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Advanced methods for gene network identification and noise decomposition from single-cell data.

Zhou Fang1, Ankit Gupta1, Sant Kumar1

  • 1Department of Biosystems Science and Engineering, ETH Zurich, CH-4056, Basel, Switzerland.

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|June 8, 2024
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Summary
This summary is machine-generated.

This study introduces a novel computational method to efficiently solve complex gene expression models. The approach enhances scalability for analyzing noisy biological systems and reveals cell-to-cell variations in gene expression.

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Area of Science:

  • Computational biology
  • Systems biology
  • Biophysics

Background:

  • Analyzing noisy gene expression systems is crucial for understanding cellular processes.
  • The Chemical Master Equation (CME) models these systems but faces scalability issues in high dimensions.
  • The curse of dimensionality hinders the analysis of complex biological networks.

Purpose of the Study:

  • To develop a scalable computational method for solving high-dimensional Chemical Master Equations (CMEs).
  • To enable accurate analysis of gene expression heterogeneity at the single-cell level.
  • To identify kinetic parameters in biological systems using experimental data.

Main Methods:

  • A divide-and-conquer strategy to decompose systems into leader and follower subsystems.
  • Combining Monte Carlo estimation for the leader system with stochastic filtering for follower subsystems.
  • Applying the method to identify a yeast transcription system using mRNA time-course data.

Main Results:

  • The proposed method significantly improves scalability for solving high-dimensional CMEs.
  • Accurate identification of a yeast transcription system at single-cell resolution was achieved.
  • The results allow for precise examination of rate parameter heterogeneity among isogenic cells.
  • A novel noise decomposition technique was developed for validation.

Conclusions:

  • The developed computational approach offers a scalable solution for analyzing complex gene expression dynamics.
  • This method facilitates the study of cellular heterogeneity by enabling precise parameter estimation.
  • The findings contribute to a deeper understanding of gene regulation and noise in biological systems.