Dynamical systems in the life sciences are often composed of complex mixtures of overlapping behavioral regimes. Cellular subpopulations may shift from cycling to equilibrium dynamics or branch towards different developmental fates. The transitions between these regimes can appear noisy and irregular, posing a serious challenge to traditional, flow-based modeling techniques which assume locally smooth dynamics. To address this challenge, we propose MODE (Mixture Of Dynamical Experts), a graphical modeling framework whose neural gating mechanism decomposes complex dynamics into sparse, interpretable components, enabling both the unsupervised discovery of behavioral regimes and accurate long-term forecasting across regime transitions. Crucially, because agents in our framework can jump to different governing laws, MODE is especially tailored to the aforementioned noisy transitions. We evaluate our method on a battery of synthetic and real datasets from computational biology. First, we systematically benchmark MODE on an unsupervised classification task using synthetic dynamical snapshot data, including in noisy, few-sample settings. Next, we show how MODE succeeds on challenging forecasting tasks which simulate key cycling and branching processes in cell biology. Finally, we deploy our method on human, single-cell RNA sequencing data and show that it can not only distinguish proliferation from differentiation dynamics but also predict when cells will commit to their ultimate fate, a key outstanding challenge in computational biology.

翻译：生命科学中的动态系统通常由重叠行为模式的复杂混合组成。细胞亚群可能从循环动态转向平衡动态，或分化为不同的发育命运。这些模式之间的转换可能表现出噪声和不规则性，这对传统的基于流形的建模技术构成了严峻挑战，因为这些技术假设局部平滑动态。为应对这一挑战，我们提出MODE（混合动态专家），这是一种图建模框架，其神经门控机制将复杂动态分解为稀疏、可解释的组件，既能实现行为模式的无监督发现，又能跨模式转换进行准确长期预测。关键在于，由于我们框架中的代理可以跳转到不同的支配规律，MODE特别适用于上述噪声转换场景。我们在计算生物学的一系列合成和真实数据集上评估了该方法。首先，我们使用合成动态快照数据（包括噪声和少样本场景）在无监督分类任务上系统性地对MODE进行基准测试。接着，我们展示MODE在模拟细胞生物学关键循环和分支过程的挑战性预测任务上的成功应用。最后，我们将该方法应用于人类单细胞RNA测序数据，证明其不仅能区分增殖与分化动态，还能预测细胞何时决定其最终命运——这是计算生物学领域一个重要的未解难题。