Advances in bioinformatics and computational biology have enabled integration of an enormous amount of known biological interactions. This has enabled researchers to use models and data to design experiments and guide new discovery as well as test for consistency. One such computational method is constraint-based metabolic flux modeling. This is performed using genome-scale metabolic models (GEMs) that are a collection of biochemical reactions, derived from a genome's annotation. This type of flux modeling enables prediction of net metabolite conversion rates (metabolic fluxes) to help understand metabolic activities under specific environmental conditions. It can also be used to derive metabolic engineering strategies that involve genetic manipulations. Over the past decade, GEMs have been constructed for several different microbes, plants, and animal species. Researchers have also developed advanced algorithms to use GEMs to predict genetic modifications for the overproduction of biofuel and valuable commodity chemicals. Many of the predictive algorithms for microbes were validated with experimental results and some have been applied industrially. However, there is much room for improvement. For example, many algorithms lack straight-forward predictions that truly help non-computationally oriented researchers understand the predicted necessary metabolic modifications. Other algorithms are limited to simple genetic manipulations due to computational demands. Utilization of GEMs and flux-based modeling to predict in vivo characteristics of multicellular organisms has also proven to be challenging. Many researchers have created unique frameworks to use plant GEMs to hypothesize complex cellular interactions, such as metabolic adjustments in rice under variable light intensity and in developing tomato fruit. However, few quantitative predictions have been validated experimentally in plants. This research demonstrates the utility of GEMs and flux-based modeling in both metabolic engineering and analysis by tackling the challenges addressed previously with alternative approaches. Here, a novel predictive algorithm, Node-Reward Optimization (NR-Opt) toolbox, was developed. It delivers concise and accurate metabolic engineering designs (i.e. genetic modifications) that can truly improve the efficiency of strain development. As a proof-of-concept, the algorithm was deployed on GEMs of E. coli and Arabidopsis thaliana, and the predicted metabolic engineering strategies were compared with results of well-accepted algorithms and validated with published experimental data. To demonstrate the utility of GEMs and flux-based modeling in analyzing plant metabolism, specifically its response to changes in the signaling pathway, a novel modeling framework and analytical pipeline were developed to simulate changes of growth and starch metabolism in Arabidopsis over multiple stages of development. This novel framework was validated through simulation of growth and starch metabolism of Arabidopsis plants overexpressing sucrose non-fermenting related kinase 1.1 (SnRK1.1). Previous studies suggest that SnRK1.1 may play a critical signaling role in plant development and starch level (a critical carbon source for plant night growth). It has been shown that overexpressing of SnRK1.1 in Arabidopsis can delay vegetative-to-reproductive transition. Many studies on plant development have correlated the delay in developmental transition to reduction in starch turnover at night. To determine whether starch played a role in the delayed developmental transition in SnRK1.1 overexpressor plants, starch turnover was simulated at multiple developmental stages. Simulations predicted no reduction in starch turnover prior to developmental transition. Predicted results were experimentally validated, and the predictions were in close agreement with experimental data. This result further supports previous data that SnRK1.1 may regulate developmental transition in Arabidopsis. This study further validates the utility of GEMs and flux-based modeling in guiding future metabolic research. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/85179 |
| Date | 05 April 2017 |
| Creators | Yen, Jiun Yang |
| Contributors | Biological Systems Engineering, Senger, Ryan S., Gillaspy, Glenda E., Zhang, Chenming, Bevan, David R. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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