School of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK.
Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
Sci Rep. 2018 Feb 22;8(1):3498. doi: 10.1038/s41598-018-21826-8.
Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving stochastic reaction-diffusion equations are computationally intractable for large-scale networks. We present a novel method for modeling stochastic and spatial dynamics in biomolecular networks using a simple form of the Langevin equation with noisy kinetic constants. Spatial heterogeneity in molecular interactions is decoupled into a set of compartments, where the distribution of molecules in each compartment is idealised as being uniform. The reactions in the network are then modelled by Langevin equations with correcting terms, that account for differences between spatially uniform and spatially non-uniform distributions, and that can be readily estimated from available experimental data. The accuracy and extreme computational efficiency of the approach is demonstrated on a model of the epidermal growth factor receptor network in the human mammary epithelial cell.
现在，生物学中的测量技术能够同时提供多个单个分子轨迹的数据，这促使了针对生物分子网络的随机时空建模技术的发展。然而，基于求解随机反应-扩散方程的标准方法对于大规模网络来说在计算上是难以处理的。我们提出了一种新的方法，使用带有噪声动力学常数的简单形式的 Langevin 方程来对生物分子网络中的随机和空间动力学进行建模。分子相互作用的空间异质性被解耦为一组隔室，其中每个隔室中的分子分布被理想化地均匀。然后，通过带有校正项的 Langevin 方程对网络中的反应进行建模，这些校正项考虑了空间均匀和空间非均匀分布之间的差异，并且可以从可用的实验数据中容易地估计出来。该方法的准确性和极高的计算效率在人乳腺上皮细胞中表皮生长因子受体网络的模型上得到了验证。
