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

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都有效的网络动态进行可证明的准确描述。计算结果被编码为可搜索的基因调控网络动态签名数据库(DSGRN)。该提案开发了数学理论和计算工具，以有效地计算和询问数据库中具有生物学意义的网络功能相关因素。 这种方法将被用来搜索网络空间，以寻找与两种类型的酵母(酿酒酵母和新型隐球菌)和人类的细胞周期动力学相关的实验观察到的动力学。在目标1中，DSG RN被用来在参数空间中寻找在人类细胞周期限制点网络中导致从癌症到良性表型的扰动。人类网络与酿酒酵母中类似网络的紧密同源性将被用于实验验证我们的发现。目的2利用DSGRN搜索网络空间的能力，以(A)寻找致死真菌感染的致病菌新生葡萄球菌的细胞周期网络，以及(B)寻找人类细胞周期网络中潜在的缺失环节。 由于基因组和细胞表型的复杂关系，对人类基因组和细胞网络的详细了解尚未导致医学上的巨大飞跃。这一差距在数学背景下表现为缺乏根据网络结构在所有参数下预测网络动态的方法。这项建议通过开发建模和计算方法来解决这一差距，该方法允许对对所有初始数据和参数有效的动力学进行可证明的准确描述。动力学的描述比标准模型的描述要粗糙。虽然DSG RN没有在单轨迹水平上的敏锐度，但可以查询数据库中的双稳态、重复的非平衡动力学、特定的稳态表达模式和与实验时间序列数据匹配的极值模式。动力学的粗略描述允许搜索数千个甚至可能数百万个网络以寻找所需的粗略动力学，并且如果需要，可以针对表现该动力学的网络和参数区域改进模型以获得更高的敏感度。

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.