Combined in silico Modelling and Multi-Omics Analysis of Rice Metabolism during Different Abiotic Conditions

Rice plants are exposed to a number of environmental changes that adversely affect their growth and productivity by causing morphological, physiological, biochemical, and molecular changes and ultimately yield.  Therefore, a comprehensive understanding of the mechanism that operates under such abiotic conditions highlights the need for further research. In this study, we have combined in silico metabolic model of rice and ~omics data to understand, i) the mechanism of rice germination under anoxia,  ii) photorespiratory mechanism that increases under drought stress and iii) effects of different light treatment as well as dark  on various morphological and regulatory mechanism of rice plants.  Flux Balance Analysis was used to perform the simulation for all abiotic conditions. By combining in silico central metabolic model of rice with gene expression data, we identified transcriptionally regulated reactions and potential transcription factors such as MYB, bZIP, ERF and ZnF that could be involved in the regulation of metabolism under anoxia. We simulated the photorespiratory pathway in rice leaves and identified essential and lethal genes of the pathway through single gene deletion and paired synthetic lethality screening. We elucidated the light-specific rice metabolism at various levels by integrating in silico genome scale metabolic model of rice with multi-omics data. Our analysis identified potential transcription factors such as bZIP (HY5, CPRF5/6/7, GBF1/2), R2R3 MYBs and ZnF specific to blue light and involvement of several phytohormones as key biomarkers of light-mediated regulation. In summary, our approach can potentially guide for developing new targets to engineer crop productivity strategies.