Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
可变食性范围模型中向泛化主义的缓慢演化
Department of Mathematical Sciences, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
30秒速读
IN SHORT: 通过证明是种群统计噪声（而非确定性动力学）驱动了模式形成和泛化食性的演化，解决了间接竞争下物种形成的悖论。
核心创新
- Methodology Develops a continuous-space resource-consumer model with explicit resource dynamics and evolvable dietary range, extending beyond fixed-preference Lotka-Volterra frameworks.
- Theory Demonstrates that deterministic analysis predicts homogeneous steady states (no species), but stochastic simulations with demographic noise induce persistent pattern formation interpreted as species.
- Methodology Uses Fourier analysis of linearized dynamics to predict the dominant perturbation modes (e.g., number of species) from the power spectrum, linking analytical predictions to simulation outcomes.
主要结论
- 对于固定食性范围（w=0.2），傅里叶分析预测在 kL/2π=5 处存在主导模式，这对应于在随机模拟中观察到的5个等间距物种的形成（图2，3A）。
- 在可演化食性范围模型中，动力学发生在两个时间尺度上：快速协同演化到准稳态流形，随后缓慢弛豫向均匀态。种群统计噪声阻止了这种弛豫，维持了模式。
- 泛化食性（宽w）在长时间尺度上随机演化，因为与由相同资源生产率支持的、种群规模较小的专化集群相比，它们更不易受到灭绝风险的影响。
摘要: 共享栖息地的物种会协同演化以利用可用资源，因为消费受到消费者与资源之间竞争和负反馈回路的调节。给定物种的食性范围决定了其可获取的资源，从而决定了与之竞争的其他物种。狭窄的食性范围以过度依赖少量资源为代价避免竞争；相反，广泛的食性范围提供了更多替代选择，但也增加了与其他物种竞争的机会。在此，我们研究了生态位形成数学模型中食性范围的演化。我们发现了高度路径依赖的协同演化动力学，其特征是长寿命的准稳态。最终，随机效应驱动了泛化食性的演化，正如我们在分析和模拟中所揭示的。