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Description
Methane (CH4) is very important in the environment as it is a greenhouse gas and important for the degradation of organic matter. During the last 200 years the atmospheric concentration of CH4 has tripled. Methanogens are methane-producing microbes from the Archaea domain that complete the final step in breaking down organic matter to generate methane through a process called methanogenesis. They contribute to about 74% of the CH4 present on the Earth's atmosphere, producing 1 billion tons of methane annually. The purpose of this work is to generate a preliminary metabolic reconstruction model of two methanogens: Methanoregula boonei 6A8 and Methanosphaerula palustris E1-9c. M. boonei and M. palustris are part of the Methanomicrobiales order and perform hydrogenotrophic methanogenesis, which means that they reduce CO2 to CH4 by using H2 as their major electron donor. Metabolic models are frameworks for understanding a cell as a system and they provide the means to assess the changes in gene regulation in response in various environmental and physiological constraints. The Pathway-Tools software v16 was used to generate these draft models. The models were manually curated using literature searches, the KEGG database and homology methods with the Methanosarcina acetivorans strain, the closest methanogen strain with a nearly complete metabolic reconstruction. These preliminary models attempt to complete the pathways required for amino acid biosynthesis, methanogenesis, and major cofactors related to methanogenesis. The M. boonei reconstruction currently includes 99 pathways and has 82% of its reactions completed, while the M. palustris reconstruction includes 102 pathways and has 89% of its reactions completed.
ContributorsMahendra, Divya (Author) / Cadillo-Quiroz, Hinsby (Thesis director) / Wang, Xuan (Committee member) / Stout, Valerie (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / School of Life Sciences (Contributor) / Biomedical Informatics Program (Contributor)
Created2014-05
Description
Glioblastoma (GBM) is an extremely malignant form of brain cancer characterized by rapid progression and poor patient survival. Even after standard of care treatments, less than ten percent of patients with this disease survive five years. More effective therapeutic options are urgently needed to improve outcomes for patients with GBM. Adequate drug delivery is a critical challenge in GBM treatment, as drugs delivered systemically must be able to penetrate the blood brain barrier (BBB) and reach the tumor at therapeutic levels. To address this, we developed a resource to catalog BBB penetration information for investigational agents currently in clinical trials in cancer. Using an in silico prediction model and manual annotation to capture existing knowledge from the published literature, BBB content for ~500 investigational drugs was added to the investigational database tool. In addition to BBB content, the database also includes information on the gene targets of the included therapies. The investigational database tool was used to identify investigational agents that (1) may have increased activity against GBM based on the presence of a specific mutation in the tumor sample and (2) have evidence suggesting the compounds may penetrate the BBB. By prioritizing investigational agents for further study based on evidence for BBB penetration, this resource can help the GBM research community pursue more effective treatments for GBM.
ContributorsHerring, Emily Lora (Author) / Coursen, Jerry (Thesis director) / Byron, Sara (Committee member) / Biomedical Informatics Program (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05