Reconstruction and Modeling of Dynamical Molecular Networks

动态分子网络的重构与建模：摘要 生物网络和它们的定量模型可以提供对疾病病理生理学的机械性见解。 以及确定潜在的治疗干预目标。可以使用定量模型 通过模拟关键分子的扰动来产生假说，并进行了实验验证 通过药理或遗传方面的干扰。该项目涉及的是开发和实施 用于分子和模块化网络的因果推理、分析和建模的算法和方法 从结合了与生物途径相关的先验知识的大规模时间分子数据 功能。生物系统的动力学和非线性本质将通过连续的线性 通过在时间进程数据中识别不同的时间区域来建立模型。时间上的政权将被确定 通过变点检测算法。变化点潜在地反映了 生物系统。然后，提出了一种稳定的最小绝对收缩和选择算子方法 最小二乘法将被用来推断潜在的因果网络，并为特定的路径开发模型。我们 将在我们的状态空间建模方法中加入时间延迟，以确定来自过去的数据是否对 对现值的预测意义重大。由于大(因果)分子的推论和解释 从整个系统级别的时间数据中获取数以千计的组件/分子的网络令人望而却步 挑战，将识别与各种生物途径、机制和功能相对应的模块 通过集成量化的时间数据和先验的生物知识。中心分子或质心 将作为降维状态空间中的状态变量，它们将被用来推断 网络和开发状态空间模型。跨不同政权的网络的时间演变 将通过对网络进行定性和定量比较来进行严格的分析。这个 还将模块化网络与相应的粗粒度版本进行了详细的比较 分子网络作为内部验证。外部验证将包括与现有机制的比较 型号(如果有的话)。网络的预测模型将被用来产生实验可测试的 关于关键蛋白质的时间特异性药理学扰动的假说。而这些 方法学将适用于许多生物系统，在本项目中，它们将应用于两个系统， 即，1)小鼠胚胎成纤维细胞的细胞周期进程，这对分子机制的研究很重要 以及2)将人类诱导的多能干细胞分化为神经元，这对研究 神经退行性疾病。这些方法也将应用于模拟数据。R等统计工具 而Python将用于实现算法和方法，生成的包和教程将 可通过基于PHP的项目网站和公共资源库(如 作为GitHub和SourceForge。

Reconstruction and Modeling of Dynamical Molecular Networks: Abstract Biological networks and their quantitative models can provide mechanistic insights into pathophysiology of diseases as well as identify potential targets for therapeutic intervention. The quantitative models can be used for hypotheses generation through simulation of perturbations of key molecules and tested experimentally through pharmacological or genetic perturbations. This project deals with the development and implementation of algorithms and methodologies for causal inference, analysis and modeling of molecular and modular networks from large-scale temporal molecular data incorporating a priori knowledge related to biological pathways and functions. The dynamical and nonlinear nature of biological systems will be captured through successive linear models by identifying different temporal regimes in the time-course data. The temporal regimes will be identified through a change-point detection algorithm. The change-points potentially reflect mechanistic changes in the biological system. Then, a stable least absolute shrinkage and selection operator approach incorporating partial least squares will be used to infer the potentially causal networks and develop models for specific pathways. We will incorporate time-delay in our state-space modeling approach to identify if the data from the past contributes significantly to prediction of the current value. Since both inference and interpretation of large (causal) molecular networks from temporal data at the whole-systems level with thousands of components/molecules is prohibitively challenging, modules corresponding to various biological pathways, mechanisms and functions will be identified by integrating the quantitative temporal data and a priori biological knowledge. The hub-molecules or centroids of the modules will serve as state-variables in a reduced-dimensional state-space and they will be used to infer the networks and develop state-space models. The temporal evolution of the networks across various regimes will be rigorously analyzed by performing both qualitative and quantitative comparisons of the networks. The modular networks will also be compared with the corresponding coarse-grained versions of the detailed molecular networks as internal validation. External validations will include comparison with existing mechanistic models, if any. The predictive models of the networks will be used to generate experimentally testable hypotheses regarding temporally specific pharmacological perturbations of key proteins. While these methodologies will be applicable for many biological systems, in this project they will be applied to two systems, viz., 1) cell-cycle progression in mouse embryonic fibroblasts, important for the study of molecular mechanisms of cancer, and 2) differentiation of human induced pluripotent stem cells into neurons, important for the study of neurodegenerative diseases. The methods will be applied to simulated data as well. Statistical tools such as R and python will be used to implement the algorithms and methods and the resulting packages and tutorials will be made available to the research community through a PHP-based project website and public repositories such as GitHub and SourceForge.