Martis Stephen, Schwab David J, GrandPre Trevor
Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065.
The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065.
Proc Natl Acad Sci U S A. 2025 May 27;122(21):e2425607122. doi: 10.1073/pnas.2425607122. Epub 2025 May 21.
In large, natural ecosystems, many (1) phenotypically relevant mutants can emerge over the characteristic turnover time of the population. When this is the case, there can be 'eco-evolutionary feedback' between the dynamical processes that underlie mutation, selection and ecology. We show that, owing to such feedback, the precise details of the mutational process can have a qualitative impact on the long-term behavior of an eco-evolutionary system, in contrast to the classical population genetic assumption that all mutations can be modeled with an effective, homogeneous rate. We demonstrate this in the context of a version of MacArthur's consumer-resource model in which consumers mutate along a resource preference trait-space. Starting from a stochastic individual-based model, we simulate the system in the case where mutations are exogenously generated at a fixed rate (e.g. via external mutagens) and in the case where mutations are coupled to replication (e.g. via DNA copying errors). We find that, surprisingly, replication-coupled mutations are capable of generating a patterned phase in the limit of fast ecological relaxation - precisely the regime where classical population genetic models are expected to operate. We derive a mean-field description of the stochastic model and show that the patterned phase comes about due to a Turing-like mechanism driven by the non-reciprocal and nonlinear nature of replicative mutations. Furthermore, we show that additional interactions like those due to host defense mechanisms can extend the patterned regime to arbitrarily high dimensional phenotype spaces. We demonstrate that these results are robust to demographic noise and model choices and we discuss systems in which this phenomenology might be relevant.
在大型自然生态系统中,在种群的特征周转时间内,许多(1)与表型相关的突变体可能会出现。当出现这种情况时,在突变、选择和生态背后的动态过程之间可能会存在“生态进化反馈”。我们表明,由于这种反馈,突变过程的精确细节可能会对生态进化系统的长期行为产生定性影响,这与经典种群遗传学假设所有突变都可以用有效、均匀的速率建模形成对比。我们在麦克阿瑟消费者 - 资源模型的一个版本中证明了这一点,在该模型中消费者沿着资源偏好性状空间发生突变。从基于个体的随机模型开始,我们模拟了在以固定速率(例如通过外部诱变剂)外生产生突变的情况下以及在突变与复制耦合(例如通过DNA复制错误)的情况下的系统。我们发现,令人惊讶的是,复制耦合突变在快速生态弛豫的极限情况下能够产生一个有模式的阶段——正是经典种群遗传模型预期运行的状态。我们推导了随机模型的平均场描述,并表明有模式的阶段是由复制突变的非互易和非线性性质驱动的类似图灵机制产生的。此外,我们表明像宿主防御机制引起的那些额外相互作用可以将有模式的区域扩展到任意高维的表型空间。我们证明这些结果对人口统计噪声和模型选择具有鲁棒性,并且我们讨论了这种现象学可能相关的系统。