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Steady state statistical correlations predict bistability in reaction motifs.

Suchana Chakravarty1, Debashis Barik1

  • 1School of Chemistry, University of Hyderabad, Central University P.O., Hyderabad, 500046, Telangana, India. dbariksc@uohyd.ac.in.

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This study introduces a novel method to identify intrinsic bistability in cellular feedback networks by analyzing moments and cumulants. This approach accurately detects true bistability, distinguishing it from mere response bimodality, and is applicable to single-cell protein data.

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Area of Science:

  • Cellular biology
  • Systems biology
  • Biophysics

Background:

  • Cellular decision-making relies on bistable switches converting graded inputs to binary outputs.
  • Traditional methods for identifying bistability involve hysteresis or response distribution bimodality.
  • Positive feedback loops are key mechanisms generating bistable switches.

Purpose of the Study:

  • To propose a new method for identifying intrinsic bistability in feedback-regulated cellular networks.
  • To differentiate true bistability from systems exhibiting only marginal response bimodality.
  • To provide a framework for analyzing single-cell protein data.

Main Methods:

  • Correlating higher-order moments and cumulants (≥2) of joint steady-state distributions.
  • Performing stochastic simulations of four feedback-regulated models with intrinsic bistability.
  • Analyzing the relationship between variance and covariance, and cross-cumulants.

Main Results:

  • Identified a characteristic cusp-shaped curve in steady-state variance vs. covariance for bistable switches.
  • Observed a closed-loop structure in (n+1)th order vs. nth order cross-cumulants (for n≥3).
  • Demonstrated the method's ability to identify systems lacking intrinsic bistability, even with bimodal marginal distributions.

Conclusions:

  • The proposed moment and cumulant correlation method reliably detects intrinsic bistability in cellular networks.
  • This approach offers a robust alternative to traditional methods, especially for complex systems.
  • The technique is suitable for analyzing experimental single-cell protein data from techniques like flow cytometry.