BioModTool: from biomass composition data to structured biomass objective functions for genome-scale metabolic models.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-02-21 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf036

Clémence Dupont Thibert, Sylvaine Roy, Gilles Curien, Maxime Durot

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引用次数: 0

Abstract

Summary: BioModTool is a Python program allowing easy generation of biomass objective functions for genome-scale metabolic models from user data. BioModTool loads biomass composition data in the form of a structured Excel file completed by the user, normalizes these data into model-compatible units (mmol.gDW^-1), and creates a structured biomass objective function to update a metabolic model. Aimed at a wide range of users, BioModTool can be run as a Python module compatible with COBRApy but also comes with an interface allowing its use by non-modelers. By providing an easy definition of new biomass objective functions, BioModTool can accelerate new genome-scale metabolic reconstructions, improve existing ones, and facilitate biomass-specific experimental datasets analyses with genome-scale models.

Availability and implementation: BioModTool is publicly available on PyPI (https://pypi.org/project/BioModTool/) under a GNU Lesser General Public License (LGPL). Installation instructions and source code are available on GitHub (https://github.com/Total-RD/BioModTool). BioModTool is compatible with Windows, Linux, and MacOS operating systems.

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BioModTool：从生物量组成数据到基因组尺度代谢模型的结构化生物量目标函数。

BioModTool是一个Python程序，允许从用户数据中轻松生成基因组尺度代谢模型的生物量目标函数。BioModTool以用户完成的结构化Excel文件的形式加载生物质成分数据，将这些数据归一化为模型兼容单位（mmol.gDW-1），并创建结构化生物质目标函数来更新代谢模型。针对广泛的用户，BioModTool可以作为与COBRApy兼容的Python模块运行，但也附带了一个允许非建模者使用的接口。通过提供新的生物量目标函数的简单定义，BioModTool可以加速新的基因组尺度代谢重建，改进现有的代谢重建，并促进基因组尺度模型对生物量特异性实验数据集的分析。可用性和实现：BioModTool在GNU较宽松通用公共许可证（LGPL）下在PyPI （https://pypi.org/project/BioModTool/）上公开提供。安装说明和源代码可在GitHub （https://github.com/Total-RD/BioModTool）上获得。BioModTool支持Windows、Linux和MacOS操作系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量