PDF(Pubmed)
Abstract：
Serial passaging is a fundamental technique in experimental evolution. The choice of bottleneck severity and frequency poses a dilemma: longer growth periods allow beneficial mutants to arise and grow over more generations, but simultaneously necessitate more severe bottlenecks with a higher risk of those same mutations being lost. Short growth periods require less severe bottlenecks, but come at the cost of less time between transfers for beneficial mutations to establish. The standard laboratory protocol of 24-h growth cycles with severe bottlenecking has logistical advantages for the experimenter but limited theoretical justification. Here we demonstrate that contrary to standard practice, the rate of adaptive evolution is maximized when bottlenecks are frequent and small, indeed infinitesimally so in the limit of continuous culture. This result derives from revising key assumptions underpinning previous theoretical work, notably changing the metric of optimization from adaptation per serial transfer to per experiment runtime. We also show that adding resource constraints and clonal interference to the model leaves the qualitative results unchanged. Implementing these findings will require liquid-handling robots to perform frequent bottlenecks, or chemostats for continuous culture. Further innovation in and adoption of these technologies has the potential to accelerate the rate of discovery in experimental evolution.
摘要：
连续传代是实验进化中的一项基本技术。瓶颈严重程度和频率的选择带来了一个难题：更长的生长期允许有益的突变体在更多代中出现和生长，但同时需要更严重的瓶颈，这些相同的突变丢失的风险更高。短的成长期需要不太严重的瓶颈，但代价是转移之间建立有益突变的时间更短。具有严重瓶颈的24小时生长周期的标准实验室方案对实验者具有后勤优势，但理论上的合理性有限。在这里，我们证明了与标准做法相反，当瓶颈频繁和较小时，自适应进化的速率被最大化，在连续文化的极限中，确实是无限的。这一结果源于对支撑先前理论工作的关键假设的修正，特别是将优化指标从每次串行传输的自适应更改为每次实验运行时。我们还表明，在模型中添加资源约束和克隆干扰会使定性结果保持不变。实施这些发现将需要液体处理机器人来执行频繁的瓶颈，或用于连续培养的化学计量器。这些技术的进一步创新和采用有可能加速实验进化中的发现速度。
