Time/Space-Varying Networks of Molecular Interactions: A New Paradigm for Studyin

描述（由申请人提供）：系统生物学中的一个主要挑战是定量地理解和建模细胞网络的动态拓扑和功能特性，例如控制细胞行为的转录调控电路和信号转导通路的时空特异性和上下文依赖性重新布线。目前研究生物网络的努力主要集中在创建宏观属性的描述性分析。这种简单的分析提供了有限的见解非常复杂的功能和结构组织的生物系统，特别是在动态的背景下。此外，大多数现有的基于高通量数据重建分子网络的技术忽略了网络拓扑结构的动态方面，并将其表示为不变图。据我们所知，网络本身很少被认为是一个不断变化和发展的对象。在这项提案中，我们的目标是开发有原则的机器学习算法，从纵向或空间实验数据中对生物分子之间的时间和空间变化的相互作用进行逆向工程。我们的方法将考虑生物先验信息，如转录因子结合靶点，基因敲除实验，基因本体论，和PPI。与传统的共表达研究相反，我们的方法揭示了整个生物过程中潜在的重新布线网络。这将使发现和跟踪过程进展过程中的瞬时分子相互作用、模块和途径成为可能。我们还将开发贝叶斯形式主义来建模和推断“动态网络断层扫描”-确定每个分子的功能和与其他分子的关系的元状态，从而驱动网络拓扑结构的演变，可能是为了响应内部扰动或环境变化。使用这些新工具，我们将对乳腺癌进展/逆转的器官型模型的时间序列基因表达数据进行案例研究，以深入了解在此过程中驱动基因网络时间重新布线的机制。最后，我们还将提供一个软件平台，向公众提供本项目中开发的工具。到目前为止，还没有工作做考虑时间和空间变化的生物相互作用下一个统一的框架。我们提出的工作是对这一重要问题的初步尝试。我们提议的工作是在现行方法基础上向前迈出的重要一步。我们设想了一个新的范式，促进：1）统计推断和学习的基因网络，在空间和时间上不断演变，可能是响应于各种刺激，并可能介导基因组与环境的相互作用。2)深入探索驱动网络重新布线的潜在功能基础，动态轨迹和功能进化趋势。3)揭示动态系统中发生的瞬时事件，建立对基因调控，网络形成和进化机制的预测性理解。4)快速准确的计算算法，具有更强的统计保证，在大规模动态网络分析中具有更强的可扩展性和鲁棒性。5)面向公众的全方位便捷的动态网络分析软件包和用户界面。

DESCRIPTION (provided by applicant): A major challenge in systems biology is to quantitatively understand and model the dynamic topological and functional properties of cellular networks, such as the spatial-temporally specific and context-dependent rewiring of transcriptional regulatory circuitry and signal transduction pathways that control cell behavior. Current efforts to study biological networks have primarily focused on creating a descriptive analysis of macroscopic properties. Such simple analyses offer limited insights into the remarkably complex functional and structural organization of a biological system, especially in a dynamic context. Furthermore, most existing techniques for reconstructing molecular networks based on high-throughput data ignore the dynamic aspect of the network topology and represent it as an invariant graph. To our knowledge the network itself is rarely considered as an object that is changing and evolving. In this proposal, we aim to develop principled machine learning algorithms that reverse engineer the temporally and spatially varying interactions between biological molecules from longitudinal or spatial experimental data. Our approaches will take into account biological prior information such as transcriptional factor binding targets, gene knockout experiments, gene ontology, and PPI. Contrary to traditional co-expression studies, our methods unfold the rewiring networks underlying the entire span of the biological process. This will make it possible to discover and trace transient molecular interactions, modules, and pathways during the progression of the process. We will also develop a Bayesian formalism to model and infer the "dynamic network tomography" - the meta-states that determine each molecule's function and relationship to other molecules, thereby driving the evolution of the network topology, possibly in response to internal perturbations or environmental changes. Using these new tools, we will carry out a case study on time series gene expression data from organotypic models of breast cancer progression/reversal to gain insight into the mechanisms that drive the temporal rewiring of gene networks during this process. Finally we will also deliver a software platform offering the tools developed in this project to the public. So far, there has not been work done to consider temporally and spatially varying biological interactions under a unified framework. Our proposed work represents an initial foray into this important problem. Our proposed work represents a significant step forward over the current methodology. We envisage a new paradigm that facilitates: 1) Statistical inference and learning of gene networks that are evolving over space and time, possibly in response to various stimuli and possibly mediating genome-environmental interactions. 2) Thorough exploration of the underlying functional underpinnings that drive the network rewiring, dynamic trajectory, and trend of functional evolution. 3) Uncovering transient events taking place in the dynamic systems, building predictive understanding of the mechanisms of gene regulation, network formation, and evolution. 4) Fast and accurate computational algorithms, with stronger statistical guarantee and greater scalability and robustness in large-scale dynamic network analysis. 5) A full spectrum of convenient software packages and user interfaces for dynamic network analysis, available to the public.