Design and optimization of microbial communities

Microbial communities directly affect surrounding environments and are an important biological process, with potential applications in a variety of fields, such as biotechnology, environmental, and human health. However, the overall understanding of interactions and dynamics in microbial communities remains a challenge. Synergies between computational methods and genome-scale metabolic models have been explored in the last years, as a way to unravel community interactions and behavior, as demonstrated by the numerous simulation methods developed for application in the context of microbial communities. The available simulation methods, with application to microbial communities, were here evaluated and revealed good predictions for phenotypic behavior. However, few studies are available in terms of optimization tools in the community context. Hence, this work describes the implementation of algorithms for the optimization of minimal medium composition, as well as genes/reactions for the production of target compounds. These tools were implemented in MEWpy to transform it into an integrative Python workbench for metabolic engineering to explore constraint-based models of microbial communities. Five hydrothermal samples from the São Miguel Island, Azores, were analyzed to determine prokaryotic community composition to further reconstruct individual and community genome-scale metabolic models, and through simulation and design methods try to unveil possible routes to produce compounds with industrial interest. The first manually curated genome-scale metabolic model for the thermophilic bacterium Sulfurihydrogenibium azorense Az-Fu1 was developed, uncovering the details of its metabolic capabilities and suggesting for the first time that S. azorense Az-Fu1 may have metabolic potential for bacterial cellulose production. Moreover, the microbial communities of the different samples were modeled, and co-culture optimization was performed using the implemented methods. Among other results, it was shown that S. azorense Az-Fu1 can enhance its cellulose production capabilities when fed with acetate produced by another organism.

Name thesis:degree_name

Doutoramento em Bioengenharia

Grantor

Universidade do Minho

Year dc:date.issued

2024

Author dc:creator

• Santos, Sophia Torres

Advisors dc:contributor.advisor

• Rocha, I.
• Dias, Oscar

• Design
• Extremophile environments
• Genome-scale metabolic modeling
• Microbial communities
• Optimization
• Ambientes extremófilos
• Comunidades microbianas
• Modelação metabólica à escala genómica
• Otimização

Statement dc:rights

• openAccess

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1822/91621

OAI record oai:identifier

http://repositorium.sdum.uminho.pt/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Arepositorium.uminho.pt%3A1822%2F91621