An Estimation and Biological Model Selection

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. Citation: Abdullah A, Deris S, Mohamad MS, Anwar S (2013) An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection. PLoS ONE 8(4): e61258. doi:10.1371/journal.pone. 0061258 Editor: Aamir Ahmad, Wayne State University School of Medicine, United States of America Received July 13, 2012; Accepted March 11, 2013; Published April 11, 2013 Copyright: 2013 Abdullah et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was supported by a grant from Malaysia Ministry of Science, Technology and Innovation (MOSTI), organized by Malaysia Genome Institute (MGI) project number 07-05-MGI-GMB011 entitled ‘‘Design and Development of Microbial Cell Factories for Biomolecules Production and Secretion’’ and managed by Research Management Centre (RMC), Universiti Teknologi Malaysia (UTM) grant number 73744. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: afnizanfaizal@utm.my (AA); saberi@utm.my (MSM)