Jimena: efficient computing and system state identification for genetic regulatory networks.
Jimena: efficient computing and system state identification for genetic regulatory networks.
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DOI：
10.1186/1471-2105-14-306
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发表时间：
2013-10-11
影响因子：
3
通讯作者：
Dandekar T
中科院分区：
文献类型：
--
作者：
Karl S;Dandekar T
布尔网络捕获许多自然发生的调节网络的切换行为。对于半定量建模，ON 和 OFF 状态之间的插值是必要的。细胞凋亡或增殖等细胞过程中布尔遗传调控网络 (GRN) 的高次多项式插值允许对比连续激活剂-抑制剂模型更广泛的节点相互作用进行建模，但对于包含超过 10 个输入的节点的网络会遇到扩展问题。来自文献或新基因表达实验的许多 GRN 超出了这些限制，因此开发了一种新方法。 (i) 作为我们新的 GRN 模拟框架 Jimena 的一部分，我们引入并设置了基于布尔树的数据结构； （ii）相应的算法大大加快了几乎所有情况下多项式插值的计算，从而扩大了该模型在合理时间内可以模拟的网络范围。 (iii) 使用二元决策图有效地计算和识别离散模型的稳定状态。作为应用示例，我们展示了如何在小型到大规模的激素疾病网络（拟南芥发育和免疫、病原体丁香假单胞菌以及细胞分裂素和植物激素的调节）中有效地采样系统状态。 Jimena 模拟当前可用的 GRN 的速度比以前的多项式插值模型的实现快约 10-100 倍，并且对于大型无标度网络可以获得更大的增益。这种加速还有助于对连续状态空间进行更彻底的采样，这可能导致新稳定状态的识别。大型网络的突变体可以非常快速地构建和分析，从而对网络的稳健性和行为产生新的见解。
Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.
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影响因子：
3
作者：
Krumsiek J;Pölsterl S;Wittmann DM;Theis FJ
通讯作者：
Theis FJ
影响因子：
--
作者：
Albert, Istvan;Thakar, Juilee;Li, Song;Zhang, Ranran;Albert, Reka
通讯作者：
Albert, Reka
影响因子：
4.9
作者：
Koczan D;Drynda S;Hecker M;Drynda A;Guthke R;Kekow J;Thiesen HJ
通讯作者：
Thiesen HJ
影响因子：
11.6
作者：
Naseem, Muhammad;Philippi, Nicole;Dandekar, Thomas
通讯作者：
Dandekar, Thomas
影响因子：
11.6
作者：
Espinosa-soto, C;Padilla-Longoria, P;Alvarez-Buylla, ER
通讯作者：
Alvarez-Buylla, ER