FitzGerald Cody E, Reich Shelley, Agaba Victor, Mathur Arjun, Werner Michael S, Mangan Niall M
Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA.
Department of Biological Sciences, Walla Walla University, College Place, WA 99324, USA.
ArXiv. 2025 Aug 28:arXiv:2508.21006v1.
Computationally inferring mechanistic insights and underlying control structures from typical biological data is a challenging pursuit. The technical reasons for this are multifaceted-and we delve into them in depth here, but they are easy to understand and involve both the data and model development. Even the highest-quality experimental data come with challenges. There are always sources of noise, a limit to how often we can measure the system, and we can rarely measure all the relevant states that participate in the full underlying complexity. There are usually sources of uncertainty in model development, which give rise to multiple competing model structures. To underscore the need for further analysis of structural uncertainty in modeling, we use a meta-analysis across six journals covering mathematical biology and show that a huge number of mathematical models for biological systems are developed each year, but model selection and comparison across model structures appear to be less common. We walk through a case study involving inference of regulatory network structure involved in a developmental decision in the nematode, . We first examine the of a model structure, or the ability to uniquely infer the structure given the data, across a wide range of synthetic data regimes by refitting both the true model structure and several misspecified models. We then use real biological data and compare across 13,824 models-each corresponding to a different regulatory network structure, to determine which regulatory features are supported by the data across three experimental conditions. We find that the best-fitting models for each experimental condition share a combination of features and identify a regulatory network that is common across the model sets for each condition. This model is capable of describing the data across the experimental conditions we considered and exhibits a high degree of positive regulation and interconnectivity between the key regulators, , , and . While the biological results are specific to the molecular biology of development in , the general modeling framework and underlying challenges we faced doing this analysis are widespread across biology, chemistry, physics, and many other scientific disciplines.
从典型的生物学数据中通过计算推断机制性见解和潜在控制结构是一项具有挑战性的工作。其技术原因是多方面的——我们在此深入探讨，但这些原因易于理解，且涉及数据和模型开发两方面。即使是最高质量的实验数据也存在挑战。总是存在噪声源，我们对系统进行测量的频率有限，而且我们很少能测量出参与整个潜在复杂性的所有相关状态。在模型开发中通常存在不确定性来源，这导致了多种相互竞争的模型结构。为了强调在建模中进一步分析结构不确定性的必要性，我们对涵盖数学生物学的六种期刊进行了荟萃分析，结果表明每年都会开发大量用于生物系统的数学模型，但跨模型结构的模型选择和比较似乎不太常见。我们详细介绍一个案例研究，该研究涉及线虫发育决策中调控网络结构的推断。我们首先通过重新拟合真实模型结构和几个错误指定的模型，在广泛的合成数据范围内检验模型结构的可识别性，即给定数据唯一推断结构的能力。然后我们使用真实的生物学数据，并在13824个模型（每个模型对应不同的调控网络结构）之间进行比较，以确定在三种实验条件下哪些调控特征得到了数据支持。我们发现每种实验条件下的最佳拟合模型都具有多种特征组合，并确定了每种条件下模型集共有的一个调控网络。该模型能够描述我们所考虑的实验条件下的数据，并且在关键调控因子、、和之间表现出高度的正调控和相互连接性。虽然生物学结果特定于线虫发育的分子生物学，但我们进行此分析所面临的一般建模框架和潜在挑战在生物学、化学、物理学以及许多其他科学学科中广泛存在。
