Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America.
Institute for Neural Computations, University of California, San Diego, La Jolla, California, United States of America.
PLoS Comput Biol. 2024 Apr 24;20(4):e1011800. doi: 10.1371/journal.pcbi.1011800. eCollection 2024 Apr.
Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4's Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.
活细胞中的生化信号通路通常高度组织成空间分隔的体积、膜、支架、亚细胞隔室和细胞器,其中包含少量相互作用的分子。在这种粒度水平上,随机行为占主导地位,基于浓度的均匀连续体近似失效,基于粒子的方法更加准确和高效。我们描述并验证了开源 MCell 模拟程序(MCell4)的新版本,该版本支持在溶液中离散分子和大分子复合物的扩散和化学反应的通用 3D 蒙特卡罗建模,在代表膜的表面上,以及它们的组合。与之前的版本 MCell3 和 MCell3-R 相比,MCell4 的主要改进包括 Python 接口和本机 BioNetGen 反应语言(BNGL)支持。MCell4 的 Python 接口为与外部模拟器进行接口打开了全新的可能性,从而允许创建复杂的事件驱动多尺度/多物理模拟。通过一个新的开源库 libBNG(本文也介绍了这个库)实现的本机 BNGL 支持,提供了在 MCell4 中以空间分辨方式运行给定 BNGL 模型的能力,并且在适当的简化假设下,也可以在 BioNetGen 模拟环境中运行,大大加速和简化了模型验证和比较。