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Inference of Boolean networks under constraint on bidirectional gene relationships.

G Vahedi1, I V Ivanov, E R Dougherty

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. golnaz@tamu.edu

IET Systems Biology
|May 20, 2009
PubMed
Summary

This study addresses spurious attractor cycles in Boolean networks (BNs) inferred using coefficient of determination (CoD) from steady-state data. A new constrained CoD algorithm improves network inference by reducing these cycles.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Inference

Background:

  • Coefficient of determination (CoD) infers Boolean networks (BNs) from steady-state data, prioritizing predictive gene relationships.
  • Unconstrained CoD inference can lead to spurious attractor cycles due to lack of temporal data and resulting bidirectional gene relationships.
  • These spurious cycles create unaccounted steady-state probability mass, indicating poor inference relative to network dynamics.

Purpose of the Study:

  • To characterize how bidirectional gene relationships impact the attractor structure of Boolean networks.
  • To develop and evaluate a constrained CoD inference algorithm that mitigates spurious attractor cycles.
  • To compare the performance of the constrained algorithm against unconstrained CoD inference using a melanoma-based network.

Main Methods:

  • Characterization of the effect of bidirectional gene relationships on Boolean network attractor structure.
  • Development of a constrained coefficient of determination (CoD) inference algorithm.
  • Comparative performance analysis of constrained and unconstrained CoD inference algorithms on a melanoma-based network.

Main Results:

  • Bidirectional gene relationships were characterized for their impact on Boolean network attractor structure.
  • The proposed constrained CoD inference algorithm demonstrated superior performance in avoiding spurious non-singleton attractors compared to unconstrained CoD.
  • The constrained algorithm showed improved inference accuracy relative to the attractor structure.

Conclusions:

  • Constraining the coefficient of determination (CoD) inference process effectively reduces spurious attractor cycles in Boolean networks.
  • The developed constrained CoD algorithm offers a more accurate network inference from steady-state data, particularly in avoiding artifacts like spurious attractors.
  • This improved inference method has implications for understanding gene regulatory networks, as demonstrated by its application to a melanoma-based network.