Microbial growth phenotype predication method based on control-metabolic network integration model

Abstract

The present invention discloses a microbial growth phenotype predication method based on a control-metabolic network integration model. The method comprises: firstly, constructing a gene control network, running multiple linear regression based on a large number of gene expression profile data so as to deduce a linear equation of expression variation of each target gene with a transcription factor, and calculating a fault discovery rate (FDR); and then taking a control relation (the FDR is less than or equal to 0.05) as a global control network, finding out transcription factors of control metabolic genes in the control network, calculating a reaction flow value F corresponding to a growth rate during a knockout period of the transcription factors according to the types of the transcription factors, performing same flow equilibrium analysis by an original metabolic network so as to obtain a maximal cell growth rate Fmax, and predicating growth phenotype changes of microorganisms during the knockout period of the transcription factors by calculating F / Fmax. The method provided by the present invention enables analysis precision to be improved, so that a growth phenotype of the microorganisms can be better predicted.

Description

technical field [0001] The invention belongs to the technical field of microorganisms, and in particular relates to a method for joint modeling of a gene regulation network and a metabolic network, which can be used to predict the growth phenotype of microorganisms. Background technique [0002] At present, metabolic network modeling is the main method to predict the effect of gene knockout on metabolic phenotype. The metabolic network includes the interactions between all enzymes, metabolites and biochemical reactions. Genome-wide metabolic network reconstruction and analysis can discover the effects of gene knockout, insertion, abnormal expression, and environmental changes on the phenotype of biological systems. [0003] Kinetic simulation is the most direct and effective means to analyze metabolic network, but the kinetic analysis of large-scale network is limited because many kinetic parameters are unknown. Constraint-based metabolic flux analysis, primitive model and ...

AI Technical Summary

Problems solved by technology

The gene regulatory network describes the interaction between regulatory factors and target genes. In recent years, many studies have constructed and analyzed the two networks separately, but there are few studies that integrate the two to reveal the impact of transcriptional regulation on metabolic phenotypes.

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