Gutiérrez Jayson, St Laurent Georges, Urcuqui-Inchima Silvio
Grupo de Física y Astrofísica Computacional (FACom), Instituto de Física, Universidad de Antioquia, Medellin, Colombia.
Theor Biol Med Model. 2010 Mar 15;7:7. doi: 10.1186/1742-4682-7-7.
Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors.
In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lipopolysaccharide (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology.
Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes.
Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.
信号转导网络代表信息处理系统，它决定了在特定情况下细胞能够进入哪些生化活动的动态模式。分子系统生物学的主要关注点之一是阐明信号转导网络的稳健性特性和信息处理能力。要实现这一目标，需要在生化反应系统的设计原理与其涌现的动态行为之间建立因果关系。
在本研究中，重点构建了一个信息相对丰富的确定性非线性动态模型，该模型基于标准质量作用定律和希尔饱和动力学，考虑了Toll样受体4（TLR4）介导的信号事件背后的典型反应拓扑结构的反应机制。这种信号传导机制已被证明在巨噬细胞中响应脂多糖（LPS）刺激的相对较短时间窗口内被激活，从而引发快速启动的先天免疫反应。通过局部和全局扰动策略，对该信号转导网络所占据的生化反应空间进行了广泛的计算探索。重要的是，通过计算重建了该网络在参数空间广泛分散区域内可进入的广泛的生物学上合理的动态模式。此外，还模拟了实验报道的目标促炎基因的转录读数，这些基因在网络响应LPS刺激时被积极调控。这样做的主要目的是对这种典型反应拓扑结构的内在稳健性特性进行无偏统计评估。
我们的模拟结果提供了令人信服的数值证据，支持TLR4信号网络的典型反应机制能够以稳健的方式进行信息处理这一观点，这种功能特性独立于需要执行的信号任务。然而，发现网络的稳健性能不仅仅由其设计原理（拓扑结构）决定，还可能严重依赖于网络在生化反应空间中的当前位置。最终，我们的结果使我们能够识别出在不同动态模式下最有效地控制系统性能的关键限速步骤。
总体而言，我们的计算机模拟研究表明，只有考虑到系统可达到的广泛动态模式，才能阐明复杂生物分子网络一般行为中生物学相关且非直观的方面。最重要的是，当系统地探索其参数空间时，这种策略为适当评估系统结构所施加的固有变化约束提供了手段。
