Mathematical Modelling of Microbial Population Dynamics

  • Abdulah A. Alghamdi

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Bacteria are among the most common living organisms; some are beneficial to humans, while others are harmful. Serratia marcescens, an opportunistic Gram-negative bacteria, causes many deadly diseases by attacking the human body and infecting the respiratory system, urinary tract, or skin. It uses protein secretion, which is the controlled transport of selected proteins across the cell envelop to the bacterial cell’s exterior. Protein secretion is carried out by six types of antibacterial nano-weapons known as type I to type VI (T1SS to T6SS) secretion systems.

This project cover new area that has not been study before, which is building mathematical models to describe the phases of building the nano-weapon T6SS with respect to the time t and focusses on investigating a particular bacteria that have this special nano-weapon and use it to gain competitive advantage. This will allow analysis of different strategies, parameters values and rules for interaction will affect the final outcome of the populations.

Using this as a foundation, we develop our mathematical model to investigate several attacking strategies including random fire, cell-cell contact, and mixed strategy. Then, we compare them in order to determine which one is more effective from the prospective of killing a peaceful population.

Total attrition plays an important role in studying bacterial attacking strategies in a complete sense. This can be covered by immunity and recognition of the attacking species. We further expand our mathematical models to include recognition and immunity for attacking population in four cases: (i) with full recognition and total immunity, (ii) with full recognition and zero immunity, (iii) with zero recognition and total immunity, and (iv) with zero recognition and zero immunity. Then, we compared all of the mathematical models presented in this thesis to distinguish which one is the most killing effective.
Date of Award2022
Original languageEnglish
SponsorsKing Abdulaziz University & Saudi Arabian Cultural Bureau
SupervisorFordyce Davidson (Supervisor) & Philip Murray (Supervisor)

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