Systems metabolic engineering of Arabidopsis for increased cellulose production

Computational biology enabled us to manage vast amount of experimental data and make inferences on observations that we had not made. Among the many methods, predicting metabolic functions with genome-scale models had shown promising results in the recent years. Using sophisticated algorithms, such as flux balance analysis, OptKnock, and OptForce, we can predict flux distributions and design metabolic engineering strategies at a greater efficiency. The caveat of these current methods is the accuracy of the predictions. We proposed using flux balance analysis with flux ratios as a possible solution to improving the accuracy of the conventional methods. To examine the accuracy of our approach, we implemented flux balance analyses with flux ratios in five publicly available genome-scale models of five different organisms, including Arabidopsis thaliana, yeast, cyanobacteria, Escherichia coli, and Clostridium acetobutylicum, using published metabolic engineering strategies for improving product yields in these organisms. We examined the limitations of the published strategies, searched for possible improvements, and evaluated the impact of these strategies on growth and product yields. The flux balance analysis with flux ratio method requires a prior knowledge on the critical regions of the metabolic network where altering flux ratios can have significant impact on flux redistribution. Thus, we further developed the reverse flux balance analysis with flux ratio algorithm as a possible solution to automatically identify these critical regions and suggest metabolic engineering strategies. We examined the accuracy of this algorithm using an Arabidopsis genome-scale model and found consistency in the prediction with our experimental data.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Biological Systems Engineering

Department dc:contributor.department

Biological Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2014

Author dc:creator

• Yen, Jiun Yang

Chair dc:contributor.committeechair

• Senger, Ryan S.

Committee members dc:contributor.committeemember

• Gillaspy, Glenda E.
• Zhang, Chenming

• cellulose
• Arabidopsis thaliana
• genome-scale model
• flux ratio
• flux balance analysis
• mitochondrial malate dehydrogenase
• biomass

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:2112

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/54589