scholarly journals Contribution of genome scale metabolic modeling to niche theory

2021 ◽  
Author(s):  
Antoine Regimbeau ◽  
Marko Budinich ◽  
Abdelhalim Larhlimi ◽  
Juan Jose Pierella Karlusich ◽  
Olivier Aumont ◽  
...  
Keyword(s):  
Probabilistic Inference ◽  
Metabolic Modeling ◽  
Niche Modeling ◽  
Functional Genes ◽  
Niche Theory ◽  
Ecological Studies ◽  
Occurrence Data ◽  
Genome Scale ◽  
The Way

Standard niche modeling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome-scale descriptions of these organisms. This study overcomes this shortcoming by proposing a new conceptual niche that encompasses the whole metabolic capabilities of an organism. The so-called metabolic niche resumes well-known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasizing that the presence-absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organism's niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better the genome-scale description in ecological studies.

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

scholarly journals Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Pouyan Ghaffari ◽  
Adil Mardinoglu ◽  
Anna Asplund ◽  
Saeed Shoaie ◽  
Caroline Kampf ◽  
...  
Keyword(s):  
Growth Factors ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
Metabolic Modeling ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Genome Scale

scholarly journals Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

2020 ◽  
Vol 8 (11) ◽  
pp. 1793
Author(s):  
Jinxin Zhao ◽  
Yan Zhu ◽  
Jiru Han ◽  
Yu-Wei Lin ◽  
Michael Aichem ◽  
...  
Keyword(s):  
Acinetobacter Baumannii ◽  
Bloodstream Infection ◽  
Post Infection ◽  
Metabolic Modeling ◽  
Nutrient Utilization ◽  
Multidrug Resistant ◽  
Host Environment ◽  
Gene Essentiality ◽  
Genome Scale

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.

scholarly journals Basics of genome-scale metabolic modeling and applications on C1-utilization

2018 ◽  
Vol 365 (20) ◽  
Author(s):  
Ilyas Kabimoldayev ◽  
Anh Duc Nguyen ◽  
Laurence Yang ◽  
Sunghoon Park ◽  
Eun Yeol Lee ◽  
...  
Keyword(s):  
Metabolic Modeling ◽  
Genome Scale

Towards the necessity of a new interactive approach integrating ethnology, ecology and ethology in the study of the relationship between Kyrgyz stockbreeders and wolves

2006 ◽  
Vol 45 (3) ◽  
pp. 463-478 ◽  
Author(s):  
Nicolas Lescureux
Keyword(s):  
Human Activities ◽  
Human Populations ◽  
Ecological Studies ◽  
Relational System ◽  
Interactive Approach ◽  
New Approach ◽  
Reciprocal Influences ◽  
The Relationship ◽  
Scientific Questions ◽  
The Way

The cohabitation between men and wolves arouses passions but also scientific questions. Recent ecological studies show that human activities have an unquestionable influence on wolves’ behavior. In the same way, if one refers to various ethnological works, it is undeniable that human populations are sensitive to this neighbor whose presence is marked both materially and symbolically. However, in spite of the apparent reciprocity of the relationship between these two species, they were studied up to now only in a unilateral way by ecology, ethology and ethnology. Now, the analysis of data resulting from my fieldwork in Kyrgyzstan shows a more complex reality to the relationship, which compels us to reconsider the way of treating it. The cohabitation between wolves and men, as experienced by Kyrgyz for centuries, is indeed assimilated to a real inter-relationship made up of reciprocal influences. Kyrgyz and wolves seem thus to be involved in an interactive and dynamic relational system. The latter imposes for its study a new approach, one that is more global and dialectical, concerned with the interspecific character of the relationship. However, such an approach inevitably raises methodological if not epistemological problems this article wishes to highlight.

scholarly journals Insights on the virulence of swine respiratory tract mycoplasmas through genome-scale metabolic modeling

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Mariana G. Ferrarini ◽  
Franciele M. Siqueira ◽  
Scheila G. Mucha ◽  
Tony L. Palama ◽  
Élodie Jobard ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Metabolic Modeling ◽  
Genome Scale
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