Arakane Kiwamu, Imoto Hiroaki, Ormersbach Fabian, Okada Mariko
Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.
BioQuant, Heidelberg University, Heidelberg 69120, Germany.
Bioinform Adv. 2024 Apr 4;4(1):vbae042. doi: 10.1093/bioadv/vbae042. eCollection 2024.
Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models.
We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models.
The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.
基于常微分方程的机理建模通过整合先验知识和实验数据，在系统生物学领域取得了众多发现。然而，构建模型时所需的知识人工整理成为了一个瓶颈。随着知识积累速度持续加快，人们需要一种可扩展的方法来构建可执行模型。
我们之前引入了BioMASS——一个基于Python的开源框架——用于构建、模拟和分析信号网络的机理模型。凭借其Text2Model这一特性，BioMASS允许用户以类似自然语言的格式定义模型，从而便于构建大规模模型。我们证明，Text2Model可作为一种工具，通过从通路数据库生成Text2Model文件或使用大语言模型，并通过BioMASS模拟其动态，来整合外部知识用于数学建模。我们的研究结果揭示了该工具鼓励基于先验知识进行探索的能力，并为构建数学模型的完全数据驱动方法铺平了道路。
BioMASS的代码和文档分别可在https://github.com/biomass-dev/biomass和https://biomass-core.readthedocs.io获取。本文中使用的代码可在https://github.com/okadalabipr/text2model-from-knowledge获取。
