Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially when dealing with real-world observational data, remains a challenging and underexplored area. This gap has spurred the development of various parameter estimation algorithms, particularly within the realm of Simulation-Based Inference (SBI), such as neural and statistical SBI methods. Limited research exists on how to make informed choices on SBI methods when faced with real-world data, which often results in some form of model misspecification. In this paper, we provide comprehensive guidelines for deciding between SBI approaches for complex biological models. We apply the guidelines to two agent-based models that describe cellular dynamics using real-world data. Our study unveils a critical insight: while neural SBI methods demand significantly fewer simulations for inference results, they tend to yield biased estimations, a trend persistent even with robust variants of these algorithms. On the other hand, the accuracy of statistical SBI methods enhances substantially as the number of simulations increases. This finding suggests that, given a sufficient computational budget, statistical SBI can surpass neural SBI in performance. Our results not only shed light on the efficacy of different SBI methodologies in real-world scenarios but also suggest potential avenues for enhancing neural SBI approaches. This study is poised to be a useful resource for computational biologists navigating the intricate landscape of SBI in biological modeling.

翻译：计算模型在捕捉现实世界生物过程的复杂性方面具有无可估量的价值。然而，为推断任务选择合适的算法，尤其是在处理现实世界的观测数据时，仍然是一个具有挑战性且研究不足的领域。这一空白推动了各种参数估计算法的发展，特别是在基于模拟的推断领域，例如神经和统计SBI方法。关于在面对现实世界数据（这些数据通常会导致某种形式的模型误设）时如何对SBI方法做出明智选择的研究十分有限。在本文中，我们为复杂生物模型选择SBI方法提供了全面的指导原则。我们将这些原则应用于两个使用现实世界数据描述细胞动态的基于智能体的模型。我们的研究揭示了一个关键发现：虽然神经SBI方法进行推断所需的模拟次数显著减少，但它们往往会产生有偏估计，即使使用这些算法的稳健变体，这种趋势依然存在。另一方面，统计SBI方法的准确性随着模拟次数的增加而大幅提高。这一发现表明，在给定足够计算资源的情况下，统计SBI的性能可以超越神经SBI。我们的结果不仅揭示了不同SBI方法在现实场景中的有效性，也指出了增强神经SBI方法的潜在途径。本研究有望成为计算生物学家在生物建模的复杂SBI领域中导航的有用资源。