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Approximate Bayesian computation for inferring Waddington landscapes from single-cell data.

Yujing Liu1, Stephen Y Zhang1, Istvan T Kleijn2

  • 1School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.

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|July 30, 2024
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Summary
This summary is machine-generated.

This study demonstrates how approximate Bayesian computation (ABC) can calibrate mathematical models using single-cell data. Optimal distance measures are crucial for inferring model parameters and understanding developmental dynamics.

Keywords:
epigenetic landscapelikelihood free inferencequasi-potential

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell technologies provide high-resolution insights into cellular processes.
  • Characterizing cell type transitions in stem cell and developmental biology relies on snapshot transcriptional data.

Purpose of the Study:

  • To demonstrate the application of approximate Bayesian computation (ABC) for calibrating mathematical models against single-cell data.
  • To highlight the importance of appropriate distance measures for single-cell data analysis.

Main Methods:

  • Utilized simulation studies to test approximate Bayesian computation (ABC).
  • Employed optimal transport with Sinkhorn divergence as a key distance measure.
  • Inferred parameters for mathematical models using simulated single-cell data.

Main Results:

  • Showcased the critical role of selecting adequate distance measures for single-cell data.
  • Successfully inferred model parameters from simulated single-cell data using optimal distance measures.
  • Demonstrated that ABC posteriors can characterize parameter sensitivity and dependencies.

Conclusions:

  • Approximate Bayesian computation (ABC) with appropriate distance measures enables fitting mechanistic models to single-cell data.
  • Inferred parameters facilitate the construction of developmental landscape representations (e.g., Waddington/epigenetic landscape).
  • Paves the way for advanced mechanistic modeling of stem cell differentiation using single-cell resolution data.