Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, Basel, 4058, Switzerland.
ID Scientific IT Services, ETH Zurich, Zurich, 8092, Switzerland.
BMC Bioinformatics. 2020 Jan 29;21(1):34. doi: 10.1186/s12859-020-3343-y.
To develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology.
The TopoFilter package addresses this problem in a heuristic and automated fashion by implementing the previously described topological filtering method for Bayesian model selection. It includes a core heuristic for searching the space of submodels of a parametrized model, coupled with a sampling-based exploration of the parameter space. Recent developments of the method allow to balance exhaustiveness and speed of the model space search, to efficiently re-sample parameters, to parallelize the search, and to use custom scoring functions. We use a theoretical example to motivate these features and then demonstrate TopoFilter's applicability for a yeast signaling network with more than 250'000 possible model structures.
TopoFilter is a flexible software framework that makes Bayesian model selection and reduction efficient and scalable to network models of a complexity that represents contemporary problems in, for example, cell signaling. TopoFilter is open-source, available under the GPL-3.0 license at https://gitlab.com/csb.ethz/TopoFilter. It includes installation instructions, a quickstart guide, a description of all package options, and multiple examples.
在系统生物学中，为了开发机制动态模型，我们经常需要从潜在的大量假设机制中，确定与实验数据一致的所有（或最小）生物过程表示。然而，当模型参数的数量增加时，对所有替代方案进行简单枚举很快就变得难以处理。因此，在考虑到我们的生物学知识和实验数据的不确定性的情况下，从大量模型中选择合适的动态模型，是系统生物学中的一个关键问题。
TopoFilter 包通过实现先前描述的用于贝叶斯模型选择的拓扑过滤方法，以启发式和自动化的方式解决了这个问题。它包括一个用于搜索参数化模型的子模型空间的核心启发式方法，以及对参数空间的基于抽样的探索。该方法的最新进展允许平衡模型空间搜索的详尽性和速度，有效地重新采样参数，并行搜索，并使用自定义评分函数。我们使用一个理论示例来激发这些特性，然后展示 TopoFilter 在酵母信号网络中的适用性，该网络有超过 250'000 种可能的模型结构。
TopoFilter 是一个灵活的软件框架，它使贝叶斯模型选择和简化变得高效且可扩展，适用于代表当代问题的网络模型，例如细胞信号。TopoFilter 是开源的，可在 https://gitlab.com/csb.ethz/TopoFilter 下根据 GPL-3.0 许可证获得。它包括安装说明、快速入门指南、所有包选项的说明以及多个示例。
