Inferring temporal dynamics from cross-sectional data using Langevin dynamics.

Area of Science:

• Computational Biology
• Systems Biology
• Data Science

Background:

• Cross-sectional studies are common due to feasibility but lack temporal information crucial for understanding dynamic systems.
• Temporal data is essential for developing predictive computational models and progressing towards causal modeling.
• Existing methods struggle to extract dynamic information from static, cross-sectional datasets.

Purpose of the Study:

• To propose a novel method for inferring computational models from cross-sectional data.
• To enable the study of system dynamics and predictive modeling using readily available cross-sectional datasets.
• To provide a baseline method for computational modeling that can be iteratively improved.

Main Methods:

• Inference of computational models using Langevin dynamics on cross-sectional data.
• Assumption of equal forces and local equilibrium for data points.
• Modeling of free energy landscapes and noise levels across data point groups.

Main Results:

• Development of stochastic differential equations that capture temporal dynamics from static data.
• Demonstration of significant predictive power when validated against longitudinal datasets.
• Successful application to systems in local equilibrium under constant forces.

Conclusions:

• The proposed Langevin dynamics method effectively infers underlying system dynamics from cross-sectional data.
• This approach facilitates the use of cross-sectional datasets for initial computational model development.
• The method serves as a foundational tool, amenable to enhancement with domain expertise for more robust modeling.