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Modelling human immune response dynamics to Mycobacterium Tuberculosis infection.
Thesis (MSc)--Stellenbosch University, 2017.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2017
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| _version_ | 1867614045400465408 |
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| access_status_str | Open Access |
| author | Ramolotja, Kagiso Samuel |
| author2 | Mazandu, Gaston K. |
| author_browse | Mazandu, Gaston K. Ramolotja, Kagiso Samuel |
| author_facet | Mazandu, Gaston K. Ramolotja, Kagiso Samuel |
| author_sort | Ramolotja, Kagiso Samuel |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2017. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/102612 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:45:46.810Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/102612 Modelling human immune response dynamics to Mycobacterium Tuberculosis infection. Ramolotja, Kagiso Samuel Mazandu, Gaston K. Nyabadza, Farai Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Mycobacterium tuberculosis Immune response -- Mathematical models Macrophagus Cytokines T cells Non-linear least square Bayesian statististical decision theory Markov processes Monte Carlo method Thesis (MSc)--Stellenbosch University, 2017. ENGLISH SUMMARY: Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB) still poses a great challenge to the well-being of the population, particularly in Sub-Saharan Africa. The ability to trigger strong mounted cellular response within the host environment is daunting. The host cell depends on the robust adaptive cellular immunity to battle MTB infection. Of these, localizing TB bacteria by the development and the sustainability of a strong T helper type 0 cells (Th0) response is key to thwart MTB dissemination. The factors fuelling TB pathogenesis include, Human Immunodeficiency Virus (HIV) and TB copandemic, the growing burden of Multidrug-resistant TB (MDR-TB) and Extensive drug resistant TB (XDR-TB), and incompetency of host cell to kill MTB. The survived MTB load can trigger Latent TB infection (LTBI). In addition, the largely unknown biology of the host-MTB interaction, is yet to be elucidated. In this study, we develop a mathematical model capturing and addressing key dynamics of the adaptive host immune response to MTB infection. Of these, we consider the interplay of MTB with three distinct subsets of key immune cells and cell signalling molecules, namely; macrophages, T cells and cytokines. Furthermore, we explore the mechanisms of phagocytosis, T cells priming with delay, macrophage activation leading to lysis of infected cells. To estimate unknown parameters in the model, we perform curve fitting to experimental data of an infected mice using non-linear least square and Bayesian-based Markov Chain Monte Carlo methods. We use the reproductive number R0, to address factors altering the stability of the system. This study provides insight on how the immune system can be amplified to clear the survived intracellular MTB. AFRIKAANSE OPSOMMING: Tuberkulose (TB), wat veroorsaak word deur Mycobacterium tuberculosis (MTB), bied steeds ’n groot uitdaging tot die welvaart van die bevolking, spesifiek in Sub-Sahara Afrika. Die vermoë om ’n sterk gemonteerde sellulêre reaksie in die gasheeromgewing te wek is ontmoedigend. Die gasheersel is afhanklik van die robuuste aanpasbare sellulêre immuniteit om MTB infeksie teen te veg. Van hierdie, is die lokalisering van TB bakterië deur die ontwikkeling en volhoubaarheid van sterk T helper tipe 0 selle (Th0) reaksie die sleutel om MTB verspreiding te stuit. Die faktore wat TB patogene aanhelp sluit in ’n Menslike Immuniteitsgebreksvirus (MIV) en TB mede-pandemie, die groeiende las van multidwelmbestande TB (MDR-TB) en uitgebreide dwelmbestande TB (XDR-TB), en die onvermoë van die gasheersel om MTB dood te maak. Die oorlewende MTB lading kan lei tot ’n latente TB infeksie (LTBI). Daarbenewens moet die grootliks onbekende biologie van die gasheer-MTB interaksie nog toegelig word. In hierdie studie ontwikkel ons ’n wiskundige model wat die sleuteldinamika van die aanpasbare gasheerimmuunreaksie tot MTB infeskie opvang en aanspreek. Uit hierdie beskou ons die interspel van MTB met drie verskillende deelversamelings van sleutel immuunselle en selseiningsmolekules, naamlik: makrofage, T selle en sitokiene. Verder verken ons die meganismes van fagositose, T-sel primering met vertraging, makrofage aktivering wat lei tot lise in besmette selle. Om onbekende parameters in die model te benader, pas ons ’n kurwepassing toe op eksperimentele data van besmette muise en gebruik nie-lineêre kleinste kwadrate en Bayesies-gebaseerde Markov-ketting Monte Carlo metodes, onderskeidelik. Ons gebruik die voortplantingsgetal R0 om die faktore wat die stabiliteit van die stelsel verander, aan te spreek. Hierdie studie gee insig oor hoe die immuunstelsel versterk kan word om die oorblywende intrasellulêre MTB te verwyder. 2017-11-27T16:45:42Z 2017-12-11T10:31:50Z 2018-05-28T03:00:04Z 2017-12 Thesis http://hdl.handle.net/10019.1/102612 en_ZA Stellenbosch University xi, 88 pages application/pdf application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Mycobacterium tuberculosis Immune response -- Mathematical models Macrophagus Cytokines T cells Non-linear least square Bayesian statististical decision theory Markov processes Monte Carlo method Ramolotja, Kagiso Samuel Modelling human immune response dynamics to Mycobacterium Tuberculosis infection. |
| title | Modelling human immune response
dynamics to Mycobacterium Tuberculosis infection. |
| title_full | Modelling human immune response
dynamics to Mycobacterium Tuberculosis infection. |
| title_fullStr | Modelling human immune response
dynamics to Mycobacterium Tuberculosis infection. |
| title_full_unstemmed | Modelling human immune response
dynamics to Mycobacterium Tuberculosis infection. |
| title_short | Modelling human immune response
dynamics to Mycobacterium Tuberculosis infection. |
| title_sort | modelling human immune response dynamics to mycobacterium tuberculosis infection |
| topic | Mycobacterium tuberculosis Immune response -- Mathematical models Macrophagus Cytokines T cells Non-linear least square Bayesian statististical decision theory Markov processes Monte Carlo method |
| url | http://hdl.handle.net/10019.1/102612 |
| work_keys_str_mv | AT ramolotjakagisosamuel modellinghumanimmuneresponsedynamicstomycobacteriumtuberculosisinfection |