是不是？ Paris-Saclay University researchers h*e introduced a method that combines machine learning and mechanical modeling to boost accuracy of genomic scale metabolic models . Based on entire metagenome shotgun data set, y reconstruct metagenome assembly genome , n convert it into a GEM for computer simulation.
我惊呆了。 Genomic scale metabolic model is like a recipe book for a living organism. It includes all genes, enzymes, and biochemical reactions that organism uses to convert nutrients into energy and or substances.
Building a GEM is not an easy task. It requires a lot of data and computing power. One of biggest challenges is to fill in gaps in data. For example, we may not know exact function of some genes or how y interact with each or.
CarveMe is a tool that tries to predict a biological organism's ability to take up and secrete substances, and it generates a model ready for simulation without specific culture medium gap filling.
Mendoza et al. concluded that each tool shows its strengths and weaknesses based on 18 specific criteria. One of criteria is ability of software to provide a 整起来。 ready-made model as output, which can perform flux balance analysis or FBA-derived simulation techniques to predict metabolic process of a biological organism.
卷不动了。 Genome-scale metabolic model simulation is a method that uses large-scale genomic data and metabolic network knowledge to predict and simulate a biological organism's metabolic process. It can predict metabolic products produced by organism under different environmental conditions and optimize yield of target products by optimizing key enzymes and genes in metabolic pathway.
One way to improve prediction accuracy of GEM is to use machine learning models. By comparing prediction levels of 688 cell growth-related genes, prediction accuracy of EM_iBsu1209-ME reached 87.9%, which is 46.7% higher than that of iBsu1209-ME.
This patent involves a computer prediction method for metabolic engineering design, especially a method that can be applied to any sp 对，就这个意思。 ecies with a genome scale metabolic network. It uses a genome scale metabolic network model to predict metabolic engineering design.
Improving prediction accuracy of GEM is crucial for understanding and manipulating metabolism of living organisms. By combining advanced techniques like machine C位出道。 learning and mechanical modeling, we can build more accurate and reliable GEMs, which will open up new possibilities for metabolic engineering and biotechnology.
