Computational Modeling of Planktonic and Biofilm Metabolism
| dc.contributor.author | Guo, Weihua | en |
| dc.contributor.committeechair | Feng, Xueyang | en |
| dc.contributor.committeemember | Senger, Ryan S. | en |
| dc.contributor.committeemember | Zhang, Chenming | en |
| dc.contributor.committeemember | He, Zhen | en |
| dc.contributor.department | Biological Systems Engineering | en |
| dc.date.accessioned | 2017-10-17T08:00:30Z | en |
| dc.date.available | 2017-10-17T08:00:30Z | en |
| dc.date.issued | 2017-10-16 | en |
| dc.description.abstract | Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states, which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems. To better harness microorganisms, plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells via multi-omics approaches (e.g., transcriptomics and proteomics analysis). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., metabolic fluxes) of microorganisms. Therefore, in this study, I have applied computational modeling approaches (i.e., 13C assisted pathway and flux analysis, flux balance analysis, and machine learning) to both planktonic and biofilm cells for better understanding intracellular metabolisms and providing valuable biological insights. First, I have summarized recent advances in synergizing 13C assisted pathway and flux analysis and metabolic engineering. Second, I have applied 13C assisted pathway and flux analysis to investigate the intracellular metabolisms of planktonic and biofilm cells. Various biological insights have been elucidated, including the metabolic responses under mixed stresses in the planktonic states, the metabolic rewiring in homogenous and heterologous chemical biosynthesis, key pathways of biofilm cells for electricity generation, and mechanisms behind the electricity generation. Third, I have developed a novel platform (i.e., omFBA) to integrate multi-omics data with flux balance analysis for accurate prediction of biological insights (e.g., key flux ratios) of both planktonic and biofilm cells. Fourth, I have designed a computational tool (i.e., CRISTINES) for the advanced genome editing tool (i.e., CRISPR-dCas9 system) to facilitate the sequence designs of guide RNA for programmable control of metabolic fluxes. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the scientists and engineers to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I will apply 13C assisted pathway analysis to investigate the metabolism of pathogenic biofilm cells for reducing their antibiotic resistance. | en |
| dc.description.abstractgeneral | Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states (i.e., floating in a flow and anchoring on a surface, respectively), which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems (e.g., chronic infections). Therefore, deciphering the metabolism of both planktonic and biofilm cells are of great importance to better harness microorganism. Plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells by measuring the abundances of single type of biological components (e.g., gene expression and proteins). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., enzyme activities) of microorganisms. Therefore, in this study, I have applied computational modeling approaches to both planktonic and biofilm cells for providing valuable biological insights (e.g., enzyme activities). The biological insights include 1) how planktonic cells response to mixed stresses (e.g., acids and organics) 2) how planktonic cells produce various chemicals, and 3) how biofilm cells generate electricity by rewiring the intracellular metabolic pathways. I also developed a novel platform to utilize multiple types of biological data for improving the prediction accuracy of biological insights of both planktonic and biofilm cells. In addition, I designed a computational tool to facilitate the sequence designs of an advanced genome editing tool for precisely controlling the corresponding enzyme activities. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the metabolic engineered to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I plan to investigate how the pathogenic biofilm cells improve their antibiotic resistance and attempt to reduce such strong resistance. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:13002 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/79669 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | 13C assisted pathway and flux analysis | en |
| dc.subject | planktonic and biofilm metabolism | en |
| dc.subject | flux balance analysis | en |
| dc.subject | multi-omics analysis | en |
| dc.subject | Machine learning | en |
| dc.subject | CRISPR-Cas9 | en |
| dc.subject | metabolic engineering | en |
| dc.subject | biofuels | en |
| dc.subject | cell-free protein synthesis | en |
| dc.title | Computational Modeling of Planktonic and Biofilm Metabolism | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Biological Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |