图 书 馆
利用稳态数据和动力学进行网络推理Network inference using steady-state data and Goldbeter-Koshland kinetics |
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| 课程网址: | http://videolectures.net/mlsb2012_oates_inference/ |
| 主讲教师: | Chris J Oates |
| 开课单位: | 华威大学 |
| 开课时间: | 2012-10-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | **Motivation:** Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are gene- rally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous data are typically rooted in convenient statistical formulations which do not exploit chemical kinetics to guide inference.\\ **Results:** Here we present an approach to network inference for steady-state data that is rooted in nonlinear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters; both are simultaneously learned from data. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whilst in the latter results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.\\ **Availability:** MATLAB R2009b code used to produce these results is provided in the Supplemental Information. |
| 关 键 词: | 稳态数据; 线性描述; 统计公式 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：cxin |
| 阅读次数: | 41 |