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Ines Thiele

Researcher at National University of Ireland, Galway

Publications -  159
Citations -  23484

Ines Thiele is an academic researcher from National University of Ireland, Galway. The author has contributed to research in topics: Microbiome & Metabolic network. The author has an hindex of 55, co-authored 146 publications receiving 19437 citations. Previous affiliations of Ines Thiele include University of California, San Diego & National University of Ireland.

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Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

TL;DR: This study analyzed transcriptomes from 2114 post-mortem brain samples and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway, and identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD.
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A Systems Biology Approach to the Evolution of Codon Use Pattern

TL;DR: It is proposed, supported by computations and bibliomic data, that expansion of tRNA gene content or tRNA reading is a mechanism to respond to changes in CUB, and suggests that in order to maximize growth and to adapt to new environmental niches, tRNA content must co-evolve.
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Conditions for duality between fluxes and concentrations in biochemical networks

TL;DR: In this article, the authors present a stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations in biochemical networks, and demonstrate that flux-concentration duality is a pervasive property of biochemical networks.
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MetaboAnnotator: an efficient toolbox to annotate metabolites in genome-scale metabolic reconstructions

TL;DR: A COBRA toolbox extension is developed, deemed MetaboAnnotator, which facilitates the comprehensive annotation of metabolites with database independent and dependent identifiers, obtains molecular structure files, and calculates metabolite formula and charge at pH 7.2.
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From metagenomic data to personalized computational microbiotas: Predicting dietary supplements for Crohn's disease

TL;DR: Personalized dietary treatments that could improve each patient's SCFA levels are suggested and the underlying modeling approach could aid clinical practice to find novel dietary treatment and guide recovery by rationally proposing food aliments.