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Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
The study of reaction-rate in a large metabolic network is called metabolomics. Multiple techniques and their combinations have been used to model the network of reaction-rates: 1) metabolic flux analysis; 2) system of partial differential equations; 3) time-course measurements and clustering; 4) correlation between reactions creating a correlation-network and 5) Gaussian Graph modeling (GGM) based upon multivariate Gaussian distribution.
Functional Omics and Big Data Analysis in Microalgae
Published in Gokare A. Ravishankar, Ranga Rao Ambati, Handbook of Algal Technologies and Phytochemicals, 2019
Chetan Paliwal, Tonmoy Ghosh, Asha A. Nesamma, Pavan P. Jutur
The metabolism of a living organism is the complete set of chemical reactions required for life; numerous enzymes efficiently play the role of catalysts in these reactions. There are usually two main points to be considered while studying these reactions: first by kinetics, i.e., unknown for most of the reactions and other is through determination of stoichiometry (Baart and Martens 2012). Genome-scale metabolic models (GSMMs) can be constructed and modeled once, after gathering enough of the annotated algal genome or transcriptome data available and topology of the metabolic network is analyzed. Initial draft models are generated directly from the available genome annotation data and finalized simultaneously by adding various experimental datasets, literature review and gap-filling steps; the final model evolves with the inclusion of all the reactions alga performs and various associated genes and constraints (Reijnders et al. 2014).
Composition and Diversity of Human Oral Microbiome
Published in Chaminda Jayampath Seneviratne, Microbial Biofilms, 2017
Preethi Balan, Chaminda Jayampath Seneviratne and Wim Crielaard
As the oral cavity is at the junction of entrance into the enteric and respiratory systems, the acquisition of oral microbiome is unique. The oral cavity is in constant contact with the environmental microbes via food intake or mouth breathing, which contribute to the diverse oral microbial community. The oral microbiome is one of the most diverse and complex microbiomes in the human body [15,16] (Figure 4.1). The oral microbial community is formed mainly by a diverse range of bacteria and much less characterised members which include fungi, viruses and archaea. The microbiome can survive in a planktonic state as in saliva or remain adhered to hard and soft tissues forming biofilms. The two types of microbiome that exist across body habitats are variable microbiome and core microbiome [17]. The variable microbiome is subject specific, and is acquired in response to lifestyle, genotypic and phenotypic factors. The core microbiome is the predominant overlapping microbiome profile shared among different healthy individuals [10,18]. The identification of key members of variable and core microbiome will allow us to understand the metabolic network existing among host and microbial interactions [19]. Although individuals share microbiota at similar sites of the body, there are varying differences at the species and strain levels of the microbiome. This can be as inimitable to the individual as is the fingerprint [20]. The core microbiome of the oral cavity is summarised in Figure 4.2.
Psyllium seed husk regulates the gut microbiota and improves mucosal barrier injury in the colon to attenuate renal injury in 5/6 nephrectomy rats
Published in Renal Failure, 2023
Dongmei Hu, Wenbo Liu, Wanlin Yu, Lihua Huang, Chunlan Ji, Xusheng Liu, Zhaoyu Lu
All statistical analyses were performed using R software (version 3.5.0; R Foundation for Statistical Computing, Vienna, Austria). α-diversity (Shannon and Chao indices) determination and partial least squares discrimination analysis (PLS-DA) were performed at the genus level. 16SrRNA gene sequences were analyzed using the vegan package, which was also used to perform permutation multivariate ANOVA of dissimilarity matrices to assess the effect of 10,000 permutations. A linear discriminant analysis of effect size (LEfSe) was used to analyze group characteristics and the effect sizes of significant differences between groups. EnvFit analysis was used to determine the effect sizes and significance of the covariates in each group of 999 permuted plasmids. A redundancy analysis (RDA) was performed using the vegan package. Correlations between genera and covariates, adjusted for groups, were identified by general linear modeling. The false discovery rate was used to assess the significance of differences, with q < 0.1. Based on the MetaCyc database, pathoLogic (part of the Pathway Tools software) was used to predict the metabolic network. MetaFlux (Pathway Tools) was used to convert those tools into a metabolic model, which was then used to compare the metabolic pathways of different groups.
From taxonomy to metabolic output: what factors define gut microbiome health?
Published in Gut Microbes, 2021
Tomasz Wilmanski, Noa Rappaport, Christian Diener, Sean M. Gibbons, Nathan D. Price
While metabolomics provides a window into the functional output of the gut microbiome, it is limited in its measurement of important metabolites that are rapidly transformed or excreted from the system. Locally produced and consumed metabolites that impact host physiology cannot be captured by measuring standing concentrations of blood and fecal metabolites alone (e.g. SCFAs are rapidly consumed/transformed by both host and microbial cells within the gut). To this end, other analytical methods may be particularly useful. Genome-scale metabolic modeling is an established method successfully implemented to address biological, microbiological, and bioengineering questions.141,142 It relies on detailed stoichiometric reconstructions of metabolic networks and calculates the flux of metabolites through those networks given a set of environmental constraints, providing insight into metabolic production, consumption and overall growth.143
Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation
Published in Expert Review of Proteomics, 2020
Leonardo Perez De Souza, Saleh Alseekh, Yariv Brotman, Alisdair R Fernie
It is important to highlight here some fundamental limitations of pathway analysis arising both from technical and conceptual challenges faced when investigating metabolism. We will follow this discussion with some interesting strategies that try to circumvent these challenges. A first clear drawback arises from our incomplete coverage of the metabolome and the interactions between metabolites represented in pathway databases and metabolic models [93]. As it has already been discussed here, the vast majority of signals detected by current metabolomics platforms remain unidentified. Moreover, it is evident that there is still a large gap of knowledge regarding all the intricate connections within the metabolic networks of any organism [8]. As a consequence, much of the information contained in metabolomics datasets is simply not integrated by such approaches relying on predefined pathways. Another very important limitation arises from the definition of metabolic pathways itself. Metabolic pathways represent sub-networks from the complete metabolic network. Despite efforts in trying to formalize the definitions of metabolic pathways [94], these are somehow subjective and can still significantly vary across multiple databases [3,95]. Additionally, several metabolites are present in multiple pathways complicating even further the interpretation of results based on pre-defined pathways.