扩展BioMASS,从外部知识中构建数学模型
Kiwamu Arakane1, Hiroaki Imoto1, Fabian Ormersbach2
1Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.
Bioinformatics advances
|April 12, 2024
概括
使用Text2Model的BioMASS框架自动化了自然语言的机械模型构建,克服了系统生物学中的手动策划瓶. 这加速了先前知识的整合,以实现可扩展的,数据驱动的数学建模.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 数学建模的数学建模
背景情况:
- 使用普通微分方程的机制建模在系统生物学中至关重要.
- 模型构建的手动知识策划是一个重要的瓶.
- 越来越多的知识积累需要可扩展的方法来生成可执行的模型.
研究的目的:
- 在BioMASS框架内引入和展示Text2Model功能的能力.
- 为了促进信号网络的大规模机械模型的构建.
- 为了使数学模型开发能够采用更加数据驱动的方法.
主要方法:
- 利用BioMASS框架,这是一个开源的Python工具,用于机械模型构建,模拟和分析.
- 使用Text2Model功能来定义使用类似自然语言格式的模型.
- 从路径数据库和用于模拟的大型语言模型中生成Text2Model文件.
主要成果:
- 证明了Text2Model在将外部知识整合到数学模型中的有效性.
- 使用Text2Model输入成功生成和模拟机械模型.
- 展示了框架鼓励对先前知识的探索的能力.
结论:
- 在BioMASS中的Text2Model有效地解决了手动知识策划的瓶.
- 该框架支持可执行机械模型的可扩展构建.
- 这种方法为系统生物学中完全数据驱动的数学建模铺平了道路.
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