Examensarbeten för masterexamen // Master Theses
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Browsar Examensarbeten för masterexamen // Master Theses efter Program "Complex adaptive systems (MPCAS), MSc"
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- PostCommunity Systems Modeling, from complexity to optimality(2014) Ghaffari Nouran, Pouyan; Chalmers tekniska högskola / Institutionen för kemi- och bioteknik; Chalmers University of Technology / Department of Chemical and Biological Engineering
- PostCommunity Systems Modeling: From Complexity to Optimality(2013) Ghaffari Nouran, Pouyan; Chalmers tekniska högskola / Institutionen för kemi- och bioteknik; Chalmers University of Technology / Department of Chemical and Biological EngineeringBiological systems, whether they are community of species in an ecosystem, circuits of neurons in the brain or molecular mechanisms inside a cell, are inherently dynamic and complex. This complexity is enclosed not only in number and characteristics of constituent components of the system, but rather in the non-uniform and intricate way of connections between the components to form an emergent phenotype. Human microbiota is not an exception and embodies a typical example of complex biological system. The structure, assembly and dynamics of microbiota along with its contribution to the diet, physiology and development of the host are affected by the diversity of species and genome. In addition, interactions between the various species have an important role in the metabolism of ecosystems. Recently genome-scale metabolic models (GEMs) have been used to model the between three predominant species in human gut microbiota and have been explored the interactions between microbes in simplified community. Despite recent progresses, we still have very limited knowledge about the contribution of individual microorganisms within the communities and the interactions between them. This calls for the development of system-level methods and modeling frameworks to discover details of the complex microbial communities. Here, we introduce a multidimensional distributed model to study and analyze the microbiota as whole, encompassing species with interlaced metabolic interactions in different levels. The model is developed as a comprehensive, integrated and predictive framework and can be used for different complex ecosystems with any number of interacting species. We applied our method to analyze six representative species from abundant phyla in human gut microbiota. The outputs of specialized simulations validated based on SCAFs secretion profile and species abundances for lean and obese subjects.
- PostPredicting metabolic strategies in Saccharomyces cerevisiae with a kinetically constrained FBA model(2014) Nilsson, Avlant; Chalmers tekniska högskola / Institutionen för kemi- och bioteknik; Chalmers University of Technology / Department of Chemical and Biological EngineeringMetabolism is central to all life. It provides the energy and the building blocks from which the cells are constructed and maintained. Synthetic biologists often make genetic alterations to the enzymes involved in metabolism to improve product yields. Drastic changes in metabolism are linked to several diseases, e.g. cancer. It is therefore desirable to understand and quantitatively predict cell metabolism. Flux balance analysis (FBA) is a successful mathematical approach for predicting the metabolic activity of a cell. It makes use of the stoichiometry of the biochemical reactions and the rates of nutrient uptake. These relations are used to generate self consistent sets of metabolic fluxes, i.e. rates of metabolic conversion over the reactions. Amongst these it is common to select the set that has the highest growth rate as the predicted set. This has been shown to agree well with experimental data. One problem with the standard FBA approach is that it does not constrain the flux levels. In the living cell fluxes are constrained by the fact that they are performed by a finite amount of enzymes. The enzyme levels are limited by the amount of energy available for enzyme production and a limited space for enzymes to occupy. It has been shown that taking such limits in to account can improve the prediction powers of FBA. In this master thesis a modified version of FBA has been developed that uses the fluxes and enzyme kinetic parameters to estimates the weight of the participating enzymes. The total protein weight is constrained to experimentally observed levels. This allows prediction of the maximum growth rate for different substrates and shifts in metabolic strategy to fermento respiration at high growth rates. This might become of use to metabolic engineers in predicting if a potential pathway might decrease cell fitness.