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Reconstruction and Modeling of Dynamical Molecular Networks

动态分子网络的重建和建模

基本信息

批准号：
10189695
负责人：
Shankar Subramaniam
金额：
$ 33.76万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-06 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10189695
关键词：
Algorithms Behavior Binding Biological Biology Biomedical Research Cell Cycle Cell Cycle Progression Cells Collaborations Communities Data Detection Development Dimensions Disease Embryo Etiology Evolution Fibroblasts Functional disorder Generations Genetic Genetic Transcription Grain Human In Vitro Knowledge Lead Least-Squares Analysis Letters Linear Models Malignant Neoplasms Mammalian Cell Measurement Methodology Methods Modeling Modernization Molecular Mus Nature Neural Network Simulation Neurodegenerative Disorders Neurons Pathway interactions Pharmacology Phenotype Physics Pluripotent Stem Cells Property Proteins Pythons Research Series Space Models Statistical Methods System Testing Therapeutic Intervention Time Tissues Validation algorithmic methodologies base biological systems cancer therapy computer framework data reduction experimental study gene therapy human pluripotent stem cell induced pluripotent stem cell insight mathematical model molecular modeling network models neuron development predictive modeling public repository reconstruction response simulation targeted treatment tool web based interface web site

项目摘要

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.

动态分子网络的重构与建模生物网络及其定量模型可以提供对疾病病理生理学的机制性见解，疾病以及确定治疗干预的潜在目标。定量模型可用于通过模拟关键分子的扰动生成假设，并进行实验验证通过药理学或遗传学的干扰。该项目涉及开发和实施因果推理、分子和模块网络分析和建模的算法和方法从包含与生物途径相关的先验知识的大规模时间分子数据，功能协调发展的生物系统的动力学和非线性性质将通过连续的线性通过识别时程数据中的不同时间机制来建立模型。将确定时间制度通过变化点检测算法。变化点可能反映了生物系统。然后，一个稳定的最小绝对收缩和选择算子的方法，最小二乘法将用于推断潜在的因果关系网络，并为特定途径建立模型。我们将在我们的状态空间建模方法中加入时间延迟，以确定过去的数据是否有助于对当前值的预测有很大影响。由于大（因果）分子的推断和解释从具有数千个组件/分子的整个系统级别的时间数据构建网络是令人望而却步的具有挑战性，将确定与各种生物途径，机制和功能相对应的模块通过整合定量时间数据和先验生物学知识。枢纽分子或质心的模块将作为降维状态空间中的状态变量，它们将用于推断网络和开发状态空间模型。不同制度下网络的时间演变将通过对网络进行定性和定量比较进行严格分析。的模块化网络也将与相应的粗粒度版本的详细分子网络作为内部验证。外部确认将包括与现有机制的比较模型，如果有的话。网络的预测模型将用于生成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设。虽然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，也就是，1)小鼠胚胎成纤维细胞的细胞周期进程，对分子机制研究具有重要意义 2）人类诱导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。统计工具，如R 和python将被用来实现算法和方法，所产生的包和教程将通过一个基于PHP的项目网站和公共知识库， GitHub和SourceForge。