Domain-oriented reduction of rule-based network models.
Domain-oriented reduction of rule-based network models.
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DOI：
10.1049/iet-syb:20070081
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发表时间：
2008-09
影响因子：
2.3
通讯作者：
Kholodenko BN
中科院分区：
文献类型：
--
作者：
Borisov NM;Chistopolsky AS;Faeder JR;Kholodenko BN
膜结合受体与转录调节因子和其他效应子功能的偶联是由形成复杂组件的多域蛋白介导的。蛋白质相互作用的模块化有助于基于规则的描述，其中物种和反应是由规则生成的，这些规则对发生相互作用的必要上下文进行编码，但也可能导致构成信号网络的化学物种数量的组合爆炸。我们之前已经证明，可以使用蛋白质上位点/结构域之间的分层控制关系来实现精确的网络缩减，将多结构域蛋白质分解为一组非相互作用位点，从而允许用一组衍生的辅助（后代）蛋白质替换每个“完整”（祖体）蛋白质。用比祖蛋白具有更少位点的辅助蛋白来描述网络通常会大大减小网络大小。我们在这里描述了一种自动化面向领域模型简化的方法及其作为 BioNetGen 建模包中的模块的实现。它以标准 BioNetGen 模型作为输入，并自动执行以下步骤：1）检测站点之间的层次控制关系； 2）建立辅助蛋白； 3）生成原始简化模型； 4) 清理原始模型，为简化网络中的每种化学物质提供正确的质量平衡。我们在代表生长因子受体和免疫受体介导的信号网络部分的模型上测试了该模块的性能，并确认其能够将模型大小和模拟成本减少至少一两个数量级。当前算法的局限性包括无法基于隐式位点依赖性或异二聚化来简化模型，以及随机计算动态时的准确性损失。
The coupling of membrane-bound receptors to transcriptional regulators and other effector functions is mediated by multi-domain proteins that form complex assemblies. The modularity of protein interactions lends itself to a rule-based description, in which species and reactions are generated by rules that encode the necessary context for an interaction to occur, but also can produce a combinatorial explosion in the number of chemical species that make up the signaling network. We have shown previously that exact network reduction can be achieved using hierarchical control relationships between sites/domains on proteins to dissect multi-domain proteins into sets of non-interacting sites, allowing the replacement of each “full” (progenitor) protein with a set of derived auxiliary (offspring) proteins. The description of a network in terms of auxiliary proteins that have fewer sites than progenitor proteins often greatly reduces network size. We describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modeling package. It takes as input a standard BioNetGen model and automatically performs the following steps: 1) detecting the hierarchical control relationships between sites; 2) building up the auxiliary proteins; 3) generating a raw reduced model; and 4) cleaning up the raw model to provide the correct mass balance for each chemical species in the reduced network. We tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signaling networks, and confirmed its ability to reduce the model size and simulation cost by at least one or two orders of magnitude. Limitations of the current algorithm include the inability to reduce models based on implicit site dependencies or heterodimerization, and loss of accuracy when dynamics are computed stochastically.
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影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
3
作者：
Conzelmann H;Saez-Rodriguez J;Sauter T;Kholodenko BN;Gilles ED
通讯作者：
Gilles ED
影响因子：
3.4
作者：
Borisov, NM;Markevich, NI;Kholodenko, BN
通讯作者：
Kholodenko, BN
影响因子：
3.4
作者：
Barua, Dipak;Faeder, James R.;Haugh, Jason M.
通讯作者：
Haugh, Jason M.
影响因子：
2
作者：
Morton-Firth, CJ;Bray, D
通讯作者：
Bray, D