|
[监管网络非线性状态空间模型中的参数和隐藏变量估计
Estimating Parameters and Hidden Variables in a Non-linear State-space Model of Regulatory Networks |
|
| |
| 课程网址: |
http://videolectures.net/pesb07_quach_epa/
|
| 主讲教师: |
Minh Quach
|
| 开课单位: |
埃松本科
|
| 开课时间: |
2007-04-04
|
| 课程语种: |
英语
|
| 中文简介: |
理解和识别细胞中工作的生物复杂系统需要开发能够捕捉生物过程及其动力学的随机性质的模型。聚焦于基因调控网络，我们提出了一个动态贝叶斯网络形式的新的定量模型，该模型允许在同一框架中表示基因和蛋白质。我们从Michaelis-Menten的非线性微分方程开始，这是表示生化相互作用的黄金标准，并根据这些方程建立了离散时间和概率模型。与Nachman等人[1]等先前的研究相比，我们的模型考虑了调节蛋白和编码它们的基因之间的依赖性，以及蛋白质-蛋白质相互作用和蛋白质降解。在由此产生的非线性动力学系统中，当观察到基因表达时，蛋白质浓度被隐藏。为了学习模型的参数，我们首先构建与我们的连续时间-状态空间模型相对应的离散时间概率模型，然后基于无迹变换[2]导出卡尔曼平滑算法，以递归地估计参数和未观察到的蛋白质活性。如果生物学需要，学习方法的通用性为模型的各种适应性打开了大门。
给出了阻遏器[3]和其他几个小网络的参数和状态估计的数值结果，并表明了模型的相关性。
|
| 课程简介: |
Understanding and identifying biological complex systems at work in the cell requires to develop models able to capture the stochastic nature of biological processes as well as their dynamics. Focusing on gene regulatory networks, we propose a new quantitative model in the form of a dynamical Bayesian network that allows to represent both genes and proteins in the same framework. We start from the nonlinear differential equations of Michaelis-Menten which are the gold-standard to represent biochemical interactions and develop a discrete-time and probabilistic model from these equations. Compared to previous works such as Nachman et al [1], our model takes into account the dependency between the regulatory proteins and the genes that code for them as well as protein-protein interactions and protein degradations. In the resulting nonlinear dynamical system, the proteins concentrations are hidden while gene expressions are observed. In order to learn the model's parameters, we first construct a discrete-time probabilistic model corresponding to our continuous-time state-space model and then derive a Kalman smoother algorithm based on the unscented transformation [2] to recursively estimate the parameters and unobserved protein activities. The generality of the learning method opens the door to various adaptations of the model if required by the biology.
Numerical results on parameter and state estimation for the repressilator [3] and other several small networks are presented and show the relevance of the model.
|
| 关 键 词: |
基因调控网络; 定量模型; 学习方法
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2023-03-04：chenjy
|
| 最后编审: |
2023-05-11：chenjy
|
| 阅读次数: |
30
|
|
图 书 馆