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This study introduces a new method to compare chemical reaction networks (CRNs) by analyzing causal patterns in simulations. This approach quantifies dynamic behavior using weighted directed graphs, offering a more robust comparison than traditional time-series analysis.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biochemical Engineering

Background:

  • Chemical reaction networks (CRNs) are essential for simulating biological systems.
  • Gillespie's Stochastic Simulation Algorithm (SSA) is widely used for CRN simulations.
  • Current methods for comparing CRNs rely on ad hoc time-series signals, neglecting causal patterns.

Purpose of the Study:

  • To introduce a general method for quantitatively comparing CRNs' dynamic behavior.
  • To leverage causal dependencies within stochastic simulations for CRN comparison.
  • To provide a framework for analyzing resource dependencies between reactions.

Main Methods:

  • Developed a method to detect causal patterns as resource dependencies during CRN simulations.
  • Extended SSA to track and log these dependencies as weighted directed graphs.
  • Quantified CRN behavior using these discrete graph structures.

Main Results:

  • Generated weighted directed graphs that capture causal dependencies in CRN simulations.
  • Enabled quantitative comparison of CRN behaviors based on resource dependencies.
  • Demonstrated method utility on models of gene regulation and drug metabolism.

Conclusions:

  • The proposed method offers a novel way to compare CRNs by analyzing causal dependencies.
  • Weighted directed graphs provide discrete, quantitative insights into CRN dynamics.
  • This approach enhances the analysis of complex biological mechanisms through CRN simulation.