Improved differential search algorithms for metabolic network optimization

The capabilities of Escherichia coli and Zymomonas mobilis to efficiently converting substrate into valuable metabolites have caught the attention of many industries. However, the production rates of these metabolites are still below the maximum threshold. Over the years, the organism strain design was improvised through the development of metabolic network that eases the process of exploiting and manipulating organism to maximize its growth rate and to maximize metabolites production. Due to the complexity of metabolic networks and multiple objectives, it is difficult to identify near-optimal knockout reactions that can maximize both objectives. This research has developed two improved modelling-optimization methods. The first method introduces a Differential Search Algorithm and Flux Balance Analysis (DSAFBA) to identify knockout reactions that maximize the production rate of desired metabolites. The latter method develops a non-dominated searching DSAFBA (ndsDSAFBA) to investigate the trade-off relationship between production rate and its growth rate by identifying knockout reactions that maximize both objectives. These methods were assessed against three metabolic networks – E.coli core model, iAF1260 and iEM439 for production of succinic acid, acetic acid and ethanol. The results revealed that the improved methods are superior to the other state-of-the-art methods in terms of production rate, growth rate and computation time. The study has demonstrated that the two improved modelling-optimization methods could be used to identify near-optimal knockout reactions that maximize production of desired metabolites as well as the organism’s growth rate within a shorter computation time

In silico gene knockout prediction using a hybrid of Bat algorithm and minimization of metabolic adjustment

Microorganisms commonly produce many high-demand industrial products like fuels, food, vitamins, and other chemicals. Microbial strains are the strains of microorganisms, which can be optimized to improve their technological properties through metabolic engineering. Metabolic engineering is the process of overcoming cellular regulation in order to achieve a desired product or to generate a new product that the host cells do not usually need to produce. The prediction of genetic manipulations such as gene knockout is part of metabolic engineering. Gene knockout can be used to optimize the microbial strains, such as to maximize the production rate of chemicals of interest. Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in Escherichia coli (E. coli)

Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

Multiobjective optimization requires of finding a trade-off between multiple objectives. However, most of the objectives are contradict towards each other, thus makes it difficult for the traditional approaches to find a solution that satisfies all objectives. Fortunately, the problems are able to solve by the aid of Pareto methods. Meanwhile, in in silico Metabolic Engineering, the identification of reaction knockout strategies that produce mutant strains with a permissible growth rate and product rate of desired metabolites is still hindered. Previously, Evolutionary Algorithms (EAs) has been successfully used in determining the reaction knockout strategies. Nevertheless, most methods work by optimizing one objective function, which is growth rate or production rate. Furthermore, in bioprocesses, it involves multiple and conflicting objectives. In this review, we aim to show the different multiobjective evolutionary optimization methods developed for tackling the multiple and conflicting objectives in in silico metabolic engineering, as well as the approaches in multiobjective optimization

Multi-Objective Optimization in Metabolomics/Computational Intelligence

The development of reliable computational models for detecting non-linear patterns encased in throughput datasets and characterizing them into phenotypic classes has been of particular interest and comprises dynamic studies in metabolomics and other disciplines that are encompassed within the omics science. Some of the clinical conditions that have been associated with these studies include metabotypes in cancer, in ammatory bowel disease (IBD), asthma, diabetes, traumatic brain injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few. The traction in this domain is attributable to the advancements in the procedures involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of a wide abundance of datasets. Throughput datasets generated by modern 1H NMR spectrometers are often characterized with features that are uninformative, redundant and inherently correlated. This renders it di cult for conventional multivariate analysis techniques to e ciently capture important signals and patterns. Therefore, the work covered in this research thesis provides novel alternative techniques to address the limitations of current analytical pipelines. This work delineates 13 variants of population-based nature inspired metaheuristic optimization algorithms which were further developed in this thesis as wrapper-based feature selection optimizers. The optimizers were then evaluated and benchmarked against each other through numerical experiments. Large-scale 1H NMR-linked datasets emerging from three disease studies were employed for the evaluations. The rst is a study in patients diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked by a distinctive facial appearance, learning disabilities, and gigantism culminating in tall stature and macrocephaly, also referred to as cerebral gigantism. Another study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative condition marked by intracellular accrual of cholesterol and complex lipids including sphingolipids and phospholipids in the endosomal/lysosomal system. The third study involved sore throat investigation in human (also known as `pharyngitis'); an acute infection of the upper respiratory tract that a ects the respiratory mucosa of the throat. In all three cases, samples from pathologically-con rmed cohorts with corresponding controls were acquired, and metabolomics investigations were performed using 1H NMR technique. Thereafter, computational optimizations were conducted on all three high-dimensional datasets that were generated from the disease studies outlined, so that key biomarkers and most e cient optimizers were identi ed in each study. The clinical and biochemical signi cance of the results arising from this work were discussed and highlighted

A hybrid of Cuckoo Search and minimization of metabolic adjustment to optimize metabolites production in genome-scale models

Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time

A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models

Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silicogene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time

A hybrid of cuckoo search and minimization of metabolic adjustment to optimize metabolites production in genome-scale models

Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time