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Needle in a haystack: effective search for dynamics across the space of networks

大海捞针：有效搜索网络空间的动态

基本信息

批准号：
9460106
负责人：
Konstantin Mischaikow
金额：
$ 37.26万
依托单位：
RUTGERS, THE STATE UNIV OF N.J.
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2022-03-31
项目状态：
已结题

项目摘要

This proposal introduces a revolutionary new approach that allows provably accurate description of network dynamics that is valid for all initial data and parameters. The result of the computation is encoded as a searchable database of Dynamic Signatures of Gene Regulatory Networks (DSGRN). The proposal develops mathematical theory and computational tools to efficiently compute and interrogate the database for biologically important correlates of network function. This approach will be used to search space of networks for experimentally observed dynamics related to cell cycle dynamics in two types of yeast (S. cerevisiae and Cryptococcus neoformans), and humans. In Aim 1, DSG RN is used to find perturbations in parameter space that lead from a cancer to a benign phenotype in a human cell cycle restriction point network. Close homology of the human network with an analogous network in S. cerevisiae will be used to experimentally verify our findings. Aim 2 takes advantage of DSGRN ability to search space of networks to (a) find cell cycle network for C. neoformans, a causal agent of a deadly fungal infection, and (b) find potential missing links in human cell cycle networks. Detailed understanding of human genome and cellular networks has not yet led to a quantum leap in medicine due to complex relationship of the genome with cell phenotypes. This gap is manifested in mathematical context by the lack of methods that predict dynamics of a network, at all parameters, from its structure. This proposal addresses this gap by developing modeling and computational approach that allows provably accurate description of dynamics that is valid for all initial data and parameters. The description of the dynamics is coarser than the description by standard models. While DSG RN does not have the acuity at the level of single trajectories, the database can be queried for bistability, recurrent nonequilibrium dynamics, particular steady state expression patterns, and patterns of extrema that match experimental time series data. The coarse characterization of dynamics allows searching thousands and potentially millions of networks for the desired coarse dynamics, and, if needed, to refine the model to higher acuity for the networks and parameter regions that exhibit this dynamics.

该提案引入了一种革命性的新方法，允许对所有初始数据和参数有效的网络动态进行可证明的准确描述。计算的结果被编码为基因调控网络的动态签名（DSGRN）的可搜索数据库。该提案开发了数学理论和计算工具，以有效地计算和查询数据库中的生物学重要相关的网络功能。这种方法将被用来搜索网络空间的实验观察到的动态有关的细胞周期动力学在两种类型的酵母（S。酿酒酵母和新型隐球菌）和人。在目标1中，DSG RN用于在人类细胞周期限制点网络中找到从癌症到良性表型的参数空间中的扰动。人类网络与S.酿酒酵母将用于实验验证我们的发现。目的2利用DSGRN的网络空间搜索能力，（a）找到C.新生菌，致命真菌感染的病原体，和（B）发现人类细胞周期网络中潜在的缺失环节。由于基因组与细胞表型之间的复杂关系，对人类基因组和细胞网络的详细了解尚未导致医学的飞跃。这种差距在数学背景下表现为缺乏从其结构预测网络动态的方法。该提案通过开发建模和计算方法来解决这一差距，该方法允许对所有初始数据和参数有效的可证明准确的动态描述。动力学的描述比标准模型的描述更粗糙。虽然DSG RN没有在单一轨迹的水平上的敏锐度，但可以查询数据库的双稳态，经常性的非平衡动力学，特定的稳态表达模式，以及与实验时间序列数据相匹配的极值模式。动力学的粗略表征允许搜索数千个和可能数百万个网络以获得所需的粗略动力学，并且如果需要的话，可以将模型细化到表现出这种动力学的网络和参数区域的更高灵敏度。