Metaheuristic optimization for parameter estimation in kinetic models of biological systems - recent development and future direction

Background: Kinetic models with predictive ability are important to be used in industrial biotechnology. However, the most challenging task in kinetic modeling is parameter estimation, which can be addressed using metaheuristic optimization methods. The methods are utilized to minimize scalar distance between model output and experimental data. Due to highly nonlinear nature of biological systems and large number of kinetic parameters, parameter estimation becomes difficult and time consuming. Methods: This paper provides a review on recent development of parameter estimation methods, which has received increasing attention in the field of systems biology. The development of metaheuristic optimization methods is mostly focused in this review along with the development of large-scale kinetic models. Results: Although a plethora of methods have been applied to the problem of parameter estimation, recent results show that most of the successful approaches are those based on hybrid methods and parallel strategies. In addition, the current software used for parameter estimation and the sources of biological data for kinetic modeling are also described in this review. This review also presents future direction in parameter estimation to meet current industrial demands, especially in systems biology applications. Conclusion: The development of numerous optimization methods for parameter estimation in kinetic models has brought much advancement in the application of systems biology. Currently, it seems that there are highly demanded for further development of efficient optimization methods to address the expansion of systems biology applications

Web services composition for concurrent plan using artificial intelligence planning

Automatic planning of web services composition is a challenging problem both in academia and real-world application. Artificial Intelligence (AI) planning can be applied to automate web services composition by depicting composition problem as AI planning problem. Web services composition would combine multiple services whenever some requirements cannot be fulfilled by a single service. Subsequently, many of the planning algorithms to detect and generate composition plan would focus only on sequence composition thus, neglecting concurrent composition. The aim of this paper is to develop an approach to generate a concurrent plan for web services composition based on semantic web services (OWL-S) and Hierarchical Task Network (HTN) Planning. A Bioinformatics case study for pathway data retrieval is used to validate the effectiveness of proposed approach. The planning algorithm extend Hierarchical Task Network (HTN) algorithm to solve the problem of automatic web service composition in the context of concurrent task planning. Experimental analysis showed that the proposed algorithms are capable of detecting and generating concurrent plan when compared with existing algorithms

An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems

Background: Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal values of kinetic parameters which may culminate in the best fit of model prediction to experimental data. Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. The improved algorithm is based on hybridization of quasi opposition-based learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a large-scale metabolic model of Chinese Hamster Ovary (CHO) cells. Results: The experimental result shows that the proposed algorithm performs better than other algorithms in terms of convergence speed and the minimum value of the objective function (loglikelihood). The estimated parameters from the experiment produce a better model by means of obtaining a reasonable good fit of model prediction to the experimental data. Conclusion: The kinetic parameters’ value obtained from our work was able to result in a reasonable best fit of model prediction to the experimental data, which contributes to a better understanding and produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building

5G NOMA user grouping using discrete particle swarm optimization approach

Non-orthogonal multiple access (NOMA) technology meets the increasing demand for high-seed cellular networks such as 5G by offering more users to be accommodated at once in accessing the cellular and wireless network. Moreover, the current demand of cellular networks for enhanced user fairness, greater spectrum efficiency and improved sum capacity further increase the need for NOMA improvement. However, the incurred interference in implementing NOMA user grouping constitutes one of the major barriers in achieving high throughput in NOMA systems. Therefore, this paper presents a computationally lower user grouping approach based on discrete particle swarm intelligence in finding the best user-pairing for 5G NOMA networks and beyond. A discrete particle swarm optimization (DPSO) algorithm is designed and proposed as a promising scheme in performing the user-grouping mechanism. The performance of this proposed approach is measured and demonstrated to have comparable result against the existing state-of-the art approach

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data

Automated biological pathway knowledge retrieval based on semantic web services composition and AI planning

This paper presents the experience gained on semantic web service composition technique applied to the bioinformatics domain. Specifically, the approach presented here consists of knowledge retrieval perspective in biological pathway. Semantic web services, annotated with domain ontology are used to describe services for pathway knowledge retrieval for Kyoto Encyclopedia of Gene and Genomes (KEGG) database. Retrieving knowledge can be seen as high level goals and the tasks involved can be decomposed into subtask to achieve the specified goals. We execute the composition of service by treating composition as planning problem using Hierarchical Task Network (HTN) planning system based on Simple Hierarchical Order Planner 2 (SHOP2). The approach for plan (task) decomposition using SHOP2 is implemented in automated way. We investigate the effectiveness of this approach by applying real world scenario in pathway information retrieval for Lactococcus Lactis (L. lactis) organism where biologists need to find out the pathway description from the given specific gene of interest

Biological data integration and web services composition using semantic web and artificial intelligence planning

Systems biology research relies on data integration and retrieval which are performed during the early stages of biological research to enable biologists to understand a wide range of biological data including gene and pathway. In data retrieval, web services are utilized to retrieve integrated data from a repository using web service composition. This service composition would combine multiple services whenever some requirements cannot be fulfilled by a single service. In web service composition, many of the planning algorithms to detect and generate composition plan would focus only on sequence composition thus, neglecting concurrent composition. Besides that, data integration methods cater to biological data which are heterogeneous and these methods focus on general data without taking into consideration specific bacterium data. The research is aimed at developing data integration and retrieval approach using semantic web and web service composition. In this approach, a semantic web and transformation method was applied to integrate protein, gene and pathway data of specific bacterium (Lactococcus Lactis) from several resources. The integrated data are expressed in Resource Description Framework (RDF) format and published in RDF repository. In the web service composition, Artificial Intelligence (AI) planning algorithm was developed to automate the composition by depicting the problem as an AI planning problem. The proposed planning algorithms extended the Hierarchical Task Network (HTN) in the context of concurrent service composition. The approach produced integrated results which were evaluated in terms of connected instances of biological data by performing query over RDF repository. Experimental analysis of service composition showed that the proposed algorithms are capable of detecting and generating concurrent plan when compared with existing algorithms. The research has extended data integration and retrieval approaches by combining them using a semantic web presented in RDF format as well as illustrated that the proposed algorithms can be applied effectively for data retrieval in the context of concurrent web service compositio

An approach for biological data integration and knowledge retrieval based on ontology, semantic web services composition, and AI planning

This chapter describes an approach involved in two knowledge management processes in biological fields, namely data integration and knowledge retrieval based on ontology, Web services, and Artificial Intelligence (AI) planning. For the data integration, Semantic Web combining with ontology is promising several ways to integrate a heterogeneous biological database. The goal of this work is to construct an integration approach for gram-positive bacteria organism that combines gene, protein, and pathway, thus allowing biological questions to be answered. The authors present a new perspective to retrieve knowledge by using Semantic Web services composition and Artificial Intelligence (AI) planning system, Simple Hierarchical Order Planner 2 (SHOP2). A Semantic Web service annotated with domain ontology is used to describe services for biological pathway knowledge retrieval at Kyoto Encyclopedia of Gene and Genomes (KEGG) database. The authors investigate the effectiveness of this approach by applying a real world scenario in pathway information retrieval for an organism where the biologist needs to discover the pathway description from a given specific gene of interest. Both of these two processes (data integration and knowledge retrieval) used ontology as the key role to achieve the biological goals An approach for biological data integration and knowledge retrieval based on ontology, semantic web services composition, and AI planning

An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems

Background: Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal values of kinetic parameters which may culminate in the best fit of model prediction to experimental data. Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. The improved algorithm is based on hybridization of quasi oppositionbased learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a largescale metabolic model of Chinese Hamster Ovary (CHO) cells. Results: The experiment result shows that the proposed algorithm performs better than other algorithms in terms of convergence speed and the minimum value of the objective function (loglikelihood). The estimated parameters from the experiment produce a better model by means of obtaining a reasonable good fit of model prediction to the experimental data Conclusion: The kinetic parameters’ value obtained from our work was able to result in a reasonable best fit of model prediction to the experimental data, which contributes to a better understanding and produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building

A Hybrid of Optimization Method for Multi-Objective Constraint Optimization of Biochemical System Production

In this paper, an advance method for multi-objective constraint optimization method of biochemical system production was proposed and discussed in detail. The proposed method combines Newton method, Strength Pareto Evolutionary Algorithm (SPEA) and Cooperative Co-evolutionary Algorithm (CCA). The main objective of the proposed method was to improve the desired production and at the same time to reduce the total of component concentrations involved in producing the best result. The proposed method starts with Newton method by treating the biochemical system as a non-linear equations system. Then, Genetic Algorithm (GA) in SPEA and CCA were used to represent the variables in non-linear equations system into multiple sub-chromosomes. The used of GA was to improve the desired production while CCA to reduce the total of component concentrations involved. The effectiveness of the proposed method was evaluated using two benchmark biochemical systems and the experimental results showed that the proposed method was able to generate the highest results compare to other existing works