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Studying Evolution by Laboratory Experimentation and Digital Organism Simulations


Nothing in Biology or Medicine Makes Sense
Except in the Light of Evolution
(Paraphrasing from Theodosius Dobzhansky and Ajit Vark)


Escherichia coli lives in the human intestine and is constantly washed in low doses of food additives and medications. The effects of food additives on E. coli have been less studied compared to antibiotics. We observed the adaptation of E. coli cultured in different concentration of food additives, singly or in combination, over 140 passages. Adaptability over time was estimated by generation time and cell density at stationary phase. Our results demonstrated that E. coli had adapted to every treatment. In the first phase of adaptation, genomic analysis by PCR/RFLP suggests that the stress response in E. coli may be similar, suggesting a global stress response [1]. However, in prolonged stress adaptations, our results showed a divergence in genetic distance [2]. This suggests ecological specialization over prolonged stress exposure. To further this work, we examined the extent of adaptability of E. coli, a non-halophilic microbe, by adapting E. coli to increasing salt media. Our results suggested that E. coli can adapt from 1% NaCl to 11% NaCl incrementally [3]. Our MIC results suggested that E. coli can grow at 13% NaCl media (comparatively, average seawater is 3.5% NaCl) at the end of the experiment (cultured at 7% NaCl media). Hence, we conclude that E. coli can be adapted to grow in 11% NaCl by incremental adaptation, which can have significant implications to food curing and processing industries.


Antibiotics resistance is a serious biomedical issue. There have been contradictory results regarding the prevalence of resistance following withdrawal and disuse of the specific antibiotics. However, experimental studies into antibiotics resistance is ethnically impossible. We use simulated evolution in “digital organisms” to examine the rate of gain and loss of resistance with [5] and without [4] fitness cost for maintaining resistance. In both cases, our results showed that selective pressure is likely to result in maximum resistance with respect to the selective pressure. During de-selection from the disuse of specific antibiotics, a large initial loss is observed but resistance is not lost to the stage of pre-selection. This suggests that a pool of partial resistant organisms persist after withdrawal of selective. Hence, contradictory results by previous observational studies may be a statistical variation about constant proportion. Moreover, our results also showed 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 has been established. On a larger scale, this work demonstrates that the use of digital organism simulation to examine ethically difficult areas.



  1. Lee, et al. 2012. ISRN Microbiology 2012, Article ID 965356.
  2. Loo, et al. 2014. Asia Pacific Journal of Life Sciences 7(3): 243-258.
  3. Goh, et al. 2012. Electronic Physician 4(3): 527-535.
  4. Castillo and Ling. 2014. BioMed Research International 2014, Article ID 648389.
  5. Castillo, et al. 2015. MOJ Proteomics & Bioinformatics 2(2): 00039.





March 20


11:00 am - 12:00 pm


Auditorium 5, Perdana University
Block B, MAEPS Building, MARDI Complex, Jalan MAEPS Perdana
Serdang, Selangor 43400 Malaysia


+603-8941 8646

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