Reconstructing geometry, appearance, and motion from videos is a central problem in computer vision and world modeling. In the monocular dynamic setting, this problem is severely ill-posed because the underlying 3D structure and time-varying motion must be inferred from sparse and self-motion views. Mitigating this ill-posedness requires a representation that is both temporally coherent and sufficiently constrained for monocular optimization. We introduce Gaussian Sequences, a framework that converts a monocular video into a temporally coherent sequence of 3D Gaussian primitives for dynamic reconstruction and 3D tracking. At the core of the framework is a global-local SE(3) dynamic field. Global motion patterns provide a compact representation of coherent scene-level dynamics, while local residual motion controllers add bounded corrections for fine non-rigid deformation. To further reduce monocular ambiguity, we leverage foundation-model priors as initialization cues and complementary constraints that guide geometry and motion recovery under weak observations. As an application, Gaussian sequences serve as spatio-temporal observations for differentiable particle simulation, enabling physical parameter identification from monocular videos and action-conditioned dynamics prediction. Experiments on real-world monocular videos show that our Gaussian sequences outperform strong dynamic reconstruction baselines in novel-view synthesis and point tracking. Downstream particle simulation of deformable objects further validates that the reconstructed sequences provide useful information for predictive physical modeling.