Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52428, Germany.
Bioinformatics. 2020 Jan 1;36(1):232-240. doi: 10.1093/bioinformatics/btz500.
The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging.
Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference.
Supplementary data are available at Bioinformatics online.
系统生物学中使用的基于模型的推理的有效性取决于基础模型的表述。通常，有大量相互竞争的模型可用，这些模型基于不同的假设构建，所有这些假设都与研究的生物现象的现有知识一致。作为对此的补救措施，贝叶斯模型平均（BMA）促进了基于多个模型同时进行参数和结构推断。然而，在涉及大量替代的、高维的和非线性模型的领域中，基于 BMA 的推断任务在计算上具有很大的挑战性。
在这里，我们在代谢通量分析（MFA）的复杂环境中使用 BMA，使用 13C 标记数据和代谢网络，推断潜在的可逆反应是单向还是双向进行。BMA 应用于具有不同方向设置的大量候选模型，使用定制的多模型马尔可夫链蒙特卡罗（MCMC）方法。我们的算法的适用性通过在现实网络设置中推断反应双向性的体内概率来证明，从而将 13C MFA 的范围从参数推断扩展到结构推断。
补充数据可在“Bioinformatics”在线获取。
