Displaying publications 1 - 20 of 93 in total

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  1. Fulltext In silico gene knockout prediction using a hybrid of Bat algorithm and minimization of metabolic adjustment
    Man MY, Mohamad MS, Choon YW, Ismail MA
    J Integr Bioinform, 2021 Aug 04;18(3).
    PMID: 34348418 DOI: 10.1515/jib-2020-0037
    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).
    Matched MeSH terms: Metabolic Networks and Pathways*
  2. Fulltext A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production
    Ismail MA, Deris S, Mohamad MS, Abdullah A
    PLoS One, 2015;10(5):e0126199.
    PMID: 25961295 DOI: 10.1371/journal.pone.0126199
    This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
    Matched MeSH terms: Metabolic Networks and Pathways*
  3. Fulltext Draft Genome Sequence of Phenol-Degrading Variovorax boronicumulans Strain HAB-30
    Aziz FAA, Suzuki K, Moriuchi R, Dohra H, Tashiro Y, Futamata H
    Microbiol Resour Announc, 2020 Feb 13;9(7).
    PMID: 32054711 DOI: 10.1128/MRA.01478-19
    We report the draft genome sequence of Variovorax boronicumulans strain HAB-30, which was isolated from a phenol-degrading chemostat culture. This strain contains genes encoding a multicomponent type of phenol hydroxylase, with degradation pathways for catechol and other aromatic compounds. The genome sequence will be useful for understanding the metabolic pathways involved in phenol degradation.
    Matched MeSH terms: Metabolic Networks and Pathways
  4. Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease
    Lin G, Dong L, Cheng KK, Xu X, Wang Y, Deng L, et al.
    Anal Chem, 2023 Aug 22;95(33):12505-12513.
    PMID: 37557184 DOI: 10.1021/acs.analchem.3c02246
    Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
    Matched MeSH terms: Metabolic Networks and Pathways
  5. iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions
    Wang Y, Liu X, Dong L, Cheng KK, Lin C, Wang X, et al.
    Anal Chem, 2023 Apr 18;95(15):6203-6211.
    PMID: 37023366 DOI: 10.1021/acs.analchem.2c04603
    Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.
    Matched MeSH terms: Metabolic Networks and Pathways
  6. Fulltext Draft Genome Sequence of Clostridium bifermentans Strain WYM, a Promising Biohydrogen Producer Isolated from Landfill Leachate Sludge
    Wong YM, Juan JC, Gan HM, Austin CM
    Genome Announc, 2014;2(2).
    PMID: 24604639 DOI: 10.1128/genomeA.00077-14
    Clostridium bifermentans strain WYM is an effective biohydrogen producer isolated from landfill leachate sludge. Here, we present the assembly and annotation of its genome, which may provide further insights into the metabolic pathways involved in efficient biohydrogen production.
    Matched MeSH terms: Metabolic Networks and Pathways
  7. Metabolic pathway of Cr(VI) reduction by bacteria: A review
    Ramli NN, Othman AR, Kurniawan SB, Abdullah SRS, Hasan HA
    Microbiol Res, 2023 Mar;268:127288.
    PMID: 36571921 DOI: 10.1016/j.micres.2022.127288
    Heavy metal wastes, particularly hexavalent chromium [Cr(VI)], are generated from anthropogenic activities, and their increasing abundance has been a research concern due to their toxicity, genotoxicity, carcinogenicity and mutagenicity. Exposure to these dangerous pollutants could lead to chronic infections and even mortality in humans and animals. Bioremediation using microorganisms, particularly bacteria, has gained considerable interest because it can remove contaminants naturally and is safe to the surrounding environment. Bacteria, such as Pseudomonas putida and Bacillus subtilis, can reduce the toxic Cr(VI) to the less toxic trivalent chromium Cr(III) through mechanisms including biotransformation, biosorption and bioaccumulation. These mechanisms are mostly linked to chromium reductase and nitroreductase enzymes, which are involved in the Cr(VI) reduction pathway. However, relevant data on the nitroreductase route remain insufficient. Thus, this work proposes an alternative metabolic pathway of nitroreductase, wherein nitrate activates the reaction and indirectly reduces toxic chromium. This nitroreductase pathway occurs concurrently with the chromium reduction pathway.
    Matched MeSH terms: Metabolic Networks and Pathways
  8. Advancements in medium chain fatty acids production through chain elongation: Key mechanisms and innovative solutions for overcoming rate-limiting steps
    Zhao J, Ma H, Gao M, Qian D, Wang Q, Shiung Lam S
    Bioresour Technol, 2024 Sep;408:131133.
    PMID: 39033828 DOI: 10.1016/j.biortech.2024.131133
    The depletion of fossil fuels has prompted an urgent search for alternative chemicals from renewable sources. Current technology in medium chain fatty acids (MCFAs) production though chain elongation (CE) is becoming increasingly sustainable, hence the motivation for this review, which provides the detailed description, insights and analysis of the metabolic pathways, substrates type, inoculum and fermentation process. The main rate-limiting steps of microbial MCFAs production were comprehensively revealed and the corresponding innovative solutions were also critically evaluated. Innovative strategies such as substrate pretreatment, electrochemical regulation, product separation, fermentation parameter optimization, and electroactive additives have shown significant advantages in overcoming the rate-limiting steps. Furthermore, novel regulatory strategies such as quorum sensing and electronic bifurcation are expected to further increase the MCFAs yield. Finally, the techno-economic analysis was carried out, and the future research focuses were also put forward.
    Matched MeSH terms: Metabolic Networks and Pathways
  9. Multiscale Synergy Networks Offer Insights into Disease and Comorbidity Mechanisms
    Wang Y, Zhu Z, Deng L, Cheng KK, Guo F, Lin G, et al.
    Anal Chem, 2025 Feb 18;97(6):3633-3642.
    PMID: 39908457 DOI: 10.1021/acs.analchem.4c06133
    Complex diseases involve extensive metabolic interactions within intricate biological networks. Consequently, it is advantageous to analyze metabolic phenotype data through metabolite interactions rather than focus on individual metabolites in isolation. In this article, we propose a novel analysis strategy called SynNet, which constructs multiscale synergy networks associated with specific metabolic phenotypes, offering new perspectives on the metabolic response mechanisms of diseases, including the mechanisms underlying disease comorbidity. The SynNet strategy begins with the construction of a metabolite-level synergy network (m-SynNet). This network is based on the definition and identification of significant metabolite pair interactions that distinguish disease phenotypes. Subsequently, a pathway synergy effect is defined by mapping these synergistic metabolite pairs onto the predefined metabolic pathways and performing a hypergeometric test to assess the probability of these pairs affecting a given pathway pair. The resulting significant pathway pairs identified form a pathway-level synergy network (p-SynNet). Both m-SynNet and p-SynNet offer complementary insights into disease mechanisms that go beyond conventional metabolomics analysis. For example, nodes with high connectivity in m-/p-SynNet suggest a strong correlation with the phenotype, while shared pathways across different phenotypes offer clues about the mechanisms of disease comorbidity. We applied the SynNet strategy to two real-world metabolomic data sets of disease comorbidity and identified key pathways associated with disease comorbidity from the p-SynNet. The candidate pathways are supported by the existing literature. Thus, the SynNet strategy may represent an alternative approach for metabolomic data analysis, providing novel insights into disease mechanisms and comorbidity.
    Matched MeSH terms: Metabolic Networks and Pathways
  10. Identification of metabolic pathways using pathfinding approaches: a systematic review
    Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H
    Brief Funct Genomics, 2017 03 01;16(2):87-98.
    PMID: 26969656 DOI: 10.1093/bfgp/elw002
    Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
    Matched MeSH terms: Metabolic Networks and Pathways*
  11. Fulltext Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching
    Song HS, Ahamed F, Lee JY, Henry CS, Edirisinghe JN, Nelson WC, et al.
    Sci Rep, 2025 Feb 19;15(1):6042.
    PMID: 39972043 DOI: 10.1038/s41598-025-89997-9
    Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.
    Matched MeSH terms: Metabolic Networks and Pathways*
  12. Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis
    Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J
    J Proteome Res, 2021 06 04;20(6):3204-3213.
    PMID: 34002606 DOI: 10.1021/acs.jproteome.1c00064
    Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.
    Matched MeSH terms: Metabolic Networks and Pathways
  13. Fulltext Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli
    Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA, et al.
    J Integr Bioinform, 2020 May 06;17(1).
    PMID: 32374287 DOI: 10.1515/jib-2019-0073
    The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
    Matched MeSH terms: Metabolic Networks and Pathways
  14. Bacterial community structure, predicted metabolic activities, and formation of volatile compounds attributed to Malaysian fish sauce flavour
    Shanti Dwita Lestari, Shobirin Meor Hussin A, Mustafa S, Yun Shin Sew, Ming Gan H, Hashim AM, et al.
    Food Chem, 2023 Nov 15;426:136568.
    PMID: 37437500 DOI: 10.1016/j.foodchem.2023.136568
    The fermentation of Malaysian fish sauce (budu) varies from one to twelve months depending on the producer, resulting in inconsistent quality. The microbiota, their predicted metabolic pathways and volatile metabolites profiles were determined at different stages of budu fermentation. Budu fermented for 1 and 3 months were characterized by the presence of Gram negative Enterobacterales, Gammaproteobacteria, and Fusobacteriaceae, which continuously decrease in abundance over fermentation time. The metabolic pathways prediction grouped 1- and 3- month budu in a cluster enriched with degradation reactions. 6-month budu were dominated by Halanaerobium and Staphylococcus, while the 12-month were dominated by Lentibacillus, Bacilli, and Halomonas. Biosynthesis-type predicted pathways involving protein and lipid derivatives were enriched in 6- and 12-month fermented budu, accumulating 2,6-dimethylpyrazine, methyl 2-ethyldecanoate, 2-phenylacetaldehyde, 3-methylbutanal, and 3-methylbutanoic acid. These compounds may indicate budu maturity and quality. This result may assist as a reference for quality control and fermentation monitoring.
    Matched MeSH terms: Metabolic Networks and Pathways
  15. Fulltext Integrative Roles of Dopamine Pathway and Calcium Channels Reveal a Link between Schizophrenia and Opioid Use Disorder
    Thiagarajan SK, Mok SY, Ogawa S, Parhar IS, Tang PY
    Int J Mol Sci, 2023 Feb 17;24(4).
    PMID: 36835497 DOI: 10.3390/ijms24044088
    Several theories have been proposed to explain the mechanisms of substance use in schizophrenia. Brain neurons pose a potential to provide novel insights into the association between opioid addiction, withdrawal, and schizophrenia. Thus, we exposed zebrafish larvae at 2 days post-fertilization (dpf) to domperidone (DPM) and morphine, followed by morphine withdrawal. Drug-induced locomotion and social preference were assessed, while the level of dopamine and the number of dopaminergic neurons were quantified. In the brain tissue, the expression levels of genes associated with schizophrenia were measured. The effects of DMP and morphine were compared to vehicle control and MK-801, a positive control to mimic schizophrenia. Gene expression analysis revealed that α1C, α1Sa, α1Aa, drd2a, and th1 were up-regulated after 10 days of exposure to DMP and morphine, while th2 was down-regulated. These two drugs also increased the number of positive dopaminergic neurons and the total dopamine level but reduced the locomotion and social preference. The termination of morphine exposure led to the up-regulation of th2, drd2a, and c-fos during the withdrawal phase. Our integrated data implicate that the dopamine system plays a key role in the deficits in social behavior and locomotion that are common in the schizophrenia-like symptoms and opioid dependence.
    Matched MeSH terms: Metabolic Networks and Pathways
  16. Metabolites alterations associated with obesity: A scoping review
    Abdullah A, Haron N, Mohamed E, Yusof MIM, Shahril MR
    Med J Malaysia, 2024 Mar;79(Suppl 1):158-167.
    PMID: 38555901
    INTRODUCTION: Obesity can be considered a major public health concern throughout the world. Various studies have been conducted to combat the rising number of cases of this health problem. Therefore, identifying the roots of the disease is critical in developing the desperately needed treatment approaches. However, in order to fully understand the origin of this disease, figuring out the metabolites present, and the alterations that occurred in a particular metabolism are crucial, and the information regarding the metabolites involved is limited. The aim of this study is to analyse the literature relevant to the metabolites involved in obesity conditions through a scoping review.

    MATERIALS AND METHODS: This review utilises three databases (SCOPUS, Science Direct, and PubMed). The search phrases used are (Metabolomic* OR Metabolite*) for metabolomic study, (3T3-L1 OR Adipocyte OR "Adipose Tissue") for experimental design, and (Obesity) for obesity condition. Each of the search keywords was separated by an "AND" term in the databases. Other terms related to obesity, such as insulin resistance, heart disease, type 2 diabetes, muscular disorders, respiratory problems, and psychological problems were omitted because they did not contribute to the total number of studies discovered.

    RESULTS: A total of 27 research publications were included in this scoping review. Most of the study focuses on metabolomics in obesity. Metabolites detected were found in various metabolic pathways including amino acids, carbohydrates, lipids as well as other metabolisms. Most of these metabolites discovered in obese conditions showed an alteration when compared to the level of the metabolite in normal conditions.

    CONCLUSION: Unfortunately, these studies had some limitations in which the metabolites detected varied between the articles and the information concerning the relationship between the technique or instrument utilised and the metabolites detected in the samples were not well described. Therefore, using the findings obtained in this study, it can help to determine the direction of the study in the future.

    Matched MeSH terms: Metabolic Networks and Pathways
  17. Multiobjective optimization to reconstruct biological networks
    Naef A, Abdullah R, Abdul Rashid N
    Biosystems, 2018 Sep 17;174:22-36.
    PMID: 30236951 DOI: 10.1016/j.biosystems.2018.09.003
    Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.
    Matched MeSH terms: Metabolic Networks and Pathways
  18. A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models
    Arif MA, Mohamad MS, Abd Latif MS, Deris S, Remli MA, Mohd Daud K, et al.
    Comput Biol Med, 2018 11 01;102:112-119.
    PMID: 30267898 DOI: 10.1016/j.compbiomed.2018.09.015
    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.
    Matched MeSH terms: Metabolic Networks and Pathways
  19. Fulltext RNA sequencing of kidney and liver transcriptome obtained from wild cynomolgus macaque (Macaca fascicularis) originating from Peninsular Malaysia
    Ee Uli J, Yong CS, Yeap SK, Alitheen NB, Rovie-Ryan JJ, Mat Isa N, et al.
    BMC Res Notes, 2018 Dec 22;11(1):923.
    PMID: 30577850 DOI: 10.1186/s13104-018-4014-1
    OBJECTIVE: Using high-throughput RNA sequencing technology, this study aimed to sequence the transcriptome of kidney and liver tissues harvested from Peninsular Malaysia cynomolgus macaque (Macaca fascicularis). M. fascicularis are significant nonhuman primate models in the biomedical field, owing to the macaque's biological similarities with humans. The additional transcriptomic dataset will supplement the previously described Peninsular Malaysia M. fascicularis transcriptomes obtained in a past endeavour.

    RESULTS: A total of 75,350,240 sequence reads were obtained via Hi-seq 2500 sequencing technology. A total of 5473 significant differentially expressed genes were called. Gene ontology functional categorisation showed that cellular process, catalytic activity, and cell part categories had the highest number of expressed genes, while the metabolic pathways category possessed the highest number of expressed genes in the KEGG pathway analysis. The additional sequence dataset will further enrich existing M. fascicularis transcriptome assemblies, and provide a dataset for further downstream studies.

    Matched MeSH terms: Metabolic Networks and Pathways
  20. Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis
    Ghosh P, Kumar M, Kapoor R, Kumar SS, Singh L, Vijay V, et al.
    Bioresour Technol, 2020 Jan;296:122275.
    PMID: 31683109 DOI: 10.1016/j.biortech.2019.122275
    The present study intends to evaluate the potential of co-digestion for utilizing Organic fraction of Municipal Solid Waste (OFMSW) and sewage sludge (SS) for enhanced biogas production. Metagenomic analysis was performed to identify the dominant bacteria, archaea and fungi, changes in their communities with time and their functional roles during the course of anaerobic digestion (AD). The cumulative biogas yield of 586.2 mL biogas/gVS with the highest methane concentration of 69.5% was observed under an optimum ratio of OFMSW:SS (40:60 w/w). Bacteria and fungi were found to be majorly involved in hydrolysis and initial stages of AD. Probably, the most common archaea Methanosarsina sp. primarily followed the acetoclastic pathway. The hydrogenotrophic pathway was less followed as indicated by the reduction in abundance of syntrophic acetate oxidizers. An adequate understanding of microbial communities is important to manipulate and inoculate the specific microbial consortia to maximize CH4 production through AD.
    Matched MeSH terms: Metabolic Networks and Pathways
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