Within the life sciences switching mechanisms are pervasive at all levels, from molecules to cells and tissues. Their operation can be a key determinant of health or disease. Whilst the existence and importance of switches is widely acknowledged within the biological literature, many life scientists do not deal explicitly with the switching behaviour. Frequently, steady-state behaviour before and after switching is the primary focus. Here methods for analysis of switched systems from control engineering are applied to the modelling and analysis of a riboswitch. The model has been developed by studying the dynamics of the vitamin B12 riboswitch. The simulation results have been validated using in vivo experiments by checking the bacterial growth when using Escherichia coli and Salmonella enterica where the action of the vitamin B12 riboswitch is known to be a determinant of system behaviour. The paper first describes a simple model for the B12-riboswitch regulatory network in E. coli and applies the same analysis when changing the strain to S. enterica. Validation of the simulation results has been undertaken by linking the dynamics of the riboswitch to bacterial growth.
Despite internal complexity, algae and bacteria have coexisted since the early stages of evolution. This coevolution follows relatively simple laws that can be clearly expressed using mathematical models. This paper performs a quantitative analysis, motivated from the perspective of control theory, of a classical model from the literature. The model has been developed using data from an in vivo experimental two-species system where the bacterium Mesorhizobium loti supplies the vitamin B12 required for growth to the freshwater green alga Lobomonas rostrata and where the action of the B12 riboswitch is known to be a determinant of system behaviour. Analysis of the model both before and after the add-back of nutrients is carried out. A focus is exploring the robustness of the system. The paper first describes a simple model of algal-bacterial growth and analysis is undertaken. The effect of system parameters and control mechanisms is quantified. Motivated by the inherent switching action within the biology, a sliding mode interpretation of the control mechanisms is hypothesized based on knowledge of the maximum carrying capacities for each growth. The results of a range of experiments reported in the literature are used to validate the assertions.
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