Genome Scale Metabolic Modelling of Arabidopsis thaliana and Chlamydomonas reinhardtii

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.

Grantor dc:publisher

Oxford Brookes University

Year dc:date

2017

Author dc:creator

• Adhikari, Kailash

Contributors dc:contributor

• Poolman, Mark
• Fell, David

Statement dc:rights

• All rights reserved

Language dc:language

en

DOI dc:identifier

https://doi.org/10.24384/3jd0-x523

OAI identifier oai:identifier

tle:dad76857-8db4-44ce-96e3-771defa4a90c:d6bd9758-527a-46cd-bfe2-c433766e8fca:1