A toolbox for discrete modelling of cell signalling dynamics.
A toolbox for discrete modelling of cell signalling dynamics.
复制标题
用于细胞信号动力学离散建模的工具箱。
DOI：
10.1039/c8ib00026c
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Paterson YZ
中科院分区：
文献类型：
--
作者：
Paterson YZ
在一个关于生物系统的数据量超过我们分析能力的时代，许多研究人员正在寻求系统生物学和计算建模，以帮助解开基因和蛋白质调控网络的复杂性。特别是，离散建模的使用允许在没有完整的定量描述的系统，这是必要的常微分方程（ODE）模型的信令网络的生成。为了使主流研究人员更容易使用这些技术，开发了BioModelAnalyzer（BMA）等工具，为生物系统的离散建模提供用户友好的图形界面。在这里，我们使用BMA建立一个库的离散目标函数已知的典型分子相互作用，从常微分方程（ODE）翻译。然后，我们表明，这些BMA目标函数可以用来重建复杂的网络，它可以正确地预测许多已知的遗传扰动。这个新的库支持创建BMA背后的可访问性精神，为构建复杂的细胞信号模型提供了工具箱，而不需要在计算机编程或数学建模方面的丰富经验，并允许仅用少量定量数据构建和模拟复杂的生物系统。
In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.