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To reconstruct organism-specific metabolic networks, available information on annotated genomes and established biochemical knowledge can be used.
On the other, biochemical knowledge can be biased to certain pathways and processes that have been more intensely studied than others.
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This knowledge can be used to correlate cytological and biochemical parameters among which c-mitosis and changes in sulfhydryl group metabolism after chemical treatment are the most obvious ones.
The general knowledge can be challenged by this particular knowledge.
Knowledge can be distinguished into tacit knowledge and explicit knowledge.
BUT indigenous knowledge can be faulty.
Knowledge can be lost without forgetting anything.
Knowledge can be strict or defeasible.
In contrast to most pathways, which seek to provide detailed representations of biochemical knowledge, models can be more abstract representations of the reality, depending on the needs of the modeler, the experimental data available and the investigation being undertaken.
While an approach that allows for the automated generation of reconstructions directly from the genome will clearly grow in importance given the ever-increasing volume of sequencing data, it is also clear that existing, curated data resources such as MetaCyc and KEGG still provide a great deal of biochemical knowledge that can be exploited in the metabolic reconstruction process.
iAB-RBC-283 is a proteomic based metabolic reconstruction and a biochemical knowledge-base, a functional integration of high-throughput biological data and existing experimentally verified biochemical erythrocyte knowledge that can be queried through simulations and calculations.
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