Domedel-Puig Núria, Pournara Iosifina, Wernisch Lorenz
Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Edifici GAIA, Rambla de Sant Nebridi s/n 08222, Terrassa, Barcelona, Spain.
BMC Syst Biol. 2010 Mar 3;4:18. doi: 10.1186/1752-0509-4-18.
Network motifs are small modules that show interesting functional and dynamic properties, and are believed to be the building blocks of complex cellular processes. However, the mechanistic details of such modules are often unknown: there is uncertainty about the motif architecture as well as the functional form and parameter values when converted to ordinary differential equations (ODEs). This translates into a number of candidate models being compatible with the system under study. A variety of statistical methods exist for ranking models including maximum likelihood-based and Bayesian methods. Our objective is to show how such methods can be applied in a typical systems biology setting.
We focus on four commonly occurring network motif structures and show that it is possible to differentiate between them using simulated data and any of the model comparison methods tested. We expand one of the motifs, the feed forward (FF) motif, for several possible parameterizations and apply model selection on simulated data. We then use experimental data on three biosynthetic pathways in Escherichia coli to formally assess how current knowledge matches the time series available. Our analysis confirms two of them as FF motifs. Only an expanded set of FF motif parameterizations using time delays is able to fit the third pathway, indicating that the true mechanism might be more complex in this case.
Maximum likelihood as well as Bayesian model comparison methods are suitable for selecting a plausible motif model among a set of candidate models. Our work shows that it is practical to apply model comparison to test ideas about underlying mechanisms of biological pathways in a formal and quantitative way.
网络基序是显示出有趣功能和动态特性的小模块，被认为是复杂细胞过程的构建块。然而，此类模块的机制细节通常未知：当转换为常微分方程（ODE）时，基序结构以及功能形式和参数值存在不确定性。这导致许多候选模型与所研究的系统兼容。存在多种用于对模型进行排名的统计方法，包括基于最大似然法和贝叶斯方法。我们的目标是展示如何在典型的系统生物学环境中应用这些方法。
我们专注于四种常见的网络基序结构，并表明使用模拟数据和任何测试的模型比较方法都可以区分它们。我们针对几种可能的参数化扩展了其中一种基序，即前馈（FF）基序，并对模拟数据应用模型选择。然后，我们使用大肠杆菌中三条生物合成途径的实验数据来正式评估当前知识与可用时间序列的匹配程度。我们的分析确认其中两条为FF基序。只有使用时间延迟的一组扩展的FF基序参数化能够拟合第三条途径，这表明在这种情况下真实机制可能更复杂。
最大似然法以及贝叶斯模型比较方法适用于在一组候选模型中选择合理的基序模型。我们的工作表明，以正式和定量的方式应用模型比较来测试有关生物途径潜在机制的想法是可行的。
