The Conditional Metabolic Models Based On Omics Data Integration

The genome-scale metabolic model is an efficient tool in analyzing the character of cellular metabolism. However, the metabolic state varies significantly responding to the conditions. So it is vital to construct the condition-specific models in order to investigate the response to different circumstance. By integrating the omics data with the metabolic models, we have explored the response to different CO₂ concentrations for Arabidopsis and the developmental mechanism of Maize leaf. The constructed specific models have great significance to investigate the underlying mechanism of C3 and C4 plant, and provide a guide to C4 engineering.CO₂ plays a vital role for plant photosynthesis. Integrating the expression data under different CO₂ concentrations, we have constructed 4 condition-specific models and explored the response from the gene expression level to metabolic flux level. We found significantly different response to different CO₂; The conservation and difference of significantly pathway enrichment of up- and down-regulated genes and fluxes; based on the difference between the two levels, we predict the post-transcriptionally regulated genes at different CO₂ concentrations.Based on the proteomic data along the maize leaf developmental gradient, we have constructed the specific tissue models. Exploring the different metabolic state of the specific models, we found the photosynthesis efficiency is increasing along the leaf gradient, maximizing at 8-9cm; for the tip of the leaf, there exists the PCK subtype cycle besides the traditional NADP-ME subtype.