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Animal Metabolic Pathway Analysis

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Pathway analysis aims to link changes in metabolic compounds to biological pathways and is the most common and in-demand method of analysis in the metabolomics data analysis toolkit. Unlike metabolomics, which reports on the cellular molecular composition, metabolic flux analysis reveals pathway activity in vivo. BioVenic has been introducing specialized metabolic pathway analysis services to enhance our animal metabolomics solutions. Our R&D team designs specialized software to tackle the challenges of qualitative metabolic flux analysis. We transform your complex metabolomics datasets into visual representations of static or dynamic metabolic pathways. This enables you to easily interpret the results of metabolite detection in your samples.

Fig.1 Metabolomics and Lipidomics Metabolic Pathway Analysis. (Axton, et al., 2018)Fig.1 Metabolomics and Lipidomics Metabolic Pathway Analysis.1

Animal Metabolic Pathway Analysis Services

BioVenic offers customized metabolic pathway solutions for processing animal metabolomic data. We conduct analysis on critical metabolic pathways in animals, such as glucose metabolism, lipid metabolism, tricarboxylic acid cycle, pentose phosphate pathway, amino acid metabolism, and protein synthesis. Our analysis includes metabolic pathway regulation and expansion using professional metabolomics researchers and databases. We base our analysis on the known metabolic laws in animals. At the same time, we employ metabolic flux analysis of stable isotope compounds to validate pertinent metabolic pathways.

  • Metabolic Flux Analysis

BioVenic's Metabolic Flux Analysis service is suitable for customers who need to assess the rate of metabolic changes and identify the affected pathways. We utilize comprehensive analysis of metabolic flows to uncover the correlation between metabolites in pathways and efficiently acquire pertinent information about selected pathways and metabolic flows.

  • Qualitative Metabolic Pathway Analysis

BioVenic's qualitative metabolic pathway analysis technology process has been continuously optimized through years of project accumulation and can efficiently complete this extremely challenging task for researchers worldwide. By mining pathway-targeted metabolite data and introducing stable isotope labels, our state-of-the-art technology platform enables continuous tracking of metabolic flows and visualizes results through images.

  • Quantitative Metabolic Pathway Analysis

Our comprehensive R&D team, consisting of experts in biochemical software and metabolomics, develops quantitative models to achieve precise analysis of specific metabolic pathways.

Flow Chart of Animal Metabolomic Data Analysis Service

Fig.2 Flow Chart of Animal Metabolomic Data Analysis Service. (BioVenic Original)

Why Choose Us?

Customized services that simultaneously meet diversified research needs such as single metabolic pathway analysis and animal metabolic flow analysis.

A technical team composed of professionals in metabolomics, animal physiology, software development and other fields.

Optimized analysis parameters and strict quality control system layer by layer to ensure high-quality delivery results.

BioVenic offers customized solutions for analyzing metabolic pathways in animal metabolomics data analysis. Our animal metabolomics pathway analysis services include enrichment analysis, pathway analysis, and pathway topology analysis. These services aim to comprehensively analyze all biological information associated with the target metabolic pathway. BioVenic lets you enhance your understanding of animal physiological activities by simplifying metabolic pathway experiments. We also integrate this data with other omics data to provide more comprehensive data analysis results. Please feel free to contact us and inquire about pricing.

Reference

  1. Axton, Elizabeth Rose, et al. "Metabolomics-driven elucidation of cellular nitrate tolerance reveals ascorbic acid prevents nitroglycerin-induced inactivation of xanthine oxidase." Frontiers in pharmacology 9 (2018): 1085.
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