{"id":139,"title":"PINN-based functional inference in size-structured population models: Application to fish oogenesis","title_zh":"基于物理信息神经网络的大小结构种群模型功能推断：在鱼类卵子发生中的应用","url":"https://pubmed.ncbi.nlm.nih.gov/42993775","doi":"10.1371/journal.pcbi.1014933","pmid":"42993775","journal":"PLoS Comput Biol","kind":"paper","lang":"en","published_at":"2026-11-06T00:00:00+00:00","fetched_at":"2026-11-08T03:43:57+00:00","authors":["Fostier L","Lesage M","Thermes V","Clément F","Yvinec R"],"primary_topic":"oocyte","topics":[{"slug":"oocyte","score":8.0,"hits":["oocyte","oocytes","oogenesis","ovarian reserve","ovaries"]},{"slug":"repro-endo","score":2.0,"hits":["amh","anti-müllerian"]}],"topic_score":8.0,"preference_score":0.0,"preference_labels":[],"llm_score":64.0,"final_score":64.0,"score_dims":{"novelty":82,"rigor":78,"impact":45,"relevance":50,"translation":35},"recommend_reason":"方法学上较有新意，将PINN与非局部交互的大小结构种群模型结合，用于推断卵母细胞生长和AMH反馈动态；但物种为鱼类、数据为横断面观测，与临床ART和人类卵泡生物学距离较远，适合关注计算生殖生物学的读者浏览。","keywords":["卵子发生","AMH","物理信息神经网络","大小结构种群模型","反问题推断","鱼类模型"],"selected":1,"story_id":29,"llm_provider":"deepseek","llm_model":"deepseek-flash","llm_effort":"low","source_key":"pubmed-gametes","source_name":"PubMed · 配子与性腺","source_tier":1,"mention_count":2,"has_interpretation":false,"journal_key":null,"journal_tier":"unknown","journal_if":null,"is_mock":false,"summary_zh":"该研究以幼鱼早期卵子发生为对象，构建包含未分化生殖细胞区室与生长卵母细胞种群的大小结构种群模型，并引入AMH对前体细胞更新的反馈调节。作者利用重复横断面卵母细胞大小分布数据，建立非线性反问题，用物理信息神经网络（PINN）推断卵母细胞生长率、AMH调控的净更新率和卵母细胞招募率，并结合辅助PINN处理非局部交互项。研究进一步评估函数与参数层面的可识别性，并用个体水平bootstrap分析采样与训练敏感性。结果显示该框架可恢复可解释的生长与更新动态，并推断未观测变量；但研究对象为鱼类，向人类ART的转化尚远。","summary":"Early oogenesis in juvenile fish establishes the ovarian reserve, which conditions lifelong reproductive capacity. This process is regulated by local hormonal feedback mechanisms, mainly involving Anti-Müllerian Hormone (AMH). In this work, we develop a mechanistic size-structured population model describing an unstructured germ-cell compartment and a population of growing oocytes, with AMH feedback on the renewal of the precursor cells compartment. Using repeated cross-sectional observations of oocyte size distributions in fish ovaries, we formulate and solve a nonlinear inverse problem with Physics-Informed Neural Networks (PINNs) to infer three demographic functions: the size-dependent oocyte growth rate, the AMH-regulated net renewal rate of the precursor cells compartment, and the recruitment rate of new oocytes. We introduce an auxiliary PINN formulation to handle the nonlocal interaction term and jointly infer unobserved state variables and demographic functions. Our inference strategy combines non-parametric PINNs with a data-guided model-selection step: demographic functions are first inferred without prescribing their functional forms, and the learned profiles are then used to select parsimonious parametric laws, whose parameters are subsequently estimated within a parametric PINN. We investigate identifiability at both the functional and parametric levels, combining structural identifiability analysis with synthetic-data experiments assessing the practical recovery of functions and parameters. The inference procedure is embedded within an individual-level bootstrap framework to assess sensitivity to repeated-cross-sectional sampling and stochastic training. Applied to juvenile fish oogenesis, the framework recovers interpretable growth and renewal dynamics, provides access to unobserved quantities, and enables conditional in silico investigation. More broadly, this work provides a framework for functional and parametric inference in size-structured population models with nonlocal interactions from sparse cross-sectional data.","interpretation_basis":null,"fulltext_status":null,"interpretation":null,"fulltext":{"available":false,"status":null,"reason":"尚未抓取全文（未入选、分数未达阈值，或还没跑 fulltext 步骤）","pmcid":null,"license":null,"chars":0,"source":null,"fetched_at":null}}