Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, United States of America.
Department of Statistics, University of California, Berkeley, United States of America.
Theor Popul Biol. 2024 Oct;159:1-12. doi: 10.1016/j.tpb.2024.07.002. Epub 2024 Jul 15.
Multi-type birth-death processes underlie approaches for inferring evolutionary dynamics from phylogenetic trees across biological scales, ranging from deep-time species macroevolution to rapid viral evolution and somatic cellular proliferation. A limitation of current phylogenetic birth-death models is that they require restrictive linearity assumptions that yield tractable message-passing likelihoods, but that also preclude interactions between individuals. Many fundamental evolutionary processes - such as environmental carrying capacity or frequency-dependent selection - entail interactions, and may strongly influence the dynamics in some systems. Here, we introduce a multi-type birth-death process in mean-field interaction with an ensemble of replicas of the focal process. We prove that, under quite general conditions, the ensemble's stochastically evolving interaction field converges to a deterministic trajectory in the limit of an infinite ensemble. In this limit, the replicas effectively decouple, and self-consistent interactions appear as nonlinearities in the infinitesimal generator of the focal process. We investigate a special case that is rich enough to model both carrying capacity and frequency-dependent selection while yielding tractable message-passing likelihoods in the context of a phylogenetic birth-death model.
多类型 Birth-Death 过程是从跨生物尺度的系统发育树中推断进化动态的方法的基础,这些尺度范围从远古物种的宏观进化到快速的病毒进化和体细胞增殖。当前系统发育 Birth-Death 模型的一个局限性是,它们需要严格的线性假设,这些假设产生了可处理的消息传递似然性,但也排除了个体之间的相互作用。许多基本的进化过程 - 如环境承载能力或频率依赖选择 - 需要相互作用,并且可能在某些系统中强烈影响动态。在这里,我们在均值场相互作用中引入了一个多类型 Birth-Death 过程,并引入了焦点过程的副本集合。我们证明,在相当一般的条件下,在无限集合的极限下,集合的随机演化相互作用场收敛到一个确定的轨迹。在这个极限下,副本有效地解耦,并且自洽的相互作用在焦点过程的无穷小生成器中表现为非线性。我们研究了一个足够丰富的特殊情况,该情况既能模拟承载能力又能模拟频率依赖选择,同时在系统发育 Birth-Death 模型的背景下产生可处理的消息传递似然性。