Institute of Theoretical Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
Department of Mathematics, King's College London, Strand, London WC2R 2LS, United Kingdom.
J Chem Phys. 2020 Jul 14;153(2):025101. doi: 10.1063/5.0008304.
We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g., protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the "extrinsic" sources of stochasticity arising from the rest of the network. Our approaches are based on subnetwork dynamical equations derived by projection methods and path integrals. The results provide a principled derivation of different components of the extrinsic noise that is observed experimentally in cellular biochemical reactions, over and above the intrinsic noise from the stochasticity of biochemical events in the subnetwork. We explore several intermediate approximations to assess systematically the relative importance of different extrinsic noise components, including initial transients, long-time plateaus, temporal correlations, multiplicative noise terms, and nonlinear noise propagation. The best approximations achieve excellent accuracy in quantitative tests on a simple protein network and on the epidermal growth factor receptor signaling network.
我们考虑描述较大生化反应网络（例如涉及复杂形成和解离反应的蛋白质相互作用网络）子网的动力学的一般问题。我们建议使用模型简化策略来理解来自网络其余部分的随机“外在”源。我们的方法基于通过投影方法和路径积分推导的子网动态方程。这些结果为在细胞生化反应中观察到的实验性外在噪声的不同分量提供了一种有原则的推导，超出了子网中生化事件随机性产生的内在噪声。我们探索了几种中间近似值，以系统地评估不同外在噪声分量的相对重要性，包括初始瞬态、长时间平台、时间相关性、乘法噪声项和非线性噪声传播。在简单的蛋白质网络和表皮生长因子受体信号网络上的定量测试中，最佳近似值达到了极好的准确性。
