Borsari Beatrice, Frank Mor, Wattenberg Eve S, Xu Ke, Liu Susanna X, Yu Xuezhu, Gerstein Mark
Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT, USA.
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
Nat Commun. 2025 Aug 19;16(1):7021. doi: 10.1038/s41467-025-61921-9.
Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expression and chromatin kinetics from such studies: chronODE, an interpretable framework based on ordinary differential equations. chronODE incorporates two parameters that capture biophysical constraints governing the initial cooperativity and later saturation in gene expression. These parameters group genes into three major kinetic patterns: accelerators, switchers, and decelerators. Applying chronODE to bulk and single-cell time-series data from mouse brain development reveals that most genes (~87%) follow simple logistic kinetics. Among them, genes with rapid acceleration and high saturation values are rare, highlighting biochemical limitations that prevent cells from attaining both simultaneously. Early- and late-emerging cell types display distinct kinetic patterns, with essential genes ramping up faster. Extending chronODE to chromatin, we find that genes regulated by both enhancer and silencer cis-regulatory elements are enriched in brain-specific functions. Finally, we develop a bidirectional recurrent neural network to predict changes in gene expression from corresponding chromatin changes, successfully capturing the cumulative effect of multiple regulatory elements. Overall, our framework allows investigation of the kinetics of gene regulation in diverse biological systems.
许多全基因组研究捕捉的是细胞分化或生物体发育过程中的孤立时刻。相反，纵向研究提供了一种更直接的方式来研究这些动力学过程。在此，我们展示了一种从此类研究中对基因表达和染色质动力学进行建模的方法：chronODE，一个基于常微分方程的可解释框架。chronODE纳入了两个参数，这两个参数捕捉了控制基因表达初始协同性和后期饱和度的生物物理约束。这些参数将基因分为三种主要的动力学模式：加速型、转换型和减速型。将chronODE应用于来自小鼠大脑发育的大量和单细胞时间序列数据表明，大多数基因（约87%）遵循简单的逻辑动力学。其中，具有快速加速和高饱和度值的基因很少，这突出了阻止细胞同时实现这两者的生化限制。早期和晚期出现的细胞类型表现出不同的动力学模式，关键基因的上升速度更快。将chronODE扩展到染色质，我们发现受增强子和沉默子顺式调控元件共同调控的基因在大脑特异性功能中富集。最后，我们开发了一个双向递归神经网络，从相应的染色质变化预测基因表达的变化，成功捕捉了多个调控元件的累积效应。总体而言，我们的框架允许对不同生物系统中的基因调控动力学进行研究。
