Author : Andrea M. Rocha
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (778 download)
Book Synopsis Computational Discovery of Phenotype Related Biochemical Processes for Engineering by : Andrea M. Rocha
Download or read book Computational Discovery of Phenotype Related Biochemical Processes for Engineering written by Andrea M. Rocha and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Application of bioengineering technologies for enhanced biological hydrogen production is a promising approach that may play a vital role in sustainable energy. Due to the ability of several naturally occurring microorganisms to generate hydrogen through varying metabolic processes, biological hydrogen has become an attractive alternative energy and fuel source. One area of particular interest is the production of biological hydrogen in organically-rich engineered systems, such as those associated with waste treatment. Despite the potential for high energy yields, hydrogen yields generated by bacteria in waste systems are often limited due to a focus on microbial utilization of organic material towards cellular growth rather than production of biogas. To address this concern and to improve upon current technological applications, metabolic engineering approaches may be applied to known hydrogen producing organisms. However, to successfully modify metabolic pathways, full understanding of metabolic networks involved in expression of microbial traits in hydrogen producing organisms is necessary. Because microbial communities associated with hydrogen production are capable of exhibiting a number of phenotypes, attempts to apply metabolic engineering concepts have been restricted due to limited information regarding complex metabolic processes and regulatory networks involved in expression of microbial traits associated with biohydrogen production. To bridge this gap, this dissertation focuses on identification of phenotype-related biochemical processes within sets of phenotype-expressing organisms. Specifically, through co-development and application of evolutionary genome-scale phenotype-centric comparative network analysis tools, metabolic and cellular components related to three phenotypes (i.e., dark fermentative, hydrogen production and acid tolerance) were identified.