Inferring absolute cell numbers from relative proportion in stochastic models with cell plasticity.

Area of Science:

• Mathematical Biology
• Cellular Dynamics
• Systems Biology

Background:

• Accurate quantification of dynamic cell population changes is vital for understanding biological processes like proliferation, repair, and disease.
• Relative cell proportion data offer superior reproducibility and reliability compared to direct absolute cell number measurements.
• Existing methods face limitations in precisely measuring absolute cell counts, hindering comprehensive analysis.

Purpose of the Study:

• To develop and validate mathematical mappings for inferring absolute cell numbers from relative cell proportions.
• To establish a novel approach for predicting cell population size dynamics using more robust data.
• To leverage stochastic cell dynamics for a deeper understanding of biomass interactions.

Main Methods:

• Derivation of two mathematical mappings using moment equations from stochastic cell-plasticity models.
• Utilizing variance information within cell proportion data for population size inference.
• Robustness evaluation from multiple perspectives and application extension to diverse biological mechanisms.

Main Results:

• Successfully established two distinct mathematical mappings between cell proportions and population sizes.
• Demonstrated that one mapping effectively infers population sizes without requiring initial population counts.
• Validated the robustness and broad applicability of the developed methods across various biological contexts.

Conclusions:

• The developed mathematical mappings offer a powerful new approach to quantify cell population dynamics by inferring absolute cell numbers from relative proportions.
• The ability to predict cell population sizes without initial counts, by incorporating variance, significantly advances the field.
• These findings provide valuable insights into cell plasticity and biomass interactions, overcoming limitations of direct cell counting methods.