Metabolic Network Refinement
Metabolic Network Refinement - Related Videos
11:07
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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Cited by 1 •
2021
This protocol demonstrates the use of a phenotype microarray (PM) technology platform to define metabolic requirements of Chlamydomonas reinhardtii, a green microalga, and refine an existing metabolic network model.
05:41
Metabolic Profile Analysis of Zebrafish Embryos
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Cited by 42 •
2013
Zebrafish represent a powerful vertebrate model that has been under-utilised for metabolic studies. Here we describe a rapid way to measure the in vivo metabolic profile of developing zebrafish that allows the comparison of different mitochondrial function parameters between genetically or pharmacologically manipulated embryos, thereby increasing the applicability of this organism.
10:56
Construction of Out-of-Equilibrium Metabolic Networks in Nano- and Micrometer-Sized Vesicles
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Cited by 2 •
2024
We present a protocol for reconstituting membrane proteins and encapsulating enzymes and other water-soluble components in lipid vesicles of sub-micrometer and micrometer size.
10:19
An Introduction to Cell Metabolism
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2023
In cells, critical molecules are either built by joining together individual units like amino acids or nucleotides, or broken down into smaller components. Respectively, the reactions responsible for this are referred to as anabolic and catabolic. These reactions require or produce energy typically in the form of a “high-energy” molecule called ATP. Together, these processes make up “Cell Metabolism,” and are hallmarks of healthy, living cells.JoVE’s introduction to cell metabolism briefly...
08:28
Metabolic Labeling
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2023
Metabolic labeling is used to probe the biochemical transformations and modifications that occur in a cell. This is accomplished by using chemical analogs that mimic the structure of natural biomolecules. Cells utilize analogs in their endogenous biochemical processes, producing compounds that are labeled. The label allows for the incorporation of detection and affinity tags, which can then be used to elucidate metabolic pathways using other biochemical analytical techniques, such as SDS-PAGE...
FAQs
Metabolic network refinement draws on three complementary evidence streams: genome annotations suggest which biochemical reactions may be encoded, biochemical evidence evaluates whether those reactions are plausible, and experimental measurements test how metabolism behaves in cells. Comparing these sources helps distinguish absent reactions from unsupported ones and improves the biological credibility of pathway connections used in computational analysis.
Removing unsupported pathways prevents a model from treating uncertain biology as established metabolism. Adding missing reactions, by contrast, can restore routes needed to represent observed cellular behavior. Together, these changes reduce structural errors in the network and make predictions about growth, metabolite production, or responses to genetic and environmental changes more consistent with the evidence used to build the model.
Flux constraints limit the allowable rates through reactions in a network. In a refined model, they help computational analyses evaluate biologically plausible patterns rather than every mathematically possible route. This matters for flux balance analysis, which uses the network to predict growth, metabolite production, and responses to genetic or environmental changes.
Compared with an unrefined representation, a refined network contains decisions about both inclusion and constraint: reactions lacking support can be removed, missing reactions can be added, and fluxes can be restricted. The result is not simply a larger pathway list; it is a representation more closely aligned with available biological evidence.
A practical refinement workflow begins by examining genome annotations, then checking proposed reactions against biochemical evidence and experimental measurements. Researchers can identify missing reactions, remove pathways without support, and impose flux constraints based on the assembled evidence. The revised network can then be used in flux balance analysis or related computational methods to assess predicted cellular phenotypes.
Model predictions can connect network structure with phenotypes that are otherwise difficult to interpret directly. Depending on the refined reactions and flux constraints, analyses may estimate whether an organism can grow, produce particular metabolites, or respond to a genetic or environmental change. These outputs provide computational views of cellular metabolism rather than direct measurements of the cell itself.
In biology, refined networks support two complementary goals. They can help interpret metabolic phenotypes by linking observed outcomes to reaction and pathway representations, and they can guide searches for potential drug targets. In biotechnology, the same models can inform strain design by predicting how changes may affect growth or metabolite production, helping focus experimental investigation.