使用SED-ML进行可重现的策划:在多个模拟引擎中验证生物模型
Lucian Smith1, Rahuman S Malik-Sheriff2, Tung V N Nguyen2
1Department of Bioengineering, University of Washington, Seattle, WA, USA.
bioRxiv : the preprint server for biology
|February 3, 2025
概括
我们更新和纠正了BioModels数据库中的1055个机械模型的模拟实验描述标记语言 (SED-ML) 文件. 这提高了使用这些模型的计算生物学研究的可复制性和可信度.
科学领域:
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 生物模型存储库拥有超过1000个精心策划的机械模型,主要是系统生物学标记语言 (SBML).
- SBML正式定义模型,但缺乏计算实验条件,阻碍了结果的可复制性.
- 模拟实验描述标记语言 (SED-ML) 标准化了模拟的实验条件.
研究的目的:
- 在BioModels中增强策划的机械模型的可复制性和可信性.
- 更新和纠正现有的SED-ML文件,并为生物模型集合的很大一部分提供新的文件.
- 利用SED-ML来验证跨多个计算引擎的模拟结果.
主要方法:
- 来自生物模型库的1055个精心策划的机械模型的更新和更正的SED-ML文件.
- 开发了一个用于解释SED-ML文件的包装架构.
- 在五种不同的基于普通微分方程 (ODE) 的生物模拟引擎中对模拟结果进行了验证.
主要成果:
- 成功更新和纠正了1055个生物模型条目的SED-ML文件,解决了以前的不准确性.
- 通过交叉引擎验证证明了SED-ML的实现独立性.
- 为大量生物模型收集提供了一套更强大,更可靠的模拟实验描述.
结论:
- 更新后的SED-ML文件显著提高了生物模型结果的可复制性.
- 使用SED-ML的跨平台验证提高了计算模型的可信度.
- 这项工作加强了BioModels集合对系统生物学社区的实用性.
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