Displaying publications 1 - 20 of 138 in total

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  1. Fulltext Efficient feature selection and classification of protein sequence data in bioinformatics
    Iqbal MJ, Faye I, Samir BB, Said AM
    ScientificWorldJournal, 2014;2014:173869.
    PMID: 25045727 DOI: 10.1155/2014/173869
    Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth.
    Matched MeSH terms: Computational Biology/methods*
  2. Fulltext NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data
    Xu J, Wang Y, Xu X, Cheng KK, Raftery D, Dong J
    Molecules, 2021 Sep 24;26(19).
    PMID: 34641330 DOI: 10.3390/molecules26195787
    In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.
    Matched MeSH terms: Computational Biology/methods*
  3. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions
    Naseer S, Ali RF, Khan YD, Dominic PDD
    J Biomol Struct Dyn, 2022;40(22):11691-11704.
    PMID: 34396935 DOI: 10.1080/07391102.2021.1962738
    Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Computational Biology/methods
  4. Special issue: Biomolecular Modelling and Simulation
    Choong YS, Mancera R, Lee VS
    Mol Biotechnol, 2024 Apr;66(4):567.
    PMID: 38337130 DOI: 10.1007/s12033-024-01073-1
    Matched MeSH terms: Computational Biology/methods
  5. A review on the computational approaches for gene regulatory network construction
    Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
    Matched MeSH terms: Computational Biology/methods*
  6. Fulltext Simulation of a Petri net-based model of the terpenoid biosynthesis pathway
    Hawari AH, Mohamed-Hussein ZA
    BMC Bioinformatics, 2010;11:83.
    PMID: 20144236 DOI: 10.1186/1471-2105-11-83
    The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN).
    Matched MeSH terms: Computational Biology/methods*
  7. Fulltext birgHPC: creating instant computing clusters for bioinformatics and molecular dynamics
    Chew TH, Joyce-Tan KH, Akma F, Shamsir MS
    Bioinformatics, 2011 May 1;27(9):1320-1.
    PMID: 21398666 DOI: 10.1093/bioinformatics/btr109
    birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.
    Matched MeSH terms: Computational Biology/methods*
  8. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing
    Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Computational Biology/methods*
  9. Fulltext Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters
    Najam M, Rasool RU, Ahmad HF, Ashraf U, Malik AW
    Biomed Res Int, 2019;2019:7074387.
    PMID: 31111064 DOI: 10.1155/2019/7074387
    Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.
    Matched MeSH terms: Computational Biology/methods*
  10. CircPrime: a web-based platform for design of specific circular RNA primers
    Sharko F, Rbbani G, Siriyappagouder P, Raeymaekers JAM, Galindo-Villegas J, Nedoluzhko A, et al.
    BMC Bioinformatics, 2023 May 19;24(1):205.
    PMID: 37208611 DOI: 10.1186/s12859-023-05331-y
    BACKGROUND: Circular RNAs (circRNAs) are covalently closed-loop RNAs with critical regulatory roles in cells. Tens of thousands of circRNAs have been unveiled due to the recent advances in high throughput RNA sequencing technologies and bioinformatic tools development. At the same time, polymerase chain reaction (PCR) cross-validation for circRNAs predicted by bioinformatic tools remains an essential part of any circRNA study before publication.

    RESULTS: Here, we present the CircPrime web-based platform, providing a user-friendly solution for DNA primer design and thermocycling conditions for circRNA identification with routine PCR methods.

    CONCLUSIONS: User-friendly CircPrime web platform ( http://circprime.elgene.net/ ) works with outputs of the most popular bioinformatic predictors of circRNAs to design specific circular RNA primers. CircPrime works with circRNA coordinates and any reference genome from the National Center for Biotechnology Information database).

    Matched MeSH terms: Computational Biology/methods
  11. Fulltext Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features
    Mahmud SMH, Goh KOM, Hosen MF, Nandi D, Shoombuatong W
    Sci Rep, 2024 Feb 05;14(1):2961.
    PMID: 38316843 DOI: 10.1038/s41598-024-52653-9
    DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
    Matched MeSH terms: Computational Biology/methods
  12. Microbial Nanotechnology for Precision Nanobiosynthesis: Innovations, Current Opportunities and Future Perspectives for Industrial Sustainability
    Khan SS, Kour D, Kaur T, Sharma A, Kumar S, Kumari S, et al.
    Curr Microbiol, 2024 Jul 01;81(8):251.
    PMID: 38954017 DOI: 10.1007/s00284-024-03772-z
    A new area of biotechnology is nanotechnology. Nanotechnology is an emerging field that aims to develope various substances with nano-dimensions that have utilization in the various sectors of pharmaceuticals, bio prospecting, human activities and biomedical applications. An essential stage in the development of nanotechnology is the creation of nanoparticles. To increase their biological uses, eco-friendly material synthesis processes are becoming increasingly important. Recent years have shown a lot of interest in nanostructured materials due to their beneficial and unique characteristics compared to their polycrystalline counterparts. The fascinating performance of nanomaterials in electronics, optics, and photonics has generated a lot of interest. An eco-friendly approach of creating nanoparticles has emerged in order to get around the drawbacks of conventional techniques. Today, a wide range of nanoparticles have been created by employing various microbes, and their potential in numerous cutting-edge technological fields have been investigated. These particles have well-defined chemical compositions, sizes, and morphologies. The green production of nanoparticles mostly uses plants and microbes. Hence, the use of microbial nanotechnology in agriculture and plant science is the main emphasis of this review. The present review highlights the methods of biological synthesis of nanoparticles available with a major focus on microbially synthesized nanoparticles, parameters and biochemistry involved. Further, it takes into account the genetic engineering and synthetic biology involved in microbial nanobiosynthesis to the construction of microbial nanofactories.
    Matched MeSH terms: Synthetic Biology/methods
  13. Converting differential-equation models of biological systems to membrane computing
    Muniyandi RC, Zin AM, Sanders JW
    Biosystems, 2013 Dec;114(3):219-26.
    PMID: 24120990 DOI: 10.1016/j.biosystems.2013.09.008
    This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand-receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.
    Matched MeSH terms: Computational Biology/methods*; Systems Biology/methods*
  14. Fulltext An improved swarm optimization for parameter estimation and biological model selection
    Abdullah A, Deris S, Mohamad MS, Anwar S
    PLoS One, 2013;8(4):e61258.
    PMID: 23593445 DOI: 10.1371/journal.pone.0061258
    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
    Matched MeSH terms: Computational Biology/methods*; Systems Biology/methods*
  15. Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure
    Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Computational Biology/methods*
  16. Fulltext Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010)
    Ranganathan S, Schönbach C, Nakai K, Tan TW
    BMC Genomics, 2010;11 Suppl 4:S1.
    PMID: 21143792 DOI: 10.1186/1471-2164-11-S4-S1
    The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet's 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 - Dec. 2, 2011 at Kuala Lumpur, Malaysia.
    Matched MeSH terms: Computational Biology/methods
  17. A Review of Gene Knockout Strategies for Microbial Cells
    Tang PW, Chua PS, Chong SK, Mohamad MS, Choon YW, Deris S, et al.
    Recent Pat Biotechnol, 2015;9(3):176-97.
    PMID: 27185502
    BACKGROUND: Predicting the effects of genetic modification is difficult due to the complexity of metabolic net- works. Various gene knockout strategies have been utilised to deactivate specific genes in order to determine the effects of these genes on the function of microbes. Deactivation of genes can lead to deletion of certain proteins and functions. Through these strategies, the associated function of a deleted gene can be identified from the metabolic networks.

    METHODS: The main aim of this paper is to review the available techniques in gene knockout strategies for microbial cells. The review is done in terms of their methodology, recent applications in microbial cells. In addition, the advantages and disadvantages of the techniques are compared and discuss and the related patents are also listed as well.

    RESULTS: Traditionally, gene knockout is done through wet lab (in vivo) techniques, which were conducted through laboratory experiments. However, these techniques are costly and time consuming. Hence, various dry lab (in silico) techniques, where are conducted using computational approaches, have been developed to surmount these problem.

    CONCLUSION: The development of numerous techniques for gene knockout in microbial cells has brought many advancements in the study of gene functions. Based on the literatures, we found that the gene knockout strategies currently used are sensibly implemented with regard to their benefits.

    Matched MeSH terms: Computational Biology/methods
  18. Fulltext Future impact of molecular biology and biotechnology on bacterial and viral diseases
    Pang T
    Med J Malaysia, 1993 Jun;48(2):101-6.
    PMID: 8350782
    The advent of recombinant DNA technology has already made a significant impact on various aspects related to the basic understanding of pathogenic mechanisms in infectious diseases, as well as practical applications related to diagnostics and prevention. The present paper discusses recent technological innovations and increased analytical capabilities which promise to have an even more significant impact on the control of viral and bacterial diseases.
    Matched MeSH terms: Molecular Biology/methods
  19. An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
    Ismail AM, Mohamad MS, Abdul Majid H, Abas KH, Deris S, Zaki N, et al.
    Biosystems, 2017 Dec;162:81-89.
    PMID: 28951204 DOI: 10.1016/j.biosystems.2017.09.013
    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.
    Matched MeSH terms: Computational Biology/methods*
  20. Prospects and progress in the production of valuable carotenoids: Insights from metabolic engineering, synthetic biology, and computational approaches
    Sankari M, Rao PR, Hemachandran H, Pullela PK, Doss C GP, Tayubi IA, et al.
    J Biotechnol, 2018 Jan 20;266:89-101.
    PMID: 29247672 DOI: 10.1016/j.jbiotec.2017.12.010
    Carotenoids are isoprenoid pigments synthesized exclusively by plants and microorganisms and play critical roles in light harvesting, photoprotection, attracting pollinators and phytohormone production. In recent years, carotenoids have been used for their health benefits due to their high antioxidant activity and are extensively utilized in food, pharmaceutical, and nutraceutical industries. Regulation of carotenoid biosynthesis occurs throughout the life cycle of plants, with vibrant changes in composition based on developmental needs and responses to external environmental stimuli. With advancements in metabolic engineering techniques, there has been tremendous progress in the production of industrially valuable secondary metabolites such as carotenoids. Application of metabolic engineering and synthetic biology has become essential for the successful and improved production of carotenoids. Synthetic biology is an emerging discipline; metabolic engineering approaches may provide insights into novel ideas for biosynthetic pathways. In this review, we discuss the current knowledge on carotenoid biosynthetic pathways and genetic engineering of carotenoids to improve their nutritional value. In addition, we investigated synthetic biological approaches for the production of carotenoids. Theoretical biology approaches that may aid in understanding the biological sciences are discussed in this review. A combination of theoretical knowledge and experimental strategies may improve the production of industrially relevant secondary metabolites.
    Matched MeSH terms: Synthetic Biology/methods*
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