Recovering biomolecular network dynamics from single-cell omics data requires three time points.

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Single-cell omics technologies generate high-dimensional data for studying complex biological networks.
• Current methods often lack the ability to track individual cells over time, hindering the analysis of dynamic cell state transitions.
• Understanding these dynamical phenotypes is crucial for deciphering biological processes like cell differentiation.

Purpose of the Study:

• To determine the minimum number of time-points required for single-cell omics data to reconstruct cellular dynamics.
• To investigate the feasibility of inferring network interaction matrices from time-course single-cell omics data.
• To provide a framework for designing effective single-cell omics time-course experiments.

Main Methods:

• Mathematical analysis of high-dimensional single-cell omics data.
• Numerical simulations to assess the accuracy of network reconstruction under varying conditions.
• Theoretical framework development for inferring dynamical phenotypes.

Main Results:

• Three time-points of single-cell omics data are theoretically necessary and sufficient to uniquely determine the network interaction matrix and associated dynamics.
• Accurate determination of the interaction matrix is achievable with three or more time-points, even with typical experimental noise.
• The proposed method enables data-driven phase-space analysis of cellular dynamics.

Conclusions:

• Single-cell omics time-course experiments can be designed with as few as three time-points to capture essential cellular dynamics.
• The study provides a robust computational approach for reconstructing gene regulatory networks and cell state trajectories.
• This work facilitates a deeper understanding of dynamic biological processes at the single-cell level.