Paper List
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Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
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Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
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Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
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Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
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Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
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。