A model invalidation-based approach for elucidating biological signalling pathways, applied to the chemotaxis pathway in R. sphaeroides.
A model invalidation-based approach for elucidating biological signalling pathways, applied to the chemotaxis pathway in R. sphaeroides.
复制标题
DOI：
10.1186/1752-0509-3-105
复制
发表时间：
2009-10-31
影响因子：
--
通讯作者：
Papachristodoulou A
中科院分区：
文献类型：
--
作者：
Roberts MA;August E;Hamadeh A;Maini PK;McSharry PE;Armitage JP;Papachristodoulou A
开发用于理解信号通路的连接性的方法是生物学研究中的主要挑战。为此目的，通常根据实验观察结果开发数学模型，这也允许预测不同实验条件下的系统行为。然而，通常情况下，相同的实验数据可以由几个相互竞争的网络模型来表示。在本文中，我们开发了一种新的数学模型/实验设计周期，以帮助确定可能的网络连接，通过迭代无效模型对应的竞争信号通路。为了做到这一点，我们系统地设计了计算机实验，以最好地区分竞争信号通路的模型。该方法确定的输入和参数扰动，将最好地区分模型输出，对应于什么可以测量/观察实验。我们将我们的方法应用于细菌球形红细菌的趋化途径中的未知连接性。我们首先开发了几种R. sphaeroides趋化性对应于不同的信号网络，所有这些都是生物学上合理的。拟合这些模型中的参数，使得它们都同样良好地代表野生型数据。然后将这些模型与当前的突变数据进行比较，其中一些是无效的。为了区分其余的模型，我们使用控制系统理论的思想来有效地在计算机上确定一个输入配置文件，这将导致模型输出的最大差异。然而，当我们将此输入应用于模型时，我们发现它不足以进行计算机识别。因此，为了实现更好的区分，我们确定了初始条件（总蛋白浓度）的最佳变化以及输入曲线的最佳变化。然后在活细胞上进行设计的实验，并将所得数据用于使其余候选模型中的所有模型无效。我们成功地将我们的方法应用于R. sphaeroides和使用这种方法设计的实验的结果允许我们使所有提出的网络模型无效，但只有一个。我们提出的方法是通用的，可以应用于一系列其他生物网络。
Developing methods for understanding the connectivity of signalling pathways is a major challenge in biological research. For this purpose, mathematical models are routinely developed based on experimental observations, which also allow the prediction of the system behaviour under different experimental conditions. Often, however, the same experimental data can be represented by several competing network models. In this paper, we developed a novel mathematical model/experiment design cycle to help determine the probable network connectivity by iteratively invalidating models corresponding to competing signalling pathways. To do this, we systematically design experiments in silico that discriminate best between models of the competing signalling pathways. The method determines the inputs and parameter perturbations that will differentiate best between model outputs, corresponding to what can be measured/observed experimentally. We applied our method to the unknown connectivities in the chemotaxis pathway of the bacterium Rhodobacter sphaeroides. We first developed several models of R. sphaeroides chemotaxis corresponding to different signalling networks, all of which are biologically plausible. Parameters in these models were fitted so that they all represented wild type data equally well. The models were then compared to current mutant data and some were invalidated. To discriminate between the remaining models we used ideas from control systems theory to determine efficiently in silico an input profile that would result in the biggest difference in model outputs. However, when we applied this input to the models, we found it to be insufficient for discrimination in silico. Thus, to achieve better discrimination, we determined the best change in initial conditions (total protein concentrations) as well as the best change in the input profile. The designed experiments were then performed on live cells and the resulting data used to invalidate all but one of the remaining candidate models. We successfully applied our method to chemotaxis in R. sphaeroides and the results from the experiments designed using this methodology allowed us to invalidate all but one of the proposed network models. The methodology we present is general and can be applied to a range of other biological networks.
登录
查看更多内容
影响因子：
64.8
作者：
Alon, U;Surette, MG;Leibler, S
通讯作者：
Leibler, S
影响因子：
64.8
作者：
Barkai, N;Leibler, S
通讯作者：
Leibler, S
影响因子：
11.4
作者：
Shah, DSH;Porter, SL;Armitage, JP
通讯作者：
Armitage, JP
影响因子：
--
作者：
August, Elias;Papachristodoulou, Antonis
通讯作者：
Papachristodoulou, Antonis
影响因子：
4.2
作者：
Chen, BH;Asprey, SP
通讯作者：
Asprey, SP