Publicação
Development of integrated models of hepatocyte cells
| Resumo: | Metabolism acts a machinery by maintaining the functionality of the cell in response to several perturbations, keeping a balance in the levels of crucial metabolites and cell components and producing energy by breaking down certain compounds. A better understanding of these mechanisms cannot be restricted to the knowledge of the function of specific tissues or cell types, it also requires knowledge about their interactions. The human liver has a high number of physiological functions related to the metabolism, such as the production of the bile, hormones and vitamins. The hepatocytes have a major impact in human metabolism, being the most metabolically active cell types in humans. Malfunction on the metabolism of this type of cells is related to several diseases, like hepatitis, cirrhosis or non-alcoholic fatty liver disease (NAFLD), where the last one is considered a manifestation of obesity. A particular pathway has been associated not only with obesity, but also with cancer and type 2 diabetes, the mechanistic TOR (mTOR) pathway. Signalling of this pathway has an effect on most of cellular functions and regulates growth and proliferation. It has been shown that alterations in this pathway can lead to fat accumulation in the liver of obese people. A better understanding of this complex pathway may help researchers to unveil more information on how this pathway works and how it can help in the treatment of several diseases. The increase of high-throughput data, due to the advances in sequencing and other experimental techniques, allowed us to better understand the molecular characteristics of the cell. A useful tool to process all this information are Genome-scale metabolic models (GSMMs). A GSMM is a list of mass-balanced reactions, which can be related to cellular compartments, like the cytoplasm. Given high-throughput data, GSMMs can be utilized for the simulation of the metabolism of a certain cell type through a constraintbased modelling framework. There are several algorithms/ tools to create tissue-specific metabolic models (based on a generic human model, such as Recon2) including tINIT, MBA or mCADRE. Although all these methods still face a number of issues, the generated models can simulate human tissues and can be a good starting point for a better understanding of complex diseases. An important limitation of these models is the fact that they only represent the metabolic layer of the cells, while for models to be able to support accurate simulations, a number of other important sub-systems (e.g. regulation, signalling) should also be taken into account. This models (Integrative models) combine the information and material flow of the three previous mentioned sub-systems, delivering a more robust tool with more predictive strength. |
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| Autores principais: | Ferreira, Jorge Miguel Lourenço |
| Assunto: | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Metabolism acts a machinery by maintaining the functionality of the cell in response to several perturbations, keeping a balance in the levels of crucial metabolites and cell components and producing energy by breaking down certain compounds. A better understanding of these mechanisms cannot be restricted to the knowledge of the function of specific tissues or cell types, it also requires knowledge about their interactions. The human liver has a high number of physiological functions related to the metabolism, such as the production of the bile, hormones and vitamins. The hepatocytes have a major impact in human metabolism, being the most metabolically active cell types in humans. Malfunction on the metabolism of this type of cells is related to several diseases, like hepatitis, cirrhosis or non-alcoholic fatty liver disease (NAFLD), where the last one is considered a manifestation of obesity. A particular pathway has been associated not only with obesity, but also with cancer and type 2 diabetes, the mechanistic TOR (mTOR) pathway. Signalling of this pathway has an effect on most of cellular functions and regulates growth and proliferation. It has been shown that alterations in this pathway can lead to fat accumulation in the liver of obese people. A better understanding of this complex pathway may help researchers to unveil more information on how this pathway works and how it can help in the treatment of several diseases. The increase of high-throughput data, due to the advances in sequencing and other experimental techniques, allowed us to better understand the molecular characteristics of the cell. A useful tool to process all this information are Genome-scale metabolic models (GSMMs). A GSMM is a list of mass-balanced reactions, which can be related to cellular compartments, like the cytoplasm. Given high-throughput data, GSMMs can be utilized for the simulation of the metabolism of a certain cell type through a constraintbased modelling framework. There are several algorithms/ tools to create tissue-specific metabolic models (based on a generic human model, such as Recon2) including tINIT, MBA or mCADRE. Although all these methods still face a number of issues, the generated models can simulate human tissues and can be a good starting point for a better understanding of complex diseases. An important limitation of these models is the fact that they only represent the metabolic layer of the cells, while for models to be able to support accurate simulations, a number of other important sub-systems (e.g. regulation, signalling) should also be taken into account. This models (Integrative models) combine the information and material flow of the three previous mentioned sub-systems, delivering a more robust tool with more predictive strength. |
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