Robust term-wise parameter estimation in biochemical Generalized Mass Action (GMA) models by hybrid regularized least squares-particle swarm optimization - Naval, Prospero C.
High-throughput analytical techniques such as nuclear magnetic resonance, protein kinase phosphorylation, and mass spectroscopy generate time dense profile of metabolites or proteins that contain structural and kinetic information about the underlying system that produced them. Experimentalists are in urgent need of computational tools that will allow efficient extraction of this information from time series data. We propose a new parameter estimation method for biochemical systems formulated as Generalized Mass Action (GMA) models known to capture the nonlinear dynamics of complex biological systems. For GMA models, it is well known that parameter estimation algorithm performance deteriorates rapidly with increasing parameter search space dimensionality. The major contributions of this work are the following: a term-wise decomposition strategy is proposed. In this strategy, the GMA model equations are decomposed into terms whose parameters are estimated one term at a time resulting in dramatic reductions in parameter space dimensionality. On the basis of this strategy, a hybrid algorithm employing Regularized Least Squares and Multi-objective Particle Swarm Optimization is developed. The algorithm is validated on two complex datasets: synthetic data from a well-characterized GMA Yeast Model and actual data from recent NMR experiments on L. lactis. This is the first time that GMA parameters for the L. lactis dataset have been successfully estimated by an algorithm.