A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data

Area of Science:

• Systems Biology
• Computational Biology
• Cell Biology

Background:

• Cellular systems exhibit inherent variation, even in genetically identical populations.
• Understanding the sources of this cell-to-cell variation is crucial for biological insight.
• Existing methods struggle with snapshot data and parameter estimation for complex biological systems.

Purpose of the Study:

• To develop a novel computational method for analyzing cell-to-cell variation from snapshot data.
• To address limitations of existing methods in fitting mathematical models to population-level cellular data.
• To enable robust parameter inference for underdetermined biological systems.

Main Methods:

• Introduction of the Contour Monte Carlo (CMC) computational sampling method.
• CMC fits mathematical models to probability distributions of cellular properties, not raw cell data.
• The method is designed for underdetermined systems and does not require a priori cell categorization.

Main Results:

• CMC provides a computationally efficient approach that scales independently of the number of cells observed.
• The method successfully quantifies cellular variation in three distinct biological systems.
• The algorithm is straightforward to implement and does not require predefined cell clusters.

Conclusions:

• Contour Monte Carlo (CMC) offers a powerful and flexible tool for analyzing cell-to-cell variation.
• The method facilitates biological hypothesis generation and testing using snapshot data.
• Availability of Julia code promotes wider adoption and application in biological research.