Kolczyk Katrin, Conradi Carsten
Max-Planck-Institute Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106, Magdeburg, Germany.
BMC Syst Biol. 2016 Mar 11;10:28. doi: 10.1186/s12918-016-0266-3.
Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field.
Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model.
The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database.
系统生物学推动了在通路水平上对重要细胞内过程的动态建模,例如在信号转导和细胞周期控制方面。然而,为了回答重要的生物医学问题,人们必须超越对孤立通路的研究,转向对相互作用的信号通路的联合研究,或者对信号转导和细胞周期控制的联合研究。因此,重用已建立的模型是可取的,因为这通常会减少建模工作量,并提高该领域对组合模型的接受度。
获得一个组合模型可能具有挑战性,特别是当子模型很大和/或来自不同的工作组时(当使用存储在既定存储库中的模型时,通常就是这种情况)。为了支持这项任务,我们描述了一种基于既定软件工具的半自动工作流程。特别描述了两个常见的挑战:识别重叠部分以及对集成模型进行后续的(重新)参数化。
如果目标是获得一个能够重现由各个模型解释的数据的模型,那么重新参数化步骤至关重要。为了演示目的,我们应用我们的工作流程来整合来自生物模型数据库的两条信号通路(表皮生长因子和神经生长因子)。