Selection of Microbial Mutants Tolerant To Extreme Environmental Stress Using Continuous Culture-Control Design

Abstract

The design of controllers for a continuous selection technique (BOICS; Brown and Oliver, 1982) is considered. This technique is used to obtain microbial mutants that are tolerant to extreme environmental stress. Applications of BOICS have been hampered by the problem of controller design. In this paper, a modified implementation of BOICS is considered which has a number of practical advantages. A model-based approach to controller design is taken. The case in which the stress is due to an inhibitory substance in the growth environment is considered. The analysis is intended to be applicable to any reasonable combination of organism and inhibitor. Conventional linear and time-invariant controllers are considered. Guidelines for the selection of controller parameters' values are suggested. The application of these guidelines requires that certain process parameters' values be identified. Methods by which these parameters' values can be identified are suggested. Simulation results indicate that the resulting controllers perform satisfactorily. This is confirmed by experimental data from a model selection experiment. A recipe for the design of controllers is a necessary part of a protocol for BOICS. It is hoped that the solution to the controller design problem that is offered in this paper will encourage further applications for the technique.

Relation between lipophilicity of alkyl gallates and antifungal activity against yeasts and filamentous fungi

“…5.1. Using a model-based approach, guidelines for appropriate BOICS controller design were recently presented that will likely pave the way to a broader application of this very useful selection technique [83]. Comparing the outcome of selection for inhibitor-tolerant mutants in chemostat, turbidostat, and BOICS, it was argued that only the latter se-lects specifically for variants that are tolerant to extreme concentrations of the inhibitor [84].…”

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

“…Although the molecular mechanisms underlying the rapid evolution of drug resistance are increasingly understood, it remains difficult to link this molecular and genetic information with multi-species population dynamics at different scales [16][17][18][19]. In addition, while the majority of studies focus on bacteria populations in well-mixed environments [20][21][22], natural communities evolve on spatially extended habitats that display multiple biotic and abiotic gradients [23,24] and potentially yield complicated networks of interacting subpopulations [25,26].…”

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

“…Although the molecular mechanisms underlying the rapid evolution of drug resistance are increasingly understood, it remains difficult to link this molecular and genetic information with multi-species population dynamics at different scales (MacLean et al, 2010; Holmes et al, 2016; Singer et al, 2007; Denk-Lobnig and Wood, 2023). In addition, while the majority of studies focus on bacteria populations in well-mixed environments (Lane et al, 1999; Kawecki et al, 2012; Hughes and Andersson, 2017), natural communities evolve on spatially extended habitats that display multiple biotic and abiotic gradients (Donaldson et al, 2016; Chikina and Vignjevic, 2021) and potentially yield complicated networks of interacting subpopulations (Hanski, 1998; Nicoletti et al, 2023).…”