Durzinsky Markus, Wagler Annegret, Marwan Wolfgang
Magdeburg Centre for Systems Biology, Otto-von-Guericke-Universität, Magdeburg, Germany.
BMC Syst Biol. 2011 Jul 15;5:113. doi: 10.1186/1752-0509-5-113.
Network inference methods reconstruct mathematical models of molecular or genetic networks directly from experimental data sets. We have previously reported a mathematical method which is exclusively data-driven, does not involve any heuristic decisions within the reconstruction process, and deliveries all possible alternative minimal networks in terms of simple place/transition Petri nets that are consistent with a given discrete time series data set.
We fundamentally extended the previously published algorithm to consider catalysis and inhibition of the reactions that occur in the underlying network. The results of the reconstruction algorithm are encoded in the form of an extended Petri net involving control arcs. This allows the consideration of processes involving mass flow and/or regulatory interactions. As a non-trivial test case, the phosphate regulatory network of enterobacteria was reconstructed using in silico-generated time-series data sets on wild-type and in silico mutants.
The new exact algorithm reconstructs extended Petri nets from time series data sets by finding all alternative minimal networks that are consistent with the data. It suggested alternative molecular mechanisms for certain reactions in the network. The algorithm is useful to combine data from wild-type and mutant cells and may potentially integrate physiological, biochemical, pharmacological, and genetic data in the form of a single model.
网络推断方法直接从实验数据集重建分子或遗传网络的数学模型。我们之前报道了一种完全由数据驱动的数学方法，在重建过程中不涉及任何启发式决策，并以与给定离散时间序列数据集一致的简单位置/变迁Petri网的形式给出所有可能的替代最小网络。
我们从根本上扩展了之前发表的算法，以考虑基础网络中发生的反应的催化和抑制作用。重建算法的结果以包含控制弧的扩展Petri网的形式编码。这允许考虑涉及质量流和/或调节相互作用的过程。作为一个具有挑战性的测试案例，使用野生型和计算机模拟突变体的计算机生成时间序列数据集重建了肠杆菌的磷酸盐调节网络。
新的精确算法通过找到与数据一致的所有替代最小网络，从时间序列数据集中重建扩展Petri网。它为网络中某些反应提出了替代分子机制。该算法有助于结合来自野生型和突变体细胞的数据，并可能以单一模型的形式整合生理、生化、药理和遗传数据。
