Chemical reaction networks are a popular formalism for modeling biological processes which supports both a deterministic and a stochastic interpretation based on ordinary differential equations and continuous-time Markov chains, respectively. In most cases, these models do not enjoy analytical solution, thus typically requiring expensive computational methods based on numerical solvers or stochastic simulations. Exact model reduction techniques can be used as an aid to lower the analysis cost by providing reduced networks that preserve the dynamics of interest to the modeler without incurring any approximation error. We hereby consider a family of techniques for both deterministic and stochastic networks which are based on equivalence relations over the species in the network, leading to a coarse graining which provides the exact aggregate time-course evolution for each equivalence class. We present a large-scale empirical assessment on the BioModels repository by measuring their compression capability over 579 models. Through a number of selected case studies, we also show their ability in yielding physically interpretable reductions that can reveal dynamical patterns of the bio-molecular processes under consideration, independently of the values of the kinetic parameters used in the models.

化学反应网络是一种流行的生物过程建模形式，它分别支持基于常微分方程和连续时间马尔可夫链的确定性和随机性解释。在大多数情况下，这些模型不具备解析解，因此通常需要基于数值求解器或随机模拟的昂贵计算方法。精确模型缩减技术可以通过提供缩减网络来帮助降低分析成本，这些网络可以保留建模者感兴趣的动态，而不会产生任何近似误差。我们在此考虑用于确定性和随机网络的一系列技术，这些技术基于网络中物种的等价关系，导致粗粒度，为每个等价类提供精确的聚合时间进程演变。我们通过测量超过 579 个模型的压缩能力，对 BioModels 存储库进行了大规模的实证评估。通过一些选定的案例研究，我们还展示了它们产生物理上可解释的减少的能力，这些减少可以揭示所考虑的生物分子过程的动态模式，而与模型中使用的动力学参数值无关。