| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435706 |
| 005 | | 20200228103639 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085645584 |
| 035 | |
▼a (MiAaPQ)AAI27529089 |
| 035 | |
▼a (MiAaPQ)NCState_Univ18402036759 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 610 |
| 100 | 1 |
▼a Matthews, Megan Leigh. |
| 245 | 10 |
▼a Multi-Scale Modeling of Lignin Biosynthesis and Other Wood Properties in Populus trichocarpa. |
| 260 | |
▼a [S.l.]:
▼b North Carolina State University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 159 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: B. |
| 500 | |
▼a Advisor: Williams, Cranos |
| 502 | 1 |
▼a Thesis (Ph.D.)--North Carolina State University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Understanding the mechanisms behind lignin formation has been an important area of research for more than five decades, and has significant current implications in the bioenergy and biomaterial industries. Computational models have been shown to be an indispensable tool for understanding this complex process. Computational models of the monolignol pathway in Populus trichocarpa and other plants have previously been developed and used to explore metabolic regulation and how transgenic modifications impact lignin content and structure. However, it is still unclear how these modifications propagate through the different biological layers resulting in changes to lignin and other wood properties. In this dissertation, I present the development of a multi-scale model linking transgenic modifications of the monolignol transcript abundances to changes in the associated protein abundances, metabolite concentrations, metabolic fluxes, and 25 lignin and associated wood traits in the model tree Populus trichocarpa. The multi-scale model described in this work was constructed in three main steps, which quantify the relationship between monolignol transcript and protein abundances, establish how the protein abundances impact the metabolite concentrations and metabolic fluxes in the monolignol biosynthetic pathway, and quantify the effects of the outputs of the biosynthetic pathway on lignin and the associated wood properties. In the first version of this model, we used simple linear regressions to predict each protein abundance from its associated transcript abundance. These protein abundances were input into a kinetic-based model that was previously developed using mass action kinetics to describe monolignol pathway behavior in P. trichocarpa. Multiple regressions were created to relate the metabolite and flux outputs from this metabolic model to 25 lignin and wood properties. We aimed to further improve on this model by examining the manifestation of indirect influences on the transgenic transcript and protein abundances when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood, identifying putative indirect regulatory influences to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on desired single or combinatorial knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we show that our model more accurately estimates transcript and protein abundances from xylem tissue when individual and families of monolignol genes were perturbed. We developed version 2 of the multi-scale model by incorporating this new transcript-protein model and replacing the multiple regressions, which related the flux and metabolite outputs of the metabolic model to the phenotypic traits, with random forest models that use only the steady-state flux outputs as predictors. We demonstrate that for 23 of the 25 lignin and wood properties the multi-scale model incorporating cross-influences between the monolignol gene transcripts and proteins has a smaller predictive error when emulating the transgenic experiments than a model that does not capture these influences. This multi-scale model provides a useful computational tool for exploring the cascaded impact of novel combinatorial monolignol gene perturbations to identify strategies for engineering trees to have specific prototypes for use in the bioenergy and biomaterial industries. It can also be used to guide future experiments to elucidate the mechanisms responsible for the indirect influences observed between the monolignol gene transcripts and proteins. |
| 590 | |
▼a School code: 0155. |
| 650 | 4 |
▼a Energy. |
| 650 | 4 |
▼a Wood sciences. |
| 650 | 4 |
▼a Computational chemistry. |
| 650 | 4 |
▼a Bioengineering. |
| 690 | |
▼a 0202 |
| 690 | |
▼a 0746 |
| 690 | |
▼a 0219 |
| 690 | |
▼a 0791 |
| 710 | 20 |
▼a North Carolina State University. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-03B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0155 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494131
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |