Assessing biological network dynamics: comparing numerical simulations with analytical decomposition of parameter...

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Gene regulatory networks (GRNs) exhibit complex emergent dynamics.
• Accurate mathematical modeling of GRNs is hindered by parameter uncertainty.
• Experimental determination of GRN parameters remains challenging.

Purpose of the Study:

• To compare the predictive power of two distinct computational approaches for GRN dynamics: RACIPE and DSGRN.
• To assess the agreement between parameter sampling (RACIPE) and combinatorial approximation (DSGRN) methods.
• To evaluate the robustness of DSGRN predictions across different parameter ranges.

Main Methods:

• Utilized RACIPE (RAndom CIrcuit PErturbation) for parameter sampling and ensemble statistics.
• Employed DSGRN (Dynamic Signatures Generated by Regulatory Networks) for combinatorial approximation of ODE models.
• Validated predictions on four representative 2- and 3-node GRN models relevant to cellular decision-making.

Main Results:

• Demonstrated a high degree of agreement between RACIPE simulations and DSGRN predictions for the tested GRN models.
• Showcased the predictive accuracy of DSGRN even when its assumption of high Hill coefficients differs from RACIPE's broader range (1-6).
• Confirmed that DSGRN's parameter domains, defined by parameter inequalities, accurately predict ODE model dynamics within biologically relevant parameter ranges.

Conclusions:

• RACIPE and DSGRN offer complementary and highly consistent approaches for analyzing GRN dynamics under parameter uncertainty.
• DSGRN provides a robust framework for predicting GRN behavior across a wide range of biologically plausible parameters.
• The findings support the utility of DSGRN for understanding cellular decision-making processes governed by gene regulation.