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Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry

机译：量化空间和随机性在细胞生物学和细胞生物化学计算机模拟中的作用

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Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular 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.
机译：在细胞生物学中研究的大多数迷人现象从嵌入复杂和经常动态细胞形态的高度有组织的多分子结构之间的相互作用中出现。为了探索这种系统，计算机仿真已被证明是一个宝贵的工具，这个领域的许多研究人员已经开发了复杂的计算模型，用于应用于特定的细胞生物学问题。然而，通常难以调和使用不同方法来描述相同现象的冲突的计算结果。为了系统地解决这个问题，我们已经确定了一系列计算测试案例，从非常简单到中度复杂，不同的重点，反应类型，反应速度，拥挤和细胞尺寸。然后，我们也量化了显式的空间和/或随机实现改变结果，即使所有方法都使用相同的反应网络，速率和浓度。对于简单的案例，我们通常在同一问题的解决方案中找到微小的差异。然而，我们观察到随着定位，维度降低和不可逆酶促反应的影响，增加不断的不良。我们讨论了常用计算方法的优势和局限，以探索细胞生物学问题，并为研究人员提供决策的框架开发新模型。随着计算能力和速度以显着的速度继续增加，生活细胞的全面综合计算模型的梦想可能是更接近的现实，但我们的分析表明，评估这些模型的准确性并批判性是至关重要的系统地。

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