Yeo, H. C., Vijay, V., & Selvarajoo, K. (2024). Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises. BMC Biology, 22(1). https://doi.org/10.1186/s12915-024-02019-4
Abstract:
Background The transition from explanative modeling of fitted data to the predictive modeling of unseen data
for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy
of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect
of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations:
generalized mass action (GMA), Michaelis–Menten, linear-logarithmic, and convenience kinetics. We then compare
the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization
in kinetic parameter hyperspace to determine the corresponding estimated parameters.
Results We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find
the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-
logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform
more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the
most efficacious for estimating Michaelis–Menten parameters regardless of noise, while achieving numerous folds
saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis–Menten,
and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters
of convenience kinetics using any algorithm.
Conclusions Altogether, we identify a protocol for predicting reaction parameters under marked measurement
noise, as a step towards predictive modeling for systems biology endeavors.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research is supported by core funding from: ASTAR - Bioinformatics Institute (BII).