Kailash Adhikari

Genome-scale metabolic model of Arabidopsis thaliana and Chlamydomonas reinhardtii.

Human life is dependent on different plant products and with growing population, demand for food and energy is increasing but resources to meet them remains limited on earth, so there is a need to find the ways to balance this chain. Green plants and algae can synthesize useful nutrients using carbon dioxide, water and sunlight form the environment by a process called photosynthesis and it is possible to engineer their metabolic process to increase the yield and also use them for production of biofuels.
This project is focused on use of computational modelling techniques to study and analyze the metabolic behavior of Arabidopsis thaliana - a multicellular flowering plant and Chlamydomonas reinhardtii - an unicellular green algae in response to light and other environmental conditions. In order to under-stand their metabolism and engineer them, genome-scale metabolic models (GSM) of both will be assessed and analyzed. A GSM represents the entire metabolic capabilities of an organism and is built from data extracted typically from annotated genome databases. Linear Programming (LP) will then be used to explore distribution of reaction rates i.e. the effect of each reactions over the metabolic network under variety of assumed environmental conditions. LP is a mathematical method to compute an optimal solution under given parameters such as maximizing biomass production in our case.
The experimental part of the project involves examination of biomass composition, signaling pathways etc and will be carried out in collaboration with other partners in the consortium. Both modelling and experimental results will be analyzed to identify potential cause of damage to the plants form environmental stress conditions and formulate strategies to mitigate such effects to obtained desired outputs form their metabolic activity. New hypotheses will be proposed based on these observations about operating characteristics of metabolic networks of Arabidopsis and Chlamydomonas and will be tested in collaboration with experimental and industrial partners.