An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

Abstract

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

“…Table S15 contains links for selected authors having papers concerning parameter estimation in BRNs or in dynamic systems. 1 1 1 1 Abdullah et al (2013b) . Abdullah et al (2013a) .…”

Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis

“…In the future because biological models incorporate a set of parameters that represent the physical properties of real biological systems, it is advisable to extend the capability of the parameter estimation method in dealing with the structural nonidentifiability problem. This is because the problem often involves prior knowledge of the structure of the model, which can lead to more discoveries while selecting possible routes of the pathways that are particularly important in the field of bioengineering [24]. …”

Comparison across growth kinetic models of alkaline protease production in batch and fed-batch fermentation using hybrid genetic algorithm and particle swarm optimization

“…Formally, the objective function of the problem is usually intended to minimize the difference between the model outputs produced by the estimated parameters and the respective experimental measurements. [22] In this study, the objective function ( Equation 12) is presented as a minimization of a distance measure ' between the experimental and the calculated values of the main state variables (X, S, and P):…”