Genome Scale Metabolic Modelling of Arabidopsis thaliana and Chlamydomonas reinhardtii
type: CreativeWork
description: Recent advances in genome sequencing technology have enabled the elucidation of complete genome sequence for plants and algae. Genome scale metabolic models constructed from genome annotations represent the entire metabolic characteristics of the organism and can be used to integrate other data and study metabolic capabilities of the organisms under different conditions. This framework can help develop new insight about operating characteristic of the organism and propose new hypotheses that can be tested experimentally. In order to advance our understanding of photosynthetic metabolism in plants and algae, GSMs of A. thaliana and C. reinhardtii have been constructed using annotations from their respective BioCyc databases. They satisfy all theoretical considerations and are able to represent known biological behaviors and thus can be used in subsequent investigations. In collaboration with experimental partners, the arabidopsis model was used to study the knock-out phenotype of the Calvin Cycle enzymes. This correctly predicted the viability of single knockouts of 4 Calvin cycle enzymes. Alternate metabolic routes that make such change possible were identified using flux balance analysis. The analysis demonstrated a complementary role of SBPase and FBPase in the Calvin cycle and further proposed a novel role of transaldolase in daytime metabolism under knockout conditions. Both models, were used to investigate likely coordinated changes in metabolic networks to dissipate excess energy under high light conditions. Methods that use correlation coefficient and mixed integer linear programming have been developed for this purpose. Proteomics data obtained under high light conditions was integrated in the model to propose energy dissipating modes that are more likely to occur in vivo. Further, removal of reactions involved in energy dissipation mechanisms showed improve biomass yield.
abstract: Recent advances in genome sequencing technology have enabled the elucidation of complete genome sequence for plants and algae. Genome scale metabolic models constructed from genome annotations represent the entire metabolic characteristics of the organism and can be used to integrate other data and study metabolic capabilities of the organisms under different conditions. This framework can help develop new insight about operating characteristic of the organism and propose new hypotheses that can be tested experimentally. In order to advance our understanding of photosynthetic metabolism in plants and algae, GSMs of A. thaliana and C. reinhardtii have been constructed using annotations from their respective BioCyc databases. They satisfy all theoretical considerations and are able to represent known biological behaviors and thus can be used in subsequent investigations. In collaboration with experimental partners, the arabidopsis model was used to study the knock-out phenotype of the Calvin Cycle enzymes. This correctly predicted the viability of single knockouts of 4 Calvin cycle enzymes. Alternate metabolic routes that make such change possible were identified using flux balance analysis. The analysis demonstrated a complementary role of SBPase and FBPase in the Calvin cycle and further proposed a novel role of transaldolase in daytime metabolism under knockout conditions. Both models, were used to investigate likely coordinated changes in metabolic networks to dissipate excess energy under high light conditions. Methods that use correlation coefficient and mixed integer linear programming have been developed for this purpose. Proteomics data obtained under high light conditions was integrated in the model to propose energy dissipating modes that are more likely to occur in vivo. Further, removal of reactions involved in energy dissipation mechanisms showed improve biomass yield.
author: Adhikari, Kailash
language: en
date published: 2017
date modified: 2026-07-24T03:42:25Z
provider: Oxford Brookes University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Oxford Brookes University

PropertyValue
property ID: DOI
value: 10.24384/3jd0-x523