| Title | A Model for Designing Adaptive Laboratory Evolution Experiments. |
| Year of Publication | 2017 |
| Authors | R.A. LaCroix; B.O. Palsson; A.M. Feist |
| Journal | PLoS Comput Biol |
| Abstract | The occurrence of mutations is a cornerstone of the evolutionary theory of adaptation, capitalizing on the rare chance that a mutation confers a fitness benefit. Natural selection is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite an increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining when to consider an experiment complete and for balancing outcomes with available resources (i.e., lab supplies, personnel, and time). To design and better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to optimize ALE experimentation. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim to explain differences in experimental outcomes. Further, a beneficial mutation rate of 10(-6.9)-10(-8.4) mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in serial passage batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results herein will aid in the design of ALE experiments to fit the exact needs of the project while taking into account the tradeoff in resources required, and lower the barrier of entry to this experimental technique. IMPORTANCE: Adaptive laboratory evolution (ALE) is a widely used scientific technique to increase scientific understanding, as well as create industrially relevant organisms. The manner in which ALE experiments are conducted is highly manual and uniform with little optimization for efficiency. Such inefficiencies result is a suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in a more optimized fashion and design experiments to generate greater fitness in a more accelerated time frame, thereby pushing the limits of what adaptive laboratory evolution can achieve. |
| PubMed ID | 28159796 |
