Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Blow2020 - Aphid Buchnera Hamiltonella multi-compartment metabolic model

ABSTRACT: Aphid Buchnera Hamiltonella multi-compartment metabolic model, FBA

SUBMITTER: Nana Y D Ankrah

PROVIDER: MODEL2001310002 | BioModels | 2020-06-30

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MODEL2001310002?filename=AphBucHam.xml	Xml

Items per page:

1 - 1 of 1

Similar Datasets

Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.

2012-08-02 | E-GEOD-39785 | biostudies-arrayexpress

ZucAt: multi-compartment metabolic model of growing Arabidopsis thaliana

Project description:ZucAt: multi-compartment metabolic model of growing Arabidopsis thaliana

2016-06-04 | 186 | FAIRDOMHub

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

Project description:Our comprehensive model identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providin a framework to understand metabolic adaptation in eukaryal cells.

2022-01-24 | PXD030003 | Pride

Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

2012-08-03 | GSE39785 | GEO

Multi-species single-cell transcriptomic analysis of ocular compartment regulons

Project description:Multi-species single-cell transcriptomic analysis of ocular compartment regulons

| PRJNA622707 | ENA

Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets

Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets

2019-10-22 | MSV000084488 | MassIVE

Model-driven analysis of metabolic immunomodulators of macrophage activation

Project description:Model-driven multi-omic data analysis of the RAW 264.7 cell line elucidates metabolic immunomodulators of macrophage activation

2015-11-11 | PXD003055 | Pride

Dynamical network biomarker analysis using multi-organ RNA sequencing in metabolic syndrome model mice

Project description:Dynamical network biomarker (DNB) theory has emerged as a powerful framework for detecting early warning signals of pre-disease states. Based on our previous work demonstrating its utility in adipose tissue of metabolic syndrome model mice, we conducted a comprehensive DNB analysis using RNA sequencing data across 13 organs and 14 to 16 time points in high-fat diet-fed mice. Our findings revealed organ-specific variation in the timing of early warning signals, suggesting heterogeneous inter-organ dynamics during the pre-disease state of metabolic syndrome. These results highlight the potential of DNB theory for elucidating systemic early-stage pathophysiology in complex metabolic disorders.

2025-08-23 | GSE305719 | GEO

Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model.

Project description:The brain's myelin content can be mapped by T2-relaxometry, which resolves multiple differentially relaxing T2 pools from multi-echo MRI. Unfortunately, the conventional fitting procedure is a hard and numerically ill-posed problem. Consequently, the T2 distributions and myelin maps become very sensitive to noise and are frequently difficult to interpret diagnostically. Although regularization can improve stability, it is generally not adequate, particularly at relatively low signal to noise ratio (SNR) of around 100-200. The purpose of this study was to obtain a fitting algorithm which is able to overcome these difficulties and generate usable myelin maps from noisy acquisitions in a realistic scan time. To this end, we restrict the T2 distribution to only 3 distinct resolvable tissue compartments, modeled as Gaussians: myelin water, intra/extra-cellular water and a slow relaxing cerebrospinal fluid compartment. We also impose spatial smoothness expectation that volume fractions and T2 relaxation times of tissue compartments change smoothly within coherent brain regions. The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions. Due to efficient design, the additional spatial aspect does not cause an increase in processing time. The proposed method was applied to fast spiral acquisitions on which conventional fitting gives uninterpretable results. While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry.

| S-EPMC4045663 | biostudies-literature

Electro-metabolic coupling in multi-chambered vascularized human cardiac organoids

Project description:Electro-metabolic coupling in multi-chambered vascularized human cardiac organoids

| PRJNA983720 | ENA