Displaying publications 41 - 60 of 128 in total

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  1. Fulltext Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization
    Choon YW, Mohamad MS, Deris S, Illias RM, Chong CK, Chai LE, et al.
    PLoS One, 2014;9(7):e102744.
    PMID: 25047076 DOI: 10.1371/journal.pone.0102744
    Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.
    Matched MeSH terms: Computational Biology/methods*
  2. Computational epigenetic profiling of CpG islets in MTHFR
    Wei K, Sutherland H, Camilleri E, Haupt LM, Griffiths LR, Gan SH
    Mol Biol Rep, 2014 Dec;41(12):8285-92.
    PMID: 25213548 DOI: 10.1007/s11033-014-3729-x
    Computational epigenetics is a new area of research focused on exploring how DNA methylation patterns affect transcription factor binding that affect gene expression patterns. The aim of this study was to produce a new protocol for the detection of DNA methylation patterns using computational analysis which can be further confirmed by bisulfite PCR with serial pyrosequencing. The upstream regulatory element and pre-initiation complex relative to CpG islets within the methylenetetrahydrofolate reductase gene were determined via computational analysis and online databases. The 1,104 bp long CpG island located near to or at the alternative promoter site of methylenetetrahydrofolate reductase gene was identified. The CpG plot indicated that CpG islets A and B, within the island, contained 62 and 75 % GC content CpG ratios of 0.70 and 0.80-0.95, respectively. Further exploration of the CpG islets A and B indicates that the transcription start sites were GGC which were absent from the TATA boxes. In addition, although six PROSITE motifs were identified in CpG B, no motifs were detected in CpG A. A number of cis-regulatory elements were found in different regions within the CpGs A and B. Transcription factors were predicted to bind to CpGs A and B with varying affinities depending on the DNA methylation status. In addition, transcription factor binding may influence the expression patterns of the methylenetetrahydrofolate reductase gene by recruiting chromatin condensation inducing factors. These results have significant implications for the understanding of the architecture of transcription factor binding at CpG islets as well as DNA methylation patterns that affect chromatin structure.
    Matched MeSH terms: Computational Biology/methods*
  3. A context evaluation approach for structural comparison of proteins using cross entropy over n-gram modelling
    Razmara J, Deris SB, Parvizpour S
    Comput Biol Med, 2013 Oct;43(10):1614-21.
    PMID: 24034753 DOI: 10.1016/j.compbiomed.2013.07.022
    The structural comparison of proteins is a vital step in structural biology that is used to predict and analyse a new unknown protein function. Although a number of different techniques have been explored, the study to develop new alternative methods is still an active research area. The present paper introduces a text modelling-based technique for the structural comparison of proteins. The method models the secondary and tertiary structure of proteins in two linear sequences and then applies them to the comparison of two structures. The technique used for pairwise comparison of the sequences has been adopted from computational linguistics and its well-known techniques for analysing and quantifying textual sequences. To this end, an n-gram modelling technique is used to capture regularities between sequences, and then, the cross-entropy concept is employed to measure their similarities. Several experiments are conducted to evaluate the performance of the method and compare it with other commonly used programs. The assessments for information retrieval evaluation demonstrate that the technique has a high running speed, which is similar to other linear encoding methods, such as 3D-BLAST, SARST, and TS-AMIR, whereas its accuracy is comparable to CE and TM-align, which are high accuracy comparison tools. Accordingly, the results demonstrate that the algorithm has high efficiency compared with other state-of-the-art methods.
    Matched MeSH terms: Computational Biology/methods*
  4. Fulltext An efficient clustering algorithm for partitioning Y-short tandem repeats data
    Seman A, Bakar ZA, Isa MN
    BMC Res Notes, 2012;5:557.
    PMID: 23039132 DOI: 10.1186/1756-0500-5-557
    Y-Short Tandem Repeats (Y-STR) data consist of many similar and almost similar objects. This characteristic of Y-STR data causes two problems with partitioning: non-unique centroids and local minima problems. As a result, the existing partitioning algorithms produce poor clustering results.
    Matched MeSH terms: Computational Biology/methods*
  5. Fulltext De novo assembly, characterization and functional annotation of pineapple fruit transcriptome through massively parallel sequencing
    Ong WD, Voo LY, Kumar VS
    PLoS One, 2012;7(10):e46937.
    PMID: 23091603 DOI: 10.1371/journal.pone.0046937
    BACKGROUND: Pineapple (Ananas comosus var. comosus), is an important tropical non-climacteric fruit with high commercial potential. Understanding the mechanism and processes underlying fruit ripening would enable scientists to enhance the improvement of quality traits such as, flavor, texture, appearance and fruit sweetness. Although, the pineapple is an important fruit, there is insufficient transcriptomic or genomic information that is available in public databases. Application of high throughput transcriptome sequencing to profile the pineapple fruit transcripts is therefore needed.

    METHODOLOGY/PRINCIPAL FINDINGS: To facilitate this, we have performed transcriptome sequencing of ripe yellow pineapple fruit flesh using Illumina technology. About 4.7 millions Illumina paired-end reads were generated and assembled using the Velvet de novo assembler. The assembly produced 28,728 unique transcripts with a mean length of approximately 200 bp. Sequence similarity search against non-redundant NCBI database identified a total of 16,932 unique transcripts (58.93%) with significant hits. Out of these, 15,507 unique transcripts were assigned to gene ontology terms. Functional annotation against Kyoto Encyclopedia of Genes and Genomes pathway database identified 13,598 unique transcripts (47.33%) which were mapped to 126 pathways. The assembly revealed many transcripts that were previously unknown.

    CONCLUSIONS: The unique transcripts derived from this work have rapidly increased of the number of the pineapple fruit mRNA transcripts as it is now available in public databases. This information can be further utilized in gene expression, genomics and other functional genomics studies in pineapple.

    Matched MeSH terms: Computational Biology/methods
  6. Isolation and cloning of microRNAs from recalcitrant plant tissues with small amounts of total RNA: a step-by step approach
    Yew CW, Kumar SV
    Mol Biol Rep, 2012 Feb;39(2):1783-90.
    PMID: 21625851 DOI: 10.1007/s11033-011-0919-7
    MicroRNAs (miRNAs) are small RNAs (sRNAs) with approximately 21-24 nucleotides in length. They regulate the expression of target genes through the mechanism of RNA silencing. Conventional isolation and cloning of miRNAs methods are usually technical demanding and inefficient. These limitations include the requirement for high amounts of starting total RNA, inefficient ligation of linkers, high amount of PCR artifacts and bias in the formation of short miRNA-concatamers. Here we describe in detail a method that uses 80 μg of total RNA as the starting material. Enhancement of the ligation of sRNAs and linkers with the use of polyethylene glycol (PEG8000) was described. PCR artifacts from the amplification of reverse-transcribed sRNAs were greatly decreased by using lower concentrations of primers and reducing the number of amplification cycles. Large concatamers with up to 1 kb in size with around 20 sRNAs/concatamer were obtained by using an optimized reaction condition. This protocol provide researchers with a rapid, efficient and cost-effective method for the construction of miRNA profiles from plant tissues containing low amounts of total RNA, such as fruit flesh and senescent leaves.
    Matched MeSH terms: Computational Biology/methods
  7. Analysis and construction of pathogenicity island regulatory pathways in Salmonella enterica serovar Typhi
    Ong SY, Ng FL, Badai SS, Yuryev A, Alam M
    J Integr Bioinform, 2010;7(1).
    PMID: 20861532 DOI: 10.2390/biecoll-jib-2010-145
    Signal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.
    Matched MeSH terms: Computational Biology/methods*
  8. Fulltext Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid
    Wilting A, Cord A, Hearn AJ, Hesse D, Mohamed A, Traeholdt C, et al.
    PLoS One, 2010;5(3):e9612.
    PMID: 20305809 DOI: 10.1371/journal.pone.0009612
    The flat-headed cat (Prionailurus planiceps) is one of the world's least known, highly threatened felids with a distribution restricted to tropical lowland rainforests in Peninsular Thailand/Malaysia, Borneo and Sumatra. Throughout its geographic range large-scale anthropogenic transformation processes, including the pollution of fresh-water river systems and landscape fragmentation, raise concerns regarding its conservation status. Despite an increasing number of camera-trapping field surveys for carnivores in South-East Asia during the past two decades, few of these studies recorded the flat-headed cat.
    Matched MeSH terms: Computational Biology/methods
  9. Fulltext Venom-gland transcriptome and venom proteome of the Malaysian king cobra (Ophiophagus hannah)
    Tan CH, Tan KY, Fung SY, Tan NH
    BMC Genomics, 2015;16:687.
    PMID: 26358635 DOI: 10.1186/s12864-015-1828-2
    The king cobra (Ophiophagus hannah) is widely distributed throughout many parts of Asia. This study aims to investigate the complexity of Malaysian Ophiophagus hannah (MOh) venom for a better understanding of king cobra venom variation and its envenoming pathophysiology. The venom gland transcriptome was investigated using the Illumina HiSeq™ platform, while the venom proteome was profiled by 1D-SDS-PAGE-nano-ESI-LCMS/MS.
    Matched MeSH terms: Computational Biology/methods
  10. Identification of hub glycogenes and their nsSNP analysis from mouse RNA-Seq data
    Firoz A, Malik A, Singh SK, Jha V, Ali A
    Gene, 2015 Dec 15;574(2):235-46.
    PMID: 26260015 DOI: 10.1016/j.gene.2015.08.012
    Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.
    Matched MeSH terms: Computational Biology/methods
  11. Fulltext Identification of putative drug targets for human sperm-egg interaction defect using protein network approach
    Sabetian S, Shamsir MS
    BMC Syst Biol, 2015;9:37.
    PMID: 26187737 DOI: 10.1186/s12918-015-0186-7
    Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis.
    Matched MeSH terms: Computational Biology/methods*
  12. Fulltext Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity
    Ng XY, Rosdi BA, Shahrudin S
    Biomed Res Int, 2015;2015:212715.
    PMID: 25802839 DOI: 10.1155/2015/212715
    This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.
    Matched MeSH terms: Computational Biology/methods
  13. Loxodonta Localizer: A Software Tool for Inferring the Provenance of African Elephants and Their Ivory Using Mitochondrial DNA
    Zhao K, Ishida Y, Green CE, Davidson AG, Sitam FAT, Donnelly CL, et al.
    J Hered, 2019 12 17;110(7):761-768.
    PMID: 31674643 DOI: 10.1093/jhered/esz058
    Illegal hunting is a major threat to the elephants of Africa, with more elephants killed by poachers than die from natural causes. DNA from tusks has been used to infer the source populations for confiscated ivory, relying on nuclear genetic markers. However, mitochondrial DNA (mtDNA) sequences can also provide information on the geographic origins of elephants due to female elephant philopatry. Here, we introduce the Loxodonta Localizer (LL; www.loxodontalocalizer.org), an interactive software tool that uses a database of mtDNA sequences compiled from previously published studies to provide information on the potential provenance of confiscated ivory. A 316 bp control region sequence, which can be readily generated from DNA extracted from ivory, is used as a query. The software generates a listing of haplotypes reported among 1917 African elephants in 24 range countries, sorted in order of similarity to the query sequence. The African locations from which haplotype sequences have been previously reported are shown on a map. We demonstrate examples of haplotypes reported from only a single locality or country, examine the utility of the program in identifying elephants from countries with varying degrees of sampling, and analyze batches of confiscated ivory. The LL allows for the source of confiscated ivory to be assessed within days, using widely available molecular methods that do not depend on a particular platform or laboratory. The program enables identification of potential regions or localities from which elephants are being poached, with capacity for rapid identification of populations newly or consistently targeted by poachers.
    Matched MeSH terms: Computational Biology/methods
  14. Fulltext An expanded mammal mitogenome dataset from Southeast Asia
    Mohd Salleh F, Ramos-Madrigal J, Peñaloza F, Liu S, Mikkel-Holger SS, Riddhi PP, et al.
    Gigascience, 2017 08 01;6(8):1-8.
    PMID: 28873965 DOI: 10.1093/gigascience/gix053
    Southeast (SE) Asia is 1 of the most biodiverse regions in the world, and it holds approximately 20% of all mammal species. Despite this, the majority of SE Asia's genetic diversity is still poorly characterized. The growing interest in using environmental DNA to assess and monitor SE Asian species, in particular threatened mammals-has created the urgent need to expand the available reference database of mitochondrial barcode and complete mitogenome sequences. We have partially addressed this need by generating 72 new mitogenome sequences reconstructed from DNA isolated from a range of historical and modern tissue samples. Approximately 55 gigabases of raw sequence were generated. From this data, we assembled 72 complete mitogenome sequences, with an average depth of coverage of ×102.9 and ×55.2 for modern samples and historical samples, respectively. This dataset represents 52 species, of which 30 species had no previous mitogenome data available. The mitogenomes were geotagged to their sampling location, where known, to display a detailed geographical distribution of the species. Our new database of 52 taxa will strongly enhance the utility of environmental DNA approaches for monitoring mammals in SE Asia as it greatly increases the likelihoods that identification of metabarcoding sequencing reads can be assigned to reference sequences. This magnifies the confidence in species detections and thus allows more robust surveys and monitoring programmes of SE Asia's threatened mammal biodiversity. The extensive collections of historical samples from SE Asia in western and SE Asian museums should serve as additional valuable material to further enrich this reference database.
    Matched MeSH terms: Computational Biology/methods
  15. Fulltext A systems biology approach towards the identification of candidate therapeutic genes and potential biomarkers for Parkinson's disease
    Sakharkar MK, Kashmir Singh SK, Rajamanickam K, Mohamed Essa M, Yang J, Chidambaram SB
    PLoS One, 2019;14(9):e0220995.
    PMID: 31487305 DOI: 10.1371/journal.pone.0220995
    Parkinson's disease (PD) is an irreversible and incurable multigenic neurodegenerative disorder. It involves progressive loss of mid brain dopaminergic neurons in the substantia nigra pars compacta (SN). We compared brain gene expression profiles with those from the peripheral blood cells of a separate sample of PD patients to identify disease-associated genes. Here, we demonstrate the use of gene expression profiling of brain and blood for detecting valid targets and identifying early PD biomarkers. Implementing this systematic approach, we discovered putative PD risk genes in brain, delineated biological processes and molecular functions that may be particularly disrupted in PD and also identified several putative PD biomarkers in blood. 20 of the differentially expressed genes in SN were also found to be differentially expressed in the blood. Further application of this methodology to other brain regions and neurological disorders should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for PD. The identification of valid peripheral biomarkers for PD may ultimately facilitate early identification, intervention, and prevention efforts as well.
    Matched MeSH terms: Computational Biology/methods
  16. Topologically significant directed random walk with applied walker network in cancer environment
    Seah CS, Kasim S, Saedudin RR, Md Fudzee MF, Mohamad MS, Hassan R, et al.
    Pak J Pharm Sci, 2019 May;32(3 Special):1395-1408.
    PMID: 31551221
    Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.
    Matched MeSH terms: Computational Biology/methods*
  17. Fulltext A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito
    Jasper M, Schmidt TL, Ahmad NW, Sinkins SP, Hoffmann AA
    Mol Ecol Resour, 2019 Sep;19(5):1254-1264.
    PMID: 31125998 DOI: 10.1111/1755-0998.13043
    Understanding past dispersal and breeding events can provide insight into ecology and evolution and can help inform strategies for conservation and the control of pest species. However, parent-offspring dispersal can be difficult to investigate in rare species and in small pest species such as mosquitoes. Here, we develop a methodology for estimating parent-offspring dispersal from the spatial distribution of close kin, using pairwise kinship estimates derived from genome-wide single nucleotide polymorphisms (SNPs). SNPs were scored in 162 Aedes aegypti (yellow fever mosquito) collected from eight close-set, high-rise apartment buildings in an area of Malaysia with high dengue incidence. We used the SNPs to reconstruct kinship groups across three orders of kinship. We transformed the geographical distances between all kin pairs within each kinship category into axial standard deviations of these distances, then decomposed these into components representing past dispersal events. From these components, we isolated the axial standard deviation of parent-offspring dispersal and estimated neighbourhood area (129 m), median parent-offspring dispersal distance (75 m) and oviposition dispersal radius within a gonotrophic cycle (36 m). We also analysed genetic structure using distance-based redundancy analysis and linear regression, finding isolation by distance both within and between buildings and estimating neighbourhood size at 268 individuals. These findings indicate the scale required to suppress local outbreaks of arboviral disease and to target releases of modified mosquitoes for mosquito and disease control. Our methodology is readily implementable for studies of other species, including pests and species of conservation significance.
    Matched MeSH terms: Computational Biology/methods*
  18. Fulltext Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome
    Tong DL, Kempsell KE, Szakmany T, Ball G
    Front Immunol, 2020;11:380.
    PMID: 32318053 DOI: 10.3389/fimmu.2020.00380
    Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.
    Matched MeSH terms: Computational Biology/methods*
  19. Shotgun Proteomics and Mass Spectrometry as a Tool for Protein Identification and Profiling of Bio-Carrier-Based Therapeutics on Human Cancer Cells
    Abidin SAZ, Othman I, Naidu R
    Methods Mol Biol, 2021;2211:233-240.
    PMID: 33336281 DOI: 10.1007/978-1-0716-0943-9_16
    Shotgun proteomics has been widely applied to study proteins in complex biological samples. Combination of high-performance liquid chromatography with mass spectrometry has allowed for comprehensive protein analysis with high resolution, sensitivity, and mass accuracy. Prior to mass spectrometry analysis, proteins are extracted from biological samples and subjected to in-solution trypsin digestion. The digested proteins are subjected for clean-up and injected into the liquid chromatography-mass spectrometry system for peptide mass identification. Protein identification is performed by analyzing the mass spectrometry data on a protein search engine software such as PEAKS studio loaded with protein database for the species of interest. Results such as protein score, protein coverage, number of peptides, and unique peptides identified will be obtained and can be used to determine proteins identified with high confidence. This method can be applied to understand the proteomic changes or profile brought by bio-carrier-based therapeutics in vitro. In this chapter, we describe methods in which proteins can be extracted for proteomic analysis using a shotgun approach. The chapter outlines important in vitro techniques and data analysis that can be applied to investigate the proteome dynamics.
    Matched MeSH terms: Computational Biology/methods
  20. 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*
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