论文标题
贝叶斯学习有效的化学主方程在拥挤的细胞内条件下
Bayesian learning of effective chemical master equations in crowded intracellular conditions
论文作者
论文摘要
活细胞内部的生化反应经常发生在人群的存在下 - 不参与反应但通过排除的体积效应影响反应速率的分子。但是，对随机细胞内反应动力学进行建模的标准方法是基于化学主方程（CME）的倾向，假设没有拥挤的效果。在这里，我们提出了一种基于贝叶斯优化的机器学习策略，该策略利用从空间细胞自动机（CA）模拟获得的合成数据（明确模型模型的音量排除效应）来学习CMES的有效倾向功能。然后可以将来自小CA训练数据集的预测扩展到整个参数空间范围，从而通过高斯过程回归来描述生理相关的拥挤水平。我们证明了酶催化反应和遗传反馈回路的方法，显示了有效CME和CA模拟预测的分子数的时间依赖性分布之间的良好一致性。
Biochemical reactions inside living cells often occur in the presence of crowders -- molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects. However the standard approach to modelling stochastic intracellular reaction kinetics is based on the chemical master equation (CME) whose propensities are derived assuming no crowding effects. Here, we propose a machine learning strategy based on Bayesian Optimisation utilising synthetic data obtained from spatial cellular automata (CA) simulations (that explicitly model volume-exclusion effects) to learn effective propensity functions for CMEs. The predictions from a small CA training data set can then be extended to the whole range of parameter space describing physiologically relevant levels of crowding by means of Gaussian Process regression. We demonstrate the method on an enzyme-catalyzed reaction and a genetic feedback loop, showing good agreement between the time-dependent distributions of molecule numbers predicted by the effective CME and CA simulations.
