In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways
Thesis (PhD. (Bioprocess Engineering))
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| Format: | Thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2024
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| Online Access: | http://openscience.utm.my/handle/123456789/1221 |
| _version_ | 1825623281596628992 |
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| author | Shia, Yoke Lin |
| author_facet | Shia, Yoke Lin |
| author_sort | Shia, Yoke Lin |
| collection | OpenScience |
| description | Thesis (PhD. (Bioprocess Engineering)) |
| first_indexed | 2024-09-23T23:50:36Z |
| format | Thesis |
| id | oai:openscience.utm.my:123456789/1221 |
| institution | Universiti Teknologi Malaysia - OpenScience |
| language | English |
| last_indexed | 2024-09-23T23:50:36Z |
| publishDate | 2024 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| spelling | oai:openscience.utm.my:123456789/12212024-06-30T08:44:31Z In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways Shia, Yoke Lin Type 2 diabetes Cellular signal transduction Insulin—chemistry Thesis (PhD. (Bioprocess Engineering)) It is estimated in 2015 that one in eleven adults are suffering from diabetes, this is equivalent to a global population of 415 million. About 90% of the diabetes patients involve type 2 diabetes which is associated to the cellular impaired intermediates within the insulin signaling pathway. Three key intermediates, protein kinase B (PKB) /Akt, protein kinase C-zeta (PKC-ζ), and glucose transporter type 4 (GLUT4) are identified in most of the studies. The current study used a novel approach that integrates the cellular insulin signaling pathway with a systemic glucose regulation model via Michaelis–Menten equation that was able to estimate the insulin-dependent glucose consumption based on the concentration of plasma glucose and translocation percentage of glucose transporter GLUT4. Based on this model, the impact of a single intermediate such as PKB/Akt, PKC-ζ, and GLUT4 was investigated using the software COPASI parameter scan function by multiplying the phosphorylation or activation kinetic of each intermediate with a defective coefficient, dc. The coefficient dc was divided equally into five intervals ranging from 0-1. The same procedure was repeated to measure the effect of the combined impairment of two and subsequently three intermediates. The results showed that the combination of three impaired intermediates best represented the glucose consumption rate mimicking that in diabetic patients. Through the integration of cellular insulin signalling pathway with systemic glucose regulation, the first and second objectives of the study were fulfilled as the major metabolic and signalling pathways link to type 2 diabetes were modeled and a better understanding of the interaction between metabolic and signalling pathways was developed. The integration also fulfilled the third objective of the study as the single and combinational effect of insulin intermediates over the insulin-dependent glucose consumption in normal subject was analyzed. In short, the current study has provided insight into understanding of the underlying impaired glucose uptake in diabetic patients based on the combined impairment of three key intermediates in insulin signalling pathway Faculty of Chemical Engineering 2024-06-22T10:35:24Z 2024-06-22T10:35:24Z 2016 Thesis Dataset http://openscience.utm.my/handle/123456789/1221 en application/pdf application/pdf application/pdf application/pdf application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Type 2 diabetes Cellular signal transduction Insulin—chemistry Shia, Yoke Lin In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| title | In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| title_full | In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| title_fullStr | In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| title_full_unstemmed | In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| title_short | In silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| title_sort | in silico identification of metabolites contributing to type 2 diabetes by integrating specific metabolic pathways |
| topic | Type 2 diabetes Cellular signal transduction Insulin—chemistry |
| url | http://openscience.utm.my/handle/123456789/1221 |
| work_keys_str_mv | AT shiayokelin insilicoidentificationofmetabolitescontributingtotype2diabetesbyintegratingspecificmetabolicpathways |