Using Artificial Life Simulation to Gain Insights into Contradictory Field Evidence - PyConSG 2016
- Using Artificial Life Simulation to Gain Insights into Contradictory Field Evidence - PyConSG 2016
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Speaker: Maurice Ling
Antibiotics resistance is a serious biomedical issue and there are contradictory results on the prevalence of resistance following disuse of the specific antibiotics. However, it is not ethically possible to carry out such field experiments. Hence, digital organisms are used as proxy to gain insights into contradictory field evidence in attempt to provide crucial information into this debate.
Antibiotics resistance is a serious biomedical issue as formally susceptible organisms gain resistance under its selective pressure. There have been contradictory results regarding the prevalence of resistance following withdrawal and disuse of the specific antibiotics.
In the first experiment, we use experimental evolution in “digital organisms” to examine the rate of gain and loss of resistance under the assumption that there is no fitness cost for maintaining resistance. Our results show that selective pressure is likely to result in maximum resistance with respect to the selective pressure. During de-selection as a result of disuse of the specific antibiotics, a large initial loss and prolonged stabilization of resistance are observed but resistance is not lost to the stage of pre-selection. This suggests that a pool of partial resistant organisms persist long after withdrawal of selective pressure at a relatively constant proportion.
In the second experiment, fitness costs incurred in maintaining resistance, in the form of deviation from GC-content, is added. However, our results showed that GC-content based fitness cost during de-selection by removal of antibiotic-induced selective pressure portrayed similar trends in resistance compared to that of no fitness cost, at all stages of initial selection, repeated de-selection and re-introduction of selective pressure. Paired t-test suggested that prolonged stabilization of resistance after initial loss is not statistically significant for its difference to that of no fitness cost.
Hence, contradictory results regarding the prevalence of resistance following withdrawal and disuse of the specific antibiotics may be a statistical variation about constant proportion. Our results also show that subsequent re-introduction of the same selective pressure results in rapid re-gain of maximal resistance. Thus, our simulation results suggest that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics, once a resistant pool of micro-organism has been established, which has important implications towards responsible use of antibiotics.
This work demonstrates that the use of digital organisms has the potential to provide insights into otherwise ethically and practically inaccessible areas.
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