  The dynamics of biomolecules are crucial for our understanding of their
functioning in living systems. However, current 3D imaging techniques, such as
cryogenic electron microscopy (cryo-EM), require freezing the sample, which
limits the observation of their conformational changes in real time. The
innovative liquid-phase electron microscopy (liquid-phase EM) technique allows
molecules to be placed in the native liquid environment, providing a unique
opportunity to observe their dynamics. In this paper, we propose TEMPOR, a
Temporal Electron MicroscoPy Object Reconstruction algorithm for liquid-phase
EM that leverages an implicit neural representation (INR) and a dynamical
variational auto-encoder (DVAE) to recover time series of molecular structures.
We demonstrate its advantages in recovering different motion dynamics from two
simulated datasets, 7bcq and Cas9. To our knowledge, our work is the first
attempt to directly recover 3D structures of a temporally-varying particle from
liquid-phase EM movies. It provides a promising new approach for studying
molecules' 3D dynamics in structural biology.
