Mohd. Saberi Mohamad

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.

Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems

Abstract Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bacteria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico. The tools that could facilitate this process are known as the kinetic models. Kinetic models of cellular metabolism are important in assisting researchers to understand the rational design of biological systems, predicting metabolites production, and improving bio-products development. However, the most challenging task in model development is parameter estimation, which is the process of identifying an unknown value of model parameters which provides the best fit between the model output and a set of experimental data. Due to the increased complexity and high dimensionality of the models, which are extremely nonlinear and contain large numbers of kinetic parameters, parameter estimation is known to be difficult and time-consuming. This study proposes a cooperative enhanced scatter search with opposition-based learning schemes (CeSSOL) for parameter estimation in large-scale biology models. The method was executed in parallel with the proposed cooperative mechanism in order to exchange information (kinetic parameters) between individual threads. Each thread consists of different parameters settings that enhance the systemic properties in obtaining the global minimum. The performance of the proposed method was assessed against two large-scale microorganisms models using mammalian and bacteria cells. The results revealed that the proposed method recorded faster computation time compared to other methods. The study has also demonstrated that the proposed method can be used to provide more accurate and faster estimation of kinetic models, indicating the potential benefits of utilizing this method for expert systems of industrial biotechnology.

Single-Solution Simulated Kalman Filter Algorithm for Routing in Printed Circuit Board Drilling Process

Optimal drilling path for printed circuit board is crucial in increasing productivity and reduce production costs. Single-solution Simulated Kalman Filter (ssSKF) is a new optimizer inspired by the Kalman filtering process. It uses only a single agent to solve optimization process by finding the estimate of the optimal solution. Principally, ssSKF algorithm uses the standard Kalman filter framework, aided by a local neighborhood technique during its prediction step. This paper reveals the potential of ssSKF as a good routing method in printed circuit board (PCB) drilling process. Experimental results indicate that the ssSKF algorithm outperforms the existing methods in searching a good route to speed up a PCB drilling process.

Parameter Estimation of Essential Amino Acids in Arabidopsis thaliana Using Hybrid of Bees Algorithm and Harmony Search

Mathematical models of metabolic processes are the cornerstone of computational systems biology. In model building, the task of parameter estimation is difficult due to the huge numbers of kinetics parameters involved. The common way of estimating the parameters is to formulate it as an optimization problem. Global optimization methods can be applied by minimizing the distance between experimental data and predicted models. This paper proposes the Hybrid of Bees Algorithm and Harmony Search (BAHS) to estimate the kinetics parameters of essential amino acid production in the aspartate metabolism for Arabidopsis thaliana. The performance of the BAHS is evaluated and compared with other algorithms. The results show that BAHS performed better as it improved the performance of the original BA by 60%. Meanwhile, it takes less computational time to estimate the kinetics parameters of essential amino acid production for Arabidopsis thaliana.

A Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis for Enhancing Lactate and Succinate in Escherichia Coli.

In the past decades, metabolic engineering has received great attention from different sectors of science due to its important role in enhancing the over expression of the target phenotype by manipulating the metabolic pathway. The advent of metabolic engineering has further laid the foundation for computational biology, leading to the development of computational approaches for suggesting genetic manipulation. Previously, conventional methods have been used to enhance the production of lactate and succinate in E. coli. However, these products are always far below their theoretical maxima. In this research, a hybrid algorithm is developed to seek optimal solutions in order to increase the overproduction of lactate and succinate by gene knockout in E. coli. The hybrid algorithm employed the Simple Constrained Artificial Bee Colony (SCABC) algorithm, using swarm intelligence as an optimization algorithm to optimize the objective function, where lactate and succinate productions are maximized by simulating gene knockout in E. coli. In addition, Flux Balance Analysis (FBA) is used as a fitness function in the SCABC algorithm to assess the growth rate of E. coli and the productivity of lactate and succinate. As a result of the research, the gene knockout list which induced the highest production of lactate and succinate is obtained.

Improving particle swarm optimization via adaptive switching asynchronous – synchronous update

Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of the entire swarm is evaluated before the particles’ velocities and positions are updated, whereas in A-PSO, each particles velocity and position are updated immediately after an individuals performance is evaluated. Previous research claimed that S-PSO is better in exploitation and has fast convergence, whereas A-PSO converges at a slower rate and is stronger at exploration. Exploration and exploitation are important in ensuring good performance for any population-based metaheuristic. In this paper, an adaptive switching PSO (Switch-PSO) algorithm that uses a hybrid update sequence is proposed. The iteration strategy in Switch-PSO is adaptively switched between the two traditional iteration strategies according to the performance of the swarms best member. The performance of Switch-PSO is compared with existing S-PSO, A-PSO and three state-of-the-art PSO algorithms using CEC2014s benchmark functions. The results show that Switch-PSO achieves superior performance in comparison to the other algorithms. Switch-PSO is then applied for infinite impulse response model identification, where Switch-PSO is found to rank the best among all the algorithms applied.

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.