MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics.
MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics.
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DOI：
10.1371/journal.pcbi.1008208
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发表时间：
2021-01
影响因子：
4.3
通讯作者：
Palsson BO
中科院分区：
文献类型：
--
作者：
Haiman ZB;Zielinski DC;Koike Y;Yurkovich JT;Palsson BO
代谢网络的数学模型利用模拟来研究系统级机制和功能。已经使用了各种方法来使用基因组规模的重建来建模代谢网络的稳态行为，但是从这样的重建中制定动态模型仍然是一个关键的挑战。在这里，我们提出了质量行动化学计量模拟Python（MASSpy）包，这是一个用于代谢动态建模的开源计算框架。MASSpy利用质量作用动力学和详细的化学机制来构建复杂生物过程的动态模型。MASSpy将动态建模工具添加到基于约束的重建和分析Python（COBRApy）包中，为代谢网络的基于约束和动力学建模提供统一的框架。MASSpy通过其libRoadRunner（系统生物学标记语言（SBML）仿真引擎）的实现支持高性能动态仿真。通过三个实例说明如何使用MASSpy：（1）通过动态模拟酶调控的详细机制验证MASSpy建模工具;（2）使用工作流的特征演示，所述工作流用于使用蒙特卡罗采样来生成动力学模型的集合，以近似参数的缺失数值并量化生物不确定性，和（3）一个案例研究，其中MASSpy被用来克服当将实验数据与详细的生物机制的功能状态的计算相结合时出现的问题。MASSpy是一个强大的工具，可以解决代谢网络动态建模中出现的挑战，无论是在小规模还是大规模。在第一个基因组序列出现后不久，基因组规模的代谢重建就出现了。基于约束的模型被广泛用于计算这种重建的稳态特性，但动态模型的实现仍然难以捉摸。因此，我们开发了MASSpy软件包，这是一个能够构建、模拟和可视化动态代谢模型的框架。MASSpy基于酶促反应机制中每个基本步骤的质量作用动力学。MASSpy在其框架内无缝地结合了现有的软件包，为用户提供了一个包中的各种建模工具。MASSpy集成了社区标准以促进模型的交换，使建模人员可以自由地将软件用于自己建模工作流的不同方面。此外，MASSpy包含用于生成和模拟模型集合的方法，以及明确说明生物不确定性的方法。MASSpy已经在课堂环境中取得了成功。我们预计，该套件的建模工具纳入MASSpy将提高建模社区的能力，以构建和查询复杂的动态模型的新陈代谢。
Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales. Genome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.
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DOI：
10.1126/science.1234012
发表时间：
2013-06-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Chang RL;Andrews K;Kim D;Li Z;Godzik A;Palsson BO
通讯作者：
Palsson BO
DOI：
10.1016/j.biosystems.2018.07.006
发表时间：
2018-09
期刊：
Bio Systems
影响因子：
--
作者：
Choi K;Medley JK;König M;Stocking K;Smith L;Gu S;Sauro HM
通讯作者：
Sauro HM
影响因子：
5.8
作者：
Bornstein, Benjamin J.;Keating, Sarah M.;Hucka, Michael
通讯作者：
Hucka, Michael
影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
2.7
作者：
Hindmarsh, AC;Brown, PN;Woodward, CS
通讯作者：
Woodward, CS