Soft and Living Matter Laboratory, CNR-NANOTEC, 00185 Rome, Italy.
Italian Institute for Genomic Medicine, 10126 Turin, Italy.
Phys Rev E. 2019 Jan;99(1-1):012417. doi: 10.1103/PhysRevE.99.012417.
We study a minimal model for the growth of a phenotypically heterogeneous population of cells subject to a fluctuating environment in which they can replicate (by exploiting available resources) and modify their phenotype within a given landscape (thereby exploring novel configurations). The model displays an exploration-exploitation trade-off whose specifics depend on the statistics of the environment. Most notably, the phenotypic distribution corresponding to maximum population fitness (i.e., growth rate) requires a nonzero exploration rate when the magnitude of environmental fluctuations changes randomly over time, while a purely exploitative strategy turns out to be optimal in two-state environments, independently of the statistics of switching times. We obtain analytical insight into the limiting cases of very fast and very slow exploration rates by directly linking population growth to the features of the environment.
我们研究了一个最小模型，用于描述在一个随时间变化的环境中，表现出表型异质性的细胞群体的生长情况。在这个环境中，细胞可以复制（利用可用资源）并在给定的景观中改变其表型（从而探索新的构型）。该模型显示了一种探索-开发的权衡，其具体细节取决于环境的统计特性。最值得注意的是，当环境波动的幅度随时间随机变化时，对应于最大种群适应性（即增长率）的表型分布需要一个非零的探索率，而在两态环境中，纯粹的开发策略无论转换时间的统计特性如何，都是最优的。我们通过将种群增长与环境特征直接联系起来，获得了对非常快速和非常缓慢探索率的限制情况的分析见解。
