分代数、贝叶斯统计和计算代数拓扑中的方法如何分别为 MEK 变体的模型简化、识别和参数推断提供信息。在整个过程中,我们展示了这种代数观点如何为这些模型提供严格和系统的分析。