A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate

An artificial immune system for solving production scheduling problems: a review

This article reviews the production scheduling problems focusing on those related to flexible job-shop scheduling. Job-shop and flexible job-shop scheduling problems are one of the most frequently encountered and hardest to optimize. This article begins with a review of the job-shop and flexible job-shop scheduling problem, and follow by the literature on artificial immune systems (AIS) and suggests ways them in solving job-shop and flexible job-shop scheduling problems. For the purposes of this study, AIS is defined as a computational system based on metaphors borrowed from the biological immune system. This article also, summarizes the direction of current research and suggests areas that might most profitably be given further scholarly attention

A novel method for protein 3D-structure similarity measure based on n-gram modeling

The present paper describes a novel method for measuring structural similarity of proteins in three dimensions. The method gets its roots from computational linguistics and the related techniques for modeling protein structure in string form and pairwise comparison of protein sequences. The method uses n-gram based modeling techniques for capturing regularities in protein structure sequences and joints cross-entropy measures for comparing two protein sequences to do similarity test. In this way, the 3D- structure of protein is represented in string form and, then, a similarity test is performed over these sequences. To find an overlap between two protein structures in 3D-space, a superposition task is also applied. In order to confirm the validity of this method, some experiments were performed using a collection of the protein data sets on publicly available servers which showed that the method is efficient

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

Conceptual modeling: present research and future opportunities

In the context of information systems, the requirements analysis may be the most important phase in information systems development. Requirements modeling used for many years different techniques from flowcharting through data flow diagrams and entity-relationship diagrams to object-oriented schema today. Conceptual modeling which is at the core of the analysis task focuses on building a faithful representation of a domain of problem that can be incorporated in the development of an information system. High quality conceptual modeling is important because it enables early detection and correction of system requirements errors. Yet little research has been undertaken on many aspects of conceptual modeling to address the fundamental question: “How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems?” (wand &weber,2002). This paper reviews the types of research that have already been undertaken and future research opportunities

An improved binary particle swarm optimisation for gene selection in classifying cancer classes

The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult because of the availability of the small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes an improved binary particle swarm optimisation to select a near-optimal (smaller) subset of informative genes that is relevant for cancer classification. Experimental results show that the performance of the proposed method is superior to a standard version of particle swarm optimisation and other related previous works in terms of classification accuracy and the number of selected genes

An iterative GASVM-based method: gene selection and classification of microarray data

Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). In this study, we propose an iterative method based on hybrid genetic algorithms to select a near-optimal (smaller) subset of informative genes in classification of the microarray data. The experimental results show that our proposed method is capable in selecting the near-optimal subset to obtain better classification accuracies than other related previous works as well as four methods experimented in this work. Additionally, a list of informative genes in the best gene subsets is also presented for biological usage

Using a dynamic bayesian network-based model for inference of escherichia coli SOS response pathway from gene expression data

Largely due to the technological advances in bioinformatics, researchers are now garnering interests in inferring gene regulatory networks (GRNs) from gene expression data which is otherwise unfeasible in the past. This is because of the need of researchers to uncover the potentially vast information and understand the dynamic behavior of the GRNs. In this regard, dynamic Bayesian network (DBN) has been broadly utilized for the inference of GRNs thanks to its ability to handle time-series microarray data and modeling feedback loops. Unfortunately, the commonly found missing values in gene expression data, and excessive computation time owing to the large search space whereby all genes are treated as potential regulators for a target gene, often impede the effectiveness of DBN in inferring GRNs. This paper proposes a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators selection which intends to decrease computation time by selecting potential regulators based on expression changes. We tested our proposed model on the Escherichia coli SOS response pathway which is responsible for repairing damaged DNA of the bacterium. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationship

Random forest and gene ontology for functional analysis of microarray data

With the development of DNA microarray technology, scientists can now measure gene expression levels. However, such high-throughput microarray technologies produce a long list of genes with small sample size and high noisy genes. The data need to be further analysed and interpreting information on biological process requires a lot of practice and usually is a time consuming process. Most of the traditional frameworks focus on selecting small subset of genes without analysing the gene list into a useful biological knowledge. Thus, we propose a model of Random Forest and GOstats. In this research, two datasets were used which included Leukemia and Prostate. This model was capable to select a small subset of genes that were informative with relevant significant GO terms which can be used in clinical and health areas. The experimental results also validated that the subset of genes selected was functionally related to carcinogenesis or tumour histogenesis