Ferreira Amaro Freire Tomas, Madec Sten, Gjini Erida
Center for Computational and Stochastic Mathematics, Instituto Superior Tecnico, Lisbon, Portugal.
Institut Denis Poisson, University of Tours, Tours, France.
Bull Math Biol. 2025 Jul 26;87(9):120. doi: 10.1007/s11538-025-01491-5.
Ecosystems are constantly exposed to newcoming strains or species. Which newcomer will be able to invade a resident multi-species community depends on the invader's relative fitness. Classical fitness differences between two growing strains are measured using the exponential model. Here we complement this approach, developing a more explicit framework to quantify fitness differences between two co-invading strains, based on the replicator equation. By assuming that the resident species' frequencies remain constant during the initial phase of invasion, we are able to determine the invasion fitness differential between the two strains, which drives growth rate differences post-invasion. We then apply our approach to a critical current global problem: invasion of the gut microbiota by antibiotic-resistant strains of the pathobiont Escherichia coli, using previously-published data. Our results underscore the context-dependent nature of fitness and demonstrate how species frequencies in a host environment can explicitly modulate the selection coefficient between two strains. This mechanistic framework can be augmented with machine-learning algorithms and multi-objective optimization to predict relative fitness in new environments, to steer selection, and design strategies to lower resistance levels in microbiomes.
生态系统不断面临新出现的菌株或物种。哪种新来者能够入侵一个常住的多物种群落取决于入侵者的相对适合度。使用指数模型来衡量两种生长菌株之间的经典适合度差异。在这里，我们补充了这种方法，基于复制者方程开发了一个更明确的框架，以量化两种共同入侵菌株之间的适合度差异。通过假设在入侵的初始阶段常住物种的频率保持不变，我们能够确定两种菌株之间的入侵适合度差异，这驱动了入侵后生长速率的差异。然后，我们将我们的方法应用于当前一个关键的全球问题：利用先前发表的数据，致病性大肠杆菌的抗生素抗性菌株对肠道微生物群的入侵。我们的结果强调了适合度的背景依赖性，并展示了宿主环境中的物种频率如何明确调节两种菌株之间的选择系数。这个机制框架可以通过机器学习算法和多目标优化进行扩充，以预测新环境中的相对适合度，指导选择，并设计降低微生物群落中抗性水平的策略。
