Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

细胞生物学中研究的大多数令人着迷的现象都源于嵌入复杂且频繁动态的细胞形态中的高度组织的多分子结构之间的相互作用。对于此类系统的探索，计算机模拟已被证明是一种非常宝贵的工具，该领域的许多研究人员已经开发出复杂的计算模型，用于解决特定的细胞生物学问题。然而，通常很难协调使用不同方法描述同一现象的相互冲突的计算结果。为了系统地解决这个问题，我们定义了一系列计算测试用例，范围从非常简单到中等复杂，具有不同的维度、反应类型、反应速度、拥挤和单元大小等关键特征。然后，我们量化了明确的空间和/或随机实施如何改变结果，即使所有方法都使用相同的反应网络、速率和浓度。对于简单的情况，我们通常会发现同一问题的解决方案存在细微差别。然而，我们观察到，随着定位、降维和不可逆酶反应的影响相结合，不一致的情况越来越多。我们讨论了探索细胞生物学问题的常用计算方法的优点和局限性，并为开发新模型的研究人员提供了决策框架。随着计算能力和速度继续以惊人的速度增长，完全全面的活细胞计算模型的梦想可能越来越接近现实，但我们的分析表明，批判性地评估此类模型的准确性和准确性至关重要。系统地。