Relevant bibliographies by topics / Microbial bioinformatics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Microbial bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Microbial bioinformatics"

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Pallen, Mark J. "Microbial bioinformatics 2020." Microbial Biotechnology 9, no. 5 (2016): 681–86. http://dx.doi.org/10.1111/1751-7915.12389.

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Du, Rui Fang, Jing Yu Li, Jian Li Liu, and Ji Zhao Zhao. "Application of Bioinformatics in Microbial Ecology." Advanced Materials Research 955-959 (June 2014): 276–80. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.276.

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The major goal of microbial ecology is to study the structure and function of complex microbial communities. Various bioinformatics software were employed to handle a large number of genomic information emerged by using high throughput sequencing. This paper summarizes application of bioinformatics in microbial ecology and their corresponding software used in α, β-diversity studies; and finally expounds the important roles in establishment of four synthesis databases.
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Mbareche, Hamza, Nathan Dumont-Leblond, Guillaume J. Bilodeau, and Caroline Duchaine. "An Overview of Bioinformatics Tools for DNA Meta-Barcoding Analysis of Microbial Communities of Bioaerosols: Digest for Microbiologists." Life 10, no. 9 (2020): 185. http://dx.doi.org/10.3390/life10090185.

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High-throughput DNA sequencing (HTS) has changed our understanding of the microbial composition present in a wide range of environments. Applying HTS methods to air samples from different environments allows the identification and quantification (relative abundance) of the microorganisms present and gives a better understanding of human exposure to indoor and outdoor bioaerosols. To make full use of the avalanche of information made available by these sequences, repeated measurements must be taken, community composition described, error estimates made, correlations of microbiota with covariates (variables) must be examined, and increasingly sophisticated statistical tests must be conducted, all by using bioinformatics tools. Knowing which analysis to conduct and which tools to apply remains confusing for bioaerosol scientists, as a litany of tools and data resources are now available for characterizing microbial communities. The goal of this review paper is to offer a guided tour through the bioinformatics tools that are useful in studying the microbial ecology of bioaerosols. This work explains microbial ecology features like alpha and beta diversity, multivariate analyses, differential abundances, taxonomic analyses, visualization tools and statistical tests using bioinformatics tools for bioaerosol scientists new to the field. It illustrates and promotes the use of selected bioinformatic tools in the study of bioaerosols and serves as a good source for learning the “dos and don’ts” involved in conducting a precise microbial ecology study.
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Tabassum Khan, Nida. "The Emerging Role of Bioinformatics in Biotechnology." Journal of Biotechnology and Biomedical Science 1, no. 3 (2018): 13–24. http://dx.doi.org/10.14302/issn.2576-6694.jbbs-18-2173.

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Bioinformatic tools is widely used to manage the enormous genomic and proteomic data involving DNA/protein sequences management, drug designing, homology modelling, motif/domain prediction ,docking, annotation and dynamic simulation etc. Bioinformatics offers a wide range of applications in numerous disciplines such as genomics. Proteomics, comparative genomics, nutrigenomics, microbial genome, biodefense, forensics etc. Thus it offers promising future to accelerate scientific research in biotechnology
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Alkema, Wynand, Jos Boekhorst, Michiel Wels, and Sacha A. F. T. van Hijum. "Microbial bioinformatics for food safety and production." Briefings in Bioinformatics 17, no. 2 (2015): 283–92. http://dx.doi.org/10.1093/bib/bbv034.

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Anand, Deepsikha, Jeya Nasim, Sangeeta Yadav, and Dinesh Yadav. "Bioinformatics Insights Into Microbial Xylanase Protein Sequences." Biosciences, Biotechnology Research Asia 15, no. 2 (2018): 275–94. http://dx.doi.org/10.13005/bbra/2631.

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Microbial xylanases represents an industrially important group of enzymes associated with hydrolysis of xylan, a major hemicellulosic component of plant cell walls. A total of 122 protein sequences comprising of 58 fungal, 25 bacterial, 19actinomycetes and 20 yeasts xylanaseswere retrieved from NCBI, GenBank databases. These sequences were in-silico characterized for homology,sequence alignment, phylogenetic tree construction, motif assessment and physio-chemical attributes. The amino acid residues ranged from 188 to 362, molecular weights were in the range of 20.3 to 39.7 kDa and pI ranged from 3.93 to 9.69. The aliphatic index revealed comparatively less thermostability and negative GRAVY indicated that xylanasesarehydrophilicirrespective of the source organisms.Several conserved amino acid residues associated with catalytic domain of the enzyme were observed while different microbial sources also revealed few conserved amino acid residues. The comprehensive phylogenetic tree indicatedsevenorganismsspecific,distinct major clusters,designated as A, B, C, D, E, F and G. The MEME based analysis of 10 motifs indicated predominance of motifs specific to GH11 family and one of the motif designated as motif 3 with sequence GTVTSDGGTYDIYTTTRTNAP was found to be present in most of the xylanases irrespective of the sources.Sequence analysis of microbial xylanases provides an opportunity to develop strategies for molecular cloning and expression of xylanase genes and also foridentifying sites for genetic manipulation for developing novel xylanases with desired features as per industrial needs.
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Carriço, J. A., M. Rossi, J. Moran-Gilad, G. Van Domselaar, and M. Ramirez. "A primer on microbial bioinformatics for nonbioinformaticians." Clinical Microbiology and Infection 24, no. 4 (2018): 342–49. http://dx.doi.org/10.1016/j.cmi.2017.12.015.

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Theil, Sebastien, and Etienne Rifa. "rANOMALY: AmplicoN wOrkflow for Microbial community AnaLYsis." F1000Research 10 (January 7, 2021): 7. http://dx.doi.org/10.12688/f1000research.27268.1.

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Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods. Our package produces ready to publish figures, and all of its outputs are made to be integrated in Rmarkdown code to produce automated reports.
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Baldini, Federico, Almut Heinken, Laurent Heirendt, Stefania Magnusdottir, Ronan M. T. Fleming, and Ines Thiele. "The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities." Bioinformatics 35, no. 13 (2018): 2332–34. http://dx.doi.org/10.1093/bioinformatics/bty941.

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Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.
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Sugawara, H., S. Miyazaki, J. Shimura, and Y. Ichiyanagi. "Bioinformatics tools for the study of microbial diversity." Journal of Industrial Microbiology & Biotechnology 17, no. 5-6 (1996): 490–97. http://dx.doi.org/10.1007/bf01574780.

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Dissertations / Theses on the topic "Microbial bioinformatics"

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Hooper, Sean. "Dynamics of Microbial Genome Evolution." Doctoral thesis, Uppsala University, Molecular Evolution, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3283.

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<p>The success of microbial life on Earth can be attributed not only to environmental factors, but also to the surprising hardiness, adaptability and flexibility of the microbes themselves. They are able to quickly adapt to new niches or circumstances through gene evolution and also by sheer strength of numbers, where statistics favor otherwise rare events.</p><p>An integral part of adaptation is the plasticity of the genome; losing and acquiring genes depending on whether they are needed or not. Genomes can also be the birthplace of new gene functions, by duplicating and modifying existing genes. Genes can also be acquired from outside, transcending species boundaries. In this work, the focus is set primarily on duplication, deletion and import (lateral transfer) of genes – three factors contributing to the versatility and success of microbial life throughout the biosphere. </p><p>We have developed a compositional method of identifying genes that have been imported into a genome, and the rate of import/deletion turnover has been appreciated in a number of organisms. Furthermore, we propose a model of genome evolution by duplication, where through the principle of gene amplification, novel gene functions are discovered within genes with weak- or secondary protein functions. Subsequently, the novel function is maintained by selection and eventually optimized. Finally, we discuss a possible synergic link between lateral transfer and duplicative processes in gene innovation.</p>
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Meng, Da. "Bioinformatics tools for evaluating microbial relationships." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Dissertations/Spring2009/d_meng_042209.pdf.

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Thesis (Ph. D.)--Washington State University, May 2009.<br>Title from PDF title page (viewed on June 8, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references.
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Atkinson, Samantha Nicole. "Bioinformatic assessment of disrupted microbial communities." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6696.

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Bioinformatics is a unique field in that it incorporates many different disciplines, including biology, computer science, and statistics, to study biological data. There is a vast array of techniques that utilize bioinformatics, including pangenomics, RNASeq, whole genome metagenomics, and 16S sequencing. To study bacterial interactions, we used a model system of species interactions, Myxococcus xanthus. M. xanthus is a soil bacterium that is a known predator of other bacteria. It has one of the largest repertoires of two component systems (TCS) to respond to external stresses. TCS are a pair of proteins, one that senses environmental stress (histidine kinase, HK) and another that usually acts as a transcriptional regulator (response regulators, RR). We studied a class of RRs, NtrC-like, reliant on an alternative sigma factor, sigma54. The oligomerization of NtrC-like RRs is regulated to modulate activation of the protein, which would change the bacterium’s ability to respond to its environment. We studied HsfA, a NtrC-like RR that regulates specialized metabolites. Specialized metabolites are used in bacterial interactions. In predation interactions they are used to kill prey. Our goal was to find genes that might be involved in specialized metabolite production that would aid in predation. We used prediction tools to find putative binding sites of HsfA to find potentially new metabolites. We used two motifs to attempt to predict if the oligomerization of these response regulators is positively or negatively regulated. We found that the presence of a motif in the receiver domain to be associated with negative regulation of oligomerization, but further studies are needed to experimentally confirm this finding. One environment in which bacterial interactions occur is in the gut. The gut microbiome is the consortium of organisms and their genomic content in the gastrointestinal tract. The gut microbiome is sensitive to aspects of a person’s lifestyle, such as diet and medication. Here we studied the effect of two different diets and two drugs on the gut microbiome. Risperidone, an antipsychotic used to treat schizophrenia and bipolar disorder, has been shown to cause obesity and diabetes. We studied the effect of diet and risperidone usage on weight gain and the microbiome using a C57Bl/6J female mouse model. Our results show that diet has a strong impact on the microbial composition of the gut in response to risperidone. As many mental health patients stop and restart their medication, we examined the effect of stopping and restarting risperidone on the microbiome. When risperidone is stopped the microbiome reverts to a state similar to the control group but diverges into a different microbial composition upon restarting treatment. Interestingly, mice did not gain significantly more weight than their control group upon the second risperidone treatment. Further studies are needed to examine the functional changes occurring with the stop and restart of risperidone to determine the mechanism of mice resisting weight gain during the second round of treatment. Captopril is used to treat hypertension, a very common disease in the United States. Here we studied the effect of captopril on weight gain, metabolic phenotypes, and the gut microbiome. Our results showed that captopril caused an increase in resting metabolic rate (RMR) in mice. This occurred through an increase in energy expenditure. This increase in RMR had the effect of captopril-treated mice being resistant to weight gain. Our group has previously shown that the gut microbiome can directly affect RMR. Therefore, we studied the gut microbiome of captopril-treated mice. We observed a shift in their gut microbiome to organisms Akkermansia muciniphila and Lactobacillus, associated with lean body mass. Captopril therefore has the potential to be a better medication to treat patients with both hypertension and obesity. Further studies are needed to determine the effect of captopril on the microbiome in a hypertension mouse model.
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Jones, Katy June. "Bioinformatic analysis of biotechnologically important microbial communities." Thesis, University of Exeter, 2018. http://hdl.handle.net/10871/34543.

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Difficulties associated with the study of microbial communities, such as low proportions of cultivable species, have been addressed in recent years with the advent of a range of sequencing technologies and bioinformatic tools. This is enabling previously unexplored communities to be characterised and utilised in a range of biotechnology applications. In this thesis bioinformatic methods were applied to two datasets of biotechnological interest: microbial communities found living with the oil-producing alga Botryococcus braunii and microbial communities in acid mine drainage (AMD). B. braunii is of high interest to the biofuel industry due to its ability to produce high amounts of oils, in the form of hydrocarbons. However, a number of factors, including low growth rates, have prevented its cultivation on an industrial scale. Studies show B. braunii lives in a consortium with numerous bacteria which may influence its growth. This thesis reports both whole genome analysis and 16S rRNA gene sequence analysis to gain a greater understanding of the B. braunii bacterial consortium. Bacteria have been identified, some of which had not previously been documented as living with B. braunii, and evidence is presented for ways in which they may influence growth of the alga, including B-vitamin synthesis and secretion systems. AMD is a worldwide problem, polluting the environment and negatively impacting on human health. This by-product of the mining industry is a problem in the South West of England, where disused metalliferous mines are now a source of AMD. Bioremediation of AMD is an active area of research; sulphur-reducing bacteria and other bacteria which can remove toxic metals from AMD can be utilised for this purpose. Identifying bacteria and archaea that are able to thrive in AMD and which also have these bioremediation properties is therefore of great importance. Metagenomic sequencing has been carried out on the microbial community living in AMD sediment at the Wheal Maid tailings lagoon near Penryn in Cornwall. From these data have been identified a diverse range of bacteria and archaea present at both the sediment surface level and at depth, including microorganisms closely related to taxa reported from metalliferous mines on other continents. Evidence has been found of sulphur-reducing bacteria and of pathways for various other bioremediation-linked processes.
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Brown, Shawn Paul. "Rules and patterns of microbial community assembly." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18324.

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Doctor of Philosophy<br>Division of Biology<br>Ari M. Jumpponen<br>Microorganisms are critically important for establishing and maintaining ecosystem properties and processes that fuel and sustain higher-trophic levels. Despite the universal importance of microbes, we know relatively little about the rules and processes that dictate how microbial communities establish and assemble. Largely, we rely on assumptions that microbial community establishment follow similar trajectories as plants, but on a smaller scale. However, these assumptions have been rarely validated and when validation has been attempted, the plant-based theoretical models apply poorly to microbial communities. Here, I utilized genomics-inspired tools to interrogate microbial communities at levels near community saturation to elucidate the rules and patterns of microbial community assembly. I relied on a community filtering model as a framework: potential members of the microbial community are filtered through environmental and/or biotic filters that control which taxa can establish, persist, and coexist. Additionally, I addressed whether two different microbial groups (fungi and bacteria) share similar assembly patterns. Similar dispersal capabilities and mechanisms are thought to result in similar community assembly rules for fungi and bacteria. I queried fungal and bacterial communities along a deglaciated primary successional chronosequence to determine microbial successional dynamics and to determine if fungal and bacterial assemblies are similar or follow trajectories similar to plants. These experiments demonstrate that not only do microbial community assembly dynamics not follow plant-based models of succession, but also that fungal and bacterial community assembly dynamics are distinct. We can no longer assume that because fungi and bacteria share small propagule sizes they follow similar trends. Further, additional studies targeting biotic filters (here, snow algae) suggest strong controls during community assembly, possibly because of fungal predation of the algae or because of fungal utilization of algal exudates. Finally, I examined various technical aspects of sequence-based ecological investigations. These studies aimed to improve microbial community data reliability and analyses.
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Montana, Aldrin. "Algorithms for Library-based Microbial Source Tracking." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/959.

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Pyroprinting is a novel, library-based microbial source tracking method developed by the Biology department at Cal Poly, San Luis Obispo. This method consists of two parts: (1) a collection of bacterial fingerprints, called pyroprints, from known host species, and (2) a method for pyroprint comparison. Currently, Cal Poly Library of Pyroprints (CPLOP), a web-based database application, provides storage and analysis of over $10000$ pyroprints. This number is quickly growing as students and researchers continue to use pyroprinting for research. Biologists conducting research using pyroprinting rely on methods for partitioning collected bacterial isolates into bacterial strains. Clustering algorithms are commonly used for bacterial strain analysis of organisms in computational biology. Unfortunately, agglomerative hierarchical clustering, a commonly used clustering algorithm, is inadequate given the nature of data collection for pyroprinting. While the clusters produced by agglomerative hierarchical clustering are acceptable, pyroprinting requires a method of analysis that is scalable and incorporates useful metadata into the clustering process. We propose ontology-based hierarchical clustering (OHClust!), a modification of agglomerative hierarchical clustering that expresses metadata-based relationships as an ontology to direct the order in which hierarchical clustering algorithms analyze the data. In this thesis, the strengths and weaknesses of OHClust! are discussed, and its performance is analyzed in comparison to agglomerative hierarchical clustering.
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Shankar, Vijay. "Extension of Multivariate Analyses to the Field of Microbial Ecology." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464358122.

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Sanchez, Rhea I. "Annotation consistency tool : the assessment of JCVI microbial genome annotations /." Online version of thesis, 2009. http://hdl.handle.net/1850/10653.

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Colby, Graham. "Microbial Responses to Environmental Change in Canada’s High Arctic." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39254.

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The Arctic is undergoing a rapid environmental shift with increasing temperatures and precipitations expected to continue over the next century. Yet, little is known about how microbial communities and their underlying metabolic processes will respond to ongoing climatic changes. To address this question, we focused on Lake Hazen, NU, Canada. As the largest High Arctic lake by volume, it is a unique site to investigate microbial responses to environmental changes. Over the past decade, glacial coverage of the lake has declined. Increasing glacial runoff and sedimentation rates in the lake has resulted in differential influx of nutrients through spatial gradients. I used these spatial gradients to study how environmental changes might affect microbial community structure and functional capacity in Arctic lakes. I performed a metagenomic analysis of microbial communities from hydrological regimes representing high, low, and negligible influence of glacial runoff and compared the observed structure and function to the natural geochemical gradients. Genes and reconstructed genomes found in different abundances across these sites suggest that high-runoff regimes alter geochemical gradients, homogenise the microbial structure, and reduce genetic diversity. This work shows how a genome-centric metagenomics approach can be used to predict future microbial responses to a changing climate.
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He, Lian. "Development of Steady-State and Dynamic Flux Models for Broad-Scope Microbial Metabolism Analysis." Thesis, Washington University in St. Louis, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10103229.

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<p> Flux analysis techniques, including flux balance analysis (FBA) and 13C-metabolic flux analysis (MFA), can characterize carbon and energy flows through a cell&rsquo;s metabolic network. By employing both 13C-labeling experiments and nonlinear programming, 13C-MFA provides a rigorous way of examining cell flux distributions in the central metabolism. FBA, on the other hand, gives a holistic review of optimal fluxomes on the genome scale. In this dissertation, flux analysis techniques were constructed to investigate the microbial metabolisms. First, an open-source and programming-free platform of 13C-MFA (WUFlux) with a user-friendly interface in MATLAB was developed, which allowed both mass isotopomer distribution (MID) analysis and metabolic flux calculations. Several bacterial templates with diverse substrate utilizations were included in this platform to facilitate 13C-MFA model construction. The corrected MID data and flux profiles resulting from our platform have been validated by other available 13C-MFA software. Second, 13C-MFA was applied to investigate the variations of bacterial metabolism in response to genetic manipulations or changing growth conditions. Specifically, we investigated the central metabolic responses to overproduction of fatty acids in Escherichia coli and the carbon flow distributions of Synechocystis sp. PCC 6803 under both photomixotrophic and photoheterotrophic conditions. By employing the software of isotopomer network compartmental analysis, we performed isotopically non-stationary MFA on Synechococcus elongatus UTEX 2973. The 13C-based analysis was also conducted for other non-model species, such as Chloroflexus aurantiacus. The resulting flux distributions detail how cells manage the trade-off between carbon and energy metabolisms to survive under stressed conditions, support high productions of biofuel, or organize the metabolic routes for sustaining biomass growth. Third, conventional FBA is suitable for only steady-state conditions. To describe the environmental heterogeneity in bioreactors and temporal changes of cell metabolism, we integrated genome-scale FBA with growth kinetics (time-dependent information) and cell hydrodynamic movements (space-dependent information). A case study was subsequently carried out for wild-type and engineered cyanobacteria, in which a heterogeneous light distribution in photobioreactors was considered in the model. The resulting integrated genome-scale model can offer insights into both intracellular and extracellular domains and facilitate the analysis of bacterial performance in large-scale fermentation systems. Both steady-state and dynamic flux analysis models can offer insights into metabolic responses to environmental fluctuations and genetic modifications. They are also useful tools to provide rational strategies of constructing microbial cell factories for industrial applications. </p>
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Books on the topic "Microbial bioinformatics"

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Osterman, Andrei L., and Svetlana Y. Gerdes, eds. Microbial Gene Essentiality: Protocols and Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-321-9.

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Ussery, David W. Computing for comparative microbial genomics: Bioinformatics for microbiologists. Springer, 2009.

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Ussery, David W. Computing for comparative microbial genomics: Bioinformatics for microbiologists. Springer, 2009.

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Wunch, Kenneth, Marko Stipaničev, and Max Frenzel. Microbial Bioinformatics in the Oil and Gas Industry. CRC Press, 2021. http://dx.doi.org/10.1201/9781003023395.

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National Research Council (U.S.). Committee On Metagenomics: Challenges and Functional Applications. The new science of metagenomics: Revealing the secrets of our microbial planet. National Academies Press, 2007.

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Misha, Gromov, Harel-Bellan Annick, Morozova Nadya, Pritchard Linda Louise, and SpringerLink (Online service), eds. Pattern Formation in Morphogenesis: Problems and Mathematical Issues. Springer Berlin Heidelberg, 2013.

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Wittmann, Christoph. Systems Metabolic Engineering. Springer Netherlands, 2012.

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service), SpringerLink (Online, ed. Encyclopedia of Genetics, Genomics, Proteomics and Informatics. Springer Science+Business Media, 2008.

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service), SpringerLink (Online, ed. Infectious Disease Informatics. Springer Science+Business Media, LLC, 2010.

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L, Osterman Andrei, and Gerdes Svetlana Y, eds. Microbial gene essentiality: Protocols and bioinformatics. Humana Press, 2008.

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Book chapters on the topic "Microbial bioinformatics"

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Giffard, Phil. "Bioinformatics of Microbial Sequences." In Infectious Disease Informatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1327-2_2.

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Rodríguez-García, Antonio, Alberto Sola-Landa, and Carlos Barreiro. "RNA-Seq-Based Comparative Transcriptomics: RNA Preparation and Bioinformatics." In Microbial Steroids. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7183-1_5.

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Pathak, Khyatiben V., and Sivaramaiah Nallapeta. "Plant-Microbial Interaction: A Dialogue Between Two Dynamic Bioentities." In Agricultural Bioinformatics. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1880-7_15.

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Tabish, Mohammad, Shafquat Azim, Mohammad Aamir Hussain, Sayeed Ur Rehman, Tarique Sarwar, and Hassan Mubarak Ishqi. "Bioinformatics Approaches in Studying Microbial Diversity." In Management of Microbial Resources in the Environment. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5931-2_6.

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Upadhyayula, Raghavender Surya, Pooran Singh Solanki, Prashanth Suravajhala, and Krishna Mohan Medicherla. "Bioinformatics Tools for Microbial Diversity Analysis." In Microbial Diversity in Ecosystem Sustainability and Biotechnological Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8315-1_2.

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Streets, Matthew, and Leanne Walker. "Microbial Reservoir Souring." In Microbial Bioinformatics in the Oil and Gas Industry. CRC Press, 2021. http://dx.doi.org/10.1201/9781003023395-10.

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Ulag, Songul, Elif Ilhan, Burak Aksu, et al. "Patch-Based Technology for Corneal Microbial Keratitis." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45385-5_18.

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Karaytuğ, Tuna, Nihan Arabacı İstifli, and Erman Salih İstifli. "Microbial and Bioinformatics Approach in Biofuel Production." In Clean Energy Production Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4611-6_9.

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Yadev, Brijesh Singh, Pallavi Chauhan, and Sandeep Kushwaha. "Bioinformatics Resources for Microbial Research in Biological Systems." In Microbial Genomics in Sustainable Agroecosystems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9860-6_3.

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Thatoi, H. N., and S. K. Pradhan. "Detoxification and Bioremediation of Hexavalent Chromium Using Microbes and Their Genes: An Insight into Genomic, Proteomic and Bioinformatics Studies." In Microbial Biotechnology. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6847-8_13.

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Conference papers on the topic "Microbial bioinformatics"

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White, James Robert, and Mihai Pop. "Microbial dynamics of human obesity." In 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop, BIBMW. IEEE, 2009. http://dx.doi.org/10.1109/bibmw.2009.5332071.

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Tavakoli, Sahar, and Shibu Yooseph. "Algorithms for inferring multiple microbial networks." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983194.

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Shen, Xianjun, Xue Gong, Xingpeng Jiang, Jincai Yang, Tingting He, and Xiaohua Hu. "High-order Organization of Weighted Microbial Interaction Network." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621218.

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Jian, Huang, Chang-li LIu, Shuai GAo, Wen-yang ZHou, and Jian-wu DOng. "Characteristics of Microbial Community in Degradation Agriculture Solidwastes." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5517802.

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Wei, Wei, Rongli Yu, Yashu Yuan, Gang Wang, and Rongli Yu. "Research on the Flocculation Mechanism of Microbial Flocculants." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516070.

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Imangaliyev, Sultan, Bart Keijser, Wim Crielaard, and Evgeni Tsivtsivadze. "Personalized microbial network inference via co-regularized spectral clustering." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999205.

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Cai, Qinhong, Po-Keung Wong, and Tsz Wai Ng. "Notice of Retraction: Microbial Degradation of Chromium Azo Dye." In 2011 5th International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2011. http://dx.doi.org/10.1109/icbbe.2011.5781039.

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Liu, Gao-Qiang, Du Li, Chao-Yang Zhu, Kuan Peng, and Huai-Yun Zhang. "Screening of oleaginous microorganisms for microbial lipids production and optimization." In 2010 International Conference on Bioinformatics and Biomedical Technology. IEEE, 2010. http://dx.doi.org/10.1109/icbbt.2010.5478990.

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McGovern, Jeffrey D., Eric Johnson, Alex Dekhtyar, Michael Black, Christopher Kitts, and Jennifer VanderKelen. "Library-Based Microbial Source Tracking via Strain Identification." In BCB '16: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2016. http://dx.doi.org/10.1145/2975167.2975205.

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Zhou, Guangyu, Jyun-Yu Jiang, Chelsea J. T. Ju, and Wei Wang. "Inferring Microbial Communities for City Scale Metagenomics Using Neural Networks." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621409.

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Reports on the topic "Microbial bioinformatics"

1

Beckstrom-Sternberg, Stephen. Bioinformatic Tools for Metagenomic Analysis of Pathogen Backgrounds and Human Microbial Communities. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada581677.

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