Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, United States of America.
PLoS Comput Biol. 2023 Jul 5;19(7):e1011215. doi: 10.1371/journal.pcbi.1011215. eCollection 2023 Jul.
Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.
生物过程的机制模型可以解释观察到的现象，并预测对干扰的反应。数学模型通常使用专家知识和非正式推理来构建，以对给定的观察结果生成机制解释。尽管这种方法在数据丰富且原则完善的简单系统中效果很好，但定量生物学经常面临数据和对过程的知识都缺乏的情况，因此很难确定和验证系统行为背后所有可能的机制假设。为了克服这些限制，我们引入了一种贝叶斯多模型推理（Bayes-MMI）方法，该方法可以量化机制假设如何解释给定的实验数据集，并且同时，每个数据集如何为给定的模型假设提供信息，从而能够在可用数据的背景下探索假设空间。我们展示了这种方法来探究关于小细胞肺癌（SCLC）肿瘤生长机制中的异质性、谱系可塑性和细胞间相互作用的悬而未决的问题。我们整合了三个数据集，每个数据集都对 SCLC 肿瘤生长机制提出了不同的解释，应用 Bayes-MMI 并发现数据支持高谱系可塑性促进肿瘤进化的模型预测，而不是通过扩展罕见的干细胞样群体。此外，这些模型预测，在存在与 SCLC-N 或 SCLC-A2 亚型相关的细胞的情况下，通过中间状态从 SCLC-A 亚型向 SCLC-Y 亚型的转变会减慢。这些预测共同为 SCLC 生长中观察到的并置结果提供了可测试的假设，并为肿瘤治疗耐药性提供了机制解释。
