we developed a mathematical dynamic network model. We hypothesized that different modes of 12年发表在上的文章mTORC2 regulation would result in distinguishable, dynamic network responses. With the mathematical model, we performed specific predictive dynamic simulations for alternative mechanisms of mTORC2 regulation, and then these were experimentally validated. Here, we report an insulin-mTOR network model integrating both mTORC1 and mTORC2. The model was parameterized with dynamic quantitative time course data and experimentally validated. Subsequently, we introduced in silico and experimental network perturbations to simulate and experimentally test alternative network structures connecting mTORC2 to upstream insulin signaling.
First, we selected regulation mechanisms with an important role in dynamic behavior, such as the activation of mTOR complexes by the presence of both amino acids and insulin, the pathways connecting these stimuli to the mTOR complexes, and the NFL from p70S6K to IRS. Second, we selected molecules and interactions that we could reliably measure.
amino acids plus insulin都是用的这两个刺激2016年的文章也是。
首先削减了可能的通路模型
Comparison between the simulated time courses of the general model (solid lines) and the experimental time courses (points, dotted error bars) within [0, 120] min
构建不同的模型然后看这些不同模型下的表达量的差异，simulated time courses 和experimental time courses 最接近的，就是最佳的拟合曲线。
CellDesigner 4.2 (97) was used to construct the model network topology拓扑学 in SBGN (59). COPASI 4.7.34 (98) was used for all deterministic simulations, parameter estimations, parameter scanning, and sensitivity analysis.
Parameterization of the network model：This procedure is summarized by the following steps:
(i) The initial values of the parameters that needed optimization were assigned by random generation.
(ii) The calibration was repeated until a set of parameters with consistent values was identified.
(iii) This set of parameters was fixed and the remaining free parameters were calibrated again by repeating the process. In phase 1 of the estimation of kinetic rate constants, we sought to identify isolated modules that could be calibrated independently within the network.如果 the experimental and simulated time courses matched well for all the analyzed mTOR network readouts那么就选择这个参数
根据不同的Hypothesis构建不同的模型然后看看哪个更符合。排除掉不合理的假设。
在不同的刺激下做时间梯度，计算表达量。After starvation, cells were stimulated with aa/insulin for 5, 30, and 60 min。
Here, we use dynamic modeling to discriminate among alternative network structures, in particular alternative modes of mTORC2 regulation.
