多尺度行为动力学中表型变异的解析
Unraveling phenotypic variation in multiscale behavioral dynamics
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中文总结 AI 辅助
本研究提出一种多尺度相异性度量与扩散映射方法，从有限行为观测中重建动力学表型，无需预设比较尺度，并在细菌和斑马鱼中验证，识别出影响行为变异的关键参数与经历。
中文摘要 AI 辅助
行为变异性反映了内部和外部参数的差异，并为自然选择提供了基础。然而，由于行为在多个时间尺度上非线性演化，揭示其结构具有挑战性。我们的目标是从有限的行为观测中重建表型变异，而不预设个体应被比较的尺度。为此，我们引入了一种多尺度相异性度量，通过在不同精细尺度上构建约简转移算子，并根据有限采样不确定性对其差异进行加权，来比较所有统计上可分辨分辨率下的动力学。该相异性度量定义了一种动力学表型的几何结构，我们使用扩散映射对其进行重建。我们表明，所得的表型坐标在局部上遵循动力学最敏感的参数方向。我们在一个随机模型中验证了我们的方法，并将其应用于细菌和斑马鱼幼虫的行为。在细菌中，我们确定游动速度是个体间变异的主要组成部分，并与趋化蛋白CheB相关。在斑马鱼中，我们发现先前的猎物经历重塑了行为动力学和表型变异性，草履虫经历使鱼集中在表型空间中与捕食猎物相关的区域。这些结果共同提供了一个框架，用于识别在不同尺度上构建动力学表型的内部和环境变量。
英文摘要
Behavioral variability reflects differences in internal and external parameters and provides a substrate for natural selection. However, uncovering its structure is challenging because behavior evolves nonlinearly across multiple timescales. Our goal is to reconstruct phenotypic variation from finite behavioral observations without prescribing the scales at which individuals should be compared. To achieve this, we introduce a multiscale dissimilarity that compares dynamics across all statistically resolvable resolutions by constructing reduced transfer operators at progressively finer scales and weighting their differences by finite-sampling uncertainty. This dissimilarity defines a geometry of dynamical phenotypes, which we reconstruct using diffusion maps. We show that the resulting phenotypic coordinates locally follow the parameter directions to which dynamics are most sensitive. We validate our approach in a stochastic model and apply it to bacterial and larval zebrafish behavior. In bacteria, we identify run speed as a major component of inter-individual variability, associated with the chemotaxis protein CheB. In zebrafish, we find that prior prey experience reshapes behavioral dynamics and phenotypic variability, with paramecia experience concentrating fish in a prey-capture-associated region of phenotypic space. Together, these results provide a framework for identifying internal and environmental variables that structure dynamical phenotypes across scales.
发表机构
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225（索邦大学，巴黎脑研究所）
- OIST Graduate University（冲绳科学技术大学院大学）
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