论文标题
广义的Glauber动力学用于研究生物学
Generalized Glauber dynamics for inference in biology
论文作者
论文摘要
生物学中的大型相互作用系统通常表现出新兴的动力学，例如多个时间尺度的共存，在等待时间的分布中表现出了脂肪尾巴。虽然统计推断中的现有工具（例如最大熵模型）重现经验稳态分布，但学习动态模型仍然具有挑战性。我们提出了一种新颖的推理方法，称为广义Glauber动力学。通过非马克维亚波动耗散定理构建的广义Glauber动力学调节了相互作用系统的动力学，同时保持稳态分布固定。我们激发了从Eco-Hab的真实数据的需求，这是一种在半自然环境下测试小鼠组中行为的自动栖息地，并将其呈现在简单的Ising Spin Systems上。我们通过在一组鼠标中推断出社会相互作用的动态模型来展示其对实验数据的适用性，这些模型既可以重现其集体行为和在个体动力学中观察到的长尾巴。
Large interacting systems in biology often exhibit emergent dynamics, such as coexistence of multiple time scales, manifested by fat tails in the distribution of waiting times. While existing tools in statistical inference, such as maximum entropy models, reproduce the empirical steady state distributions, it remains challenging to learn dynamical models. We present a novel inference method, called generalized Glauber dynamics. Constructed through a non-Markovian fluctuation dissipation theorem, generalized Glauber dynamics tunes the dynamics of an interacting system, while keeping the steady state distribution fixed. We motivate the need for the method on real data from Eco-HAB, an automated habitat for testing behavior in groups of mice under semi-naturalistic conditions, and present it on simple Ising spin systems. We show its applicability for experimental data, by inferring dynamical models of social interactions in a group of mice that reproduce both its collective behavior and the long tails observed in individual dynamics.
