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一种针对多种疫苗平台和病原体的疫苗诱导抗体动态的共识数学模型

Kristen M Wilding¹, Carmen Molina-París¹, Jessica Z Kubicek-Sutherland²

¹Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, United States.

Frontiers in immunology

|July 11, 2025

概括

一个新的数学模型分析了各种疫苗平台和病原体的抗体生成动态. 该工具通过预测免疫反应和减少实验工作量,有助于选择最佳的疫苗策略.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
疫苗学疫苗学疫苗学

背景情况:

抗体水平是大多数授权疫苗保护的关键相关因子.
了解不同疫苗平台的抗体生成动态对于病原体特异性疫苗开发至关重要.

研究的目的:

为抗体生成动态开发一个共识的数学模型.
分析和比较跨不同疫苗平台和病原体的抗体动态.

主要方法:

开发了一种数学模型,包括B细胞增殖,血细胞生成和记忆B细胞形成.
利用机器学习转移学习,将模型应用于各种病原体-疫苗系统.
最初将模型与SARS-CoV-2疫苗数据相匹配,然后将其调整为其他系统.

主要成果:

该模型准确地描述了所有测试系统的抗体生成.
疫苗平台之间的关键差异主要与抗原呈现动态有关.
该模型捕捉了B细胞的动态,包括扩散和分化为分泌抗体的等离子体细胞.

结论:

开发的模型为预测特定疫苗平台/病原体组合的抗体生成提供了一个框架.
在 silico 预测可以显著减少疫苗开发中的实验负担.
对抗体动态的更好理解可以为特定病原体的最佳疫苗平台做出决定.

关键词:

B 细胞细胞B 细胞细胞这就是SARS-CoV-2病毒.抗体动力学抗体动力学埃博拉病毒是什么意思数学模型是一个数学模型.记忆记忆记忆记忆记忆疫苗疫苗疫苗疫苗疫苗平台的疫苗平台是什么

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