Simulation data from: Dynamics of immune memory and learning in bacterial communities

From bacteria to humans, adaptive immune systems provide learned memories of past infections. Despite their vast biological differences, adaptive immunity shares features from microbes to vertebrates such as emergent immune diversity, long-term coexistence of hosts and pathogens, and fitness pressures from evolving pathogens and adapting hosts, yet there is no conceptual model that addresses all of these together. To address these questions, we propose and solve a simple phenomenological model of CRISPR-based adaptive immunity in microbes. We show that in coexisting phage and bacteria populations, immune diversity in both populations emerges spontaneously and in tandem, that bacteria track phage evolution with a context-dependent lag, and that high levels of diversity are paradoxically linked to low overall CRISPR immunity. We define average immunity, an important summary parameter predicted by our model, and use it to perform synthetic time-shift analyses on available experimental data..., This data was simulated using a custom python script to model a community of bacteria and phages interacting with CRISPR adaptive immunity. Each simulation uses the tau-leaping method to approximate the Gillespie stochastic simulation algorithm. The base simulation script is the same for all simulations with modifications to different parameters., Simulation files are stored as plain text files. An intermediate processing file is included for each simulation that stores the simulation results in a compressed scipy sparse array (*.npz). Each individual simulation folder contains the Python script used to generate the simulation results.

从细菌到人类，适应性免疫系统（adaptive immune systems）均可对过往感染形成习得性记忆。尽管各类生物的适应性免疫系统存在巨大生物学差异，但从微生物到脊椎动物，它们的适应性免疫均具备若干共同特征：涌现性免疫多样性、宿主与病原体的长期共存、以及演化中的病原体与适应性宿主带来的适合度选择压力；然而目前尚无能够同时涵盖上述所有特征的概念模型。为解决上述问题，我们提出并求解了一个针对微生物CRISPR（CRISPR）适应性免疫的简易现象学模型。我们的研究表明，在共存的噬菌体与细菌种群中，两类种群的免疫多样性会自发且同步地涌现；细菌会以依赖于环境的滞后性方式追踪噬菌体的演化；而高多样性水平与较低的整体CRISPR免疫活性存在悖论性关联。我们定义了平均免疫性（average immunity）——这是我们模型预测出的一项重要汇总参数，并基于该参数对已公开的实验数据开展合成时移分析……本数据集通过定制Python脚本模拟生成，用于建模CRISPR适应性免疫介导的细菌与噬菌体群落互作过程。每一次模拟均采用tau-leaping方法近似实现吉莱斯皮（Gillespie）随机模拟算法。所有模拟均基于同一基础模拟脚本，仅针对不同参数进行修改。模拟文件以纯文本格式存储。每一次模拟均附带一份中间处理文件，以压缩的SciPy稀疏数组（*.npz）格式存储模拟结果。每个独立的模拟文件夹均包含用于生成该模拟结果的Python脚本。