Reverse Engineering of Direct versus Indirect Effects in Biological Pathways

描述（由申请人提供）：多组分生物网络执行多种功能，从细胞分裂到环境适应。它们的结构是不完全理解的，在很大程度上是由于缺乏可靠和强大的网络逆向工程和表征方法。我们相信，工程学和生物学的融合可以导致范式转变的理论和实验进展，这将彻底改变我们理解生物系统复杂性的能力，通过对它们的网络的基本见解。 基于初步的实验，我们形成的假设，一系列的小规模的合成网络，模拟互连和拓扑经常遇到的细胞，可以用来开发和验证新的逆向工程工具和理论。我们的长期目标是为多靶点治疗的设计开发一个框架。提出的目标将使我们更接近这一目标，提供第一代强大而可靠的逆向工程算法，使我们能够阐明细胞中的直接与间接调控。 特别是，我们的目标是构建一套小规模的网络，将稳定地整合在哺乳动物细胞。随后，这些网络的各个节点将从其稳定状态受到弱扰动。扰动前和扰动后的稳定状态将被测量并输入到逆向工程算法中以预测网络结构。算法的结果将与已知的连通性进行比较，并将用于调整算法的参数，更一般地说，用于调整实验。这些参数包括扰动的大小、数据收集和处理技术以及计算处理的细节。 开发自动化和严格验证的方法来解开人类细胞中双分子网络的复杂性是生命科学家和工程师面临的主要挑战之一。我们的研究议程提出了一个创新的实验平台，以改变科学界应对这一挑战的方式，我们相信它有可能极大地影响基础生物学研究。我们将生成一个集成在人类细胞中的双分子网络的集合，免费提供给广大的科学界，从而可用于广泛的研究。利用这些细胞，我们将创造新的方法，逆向工程和生物网络的表征，结合新开发的实验技术和开发理论工具来解释数据。研究结果将用于确定生物系统的一般原则和规律，特别是侧重于描绘网络的属性和区分直接与间接影响。

DESCRIPTION (provided by applicant): Multi-component biological networks perform diverse functions, ranging from cell division to environmental adaptation. Their structures are understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. We believe that the integration of engineering and biology can lead to paradigm shifting theoretical and experimental advances that will revolutionize our ability to understanding complexity in biological systems, via deriving fundamental insights on their networks. Based on preliminary experiments, we form the hypothesis that a range of small-scale synthetic networks, that emulate interconnections and topologies frequently encountered in cells, can be utilized to develop and validate novel reverse engineering tools and theory. Our long-term objective is to develop a framework for the design of multi-target therapeutics. The proposed aim will bring us considerably closer to this objective, providing a first generation of robust and reliable reverse engineering algorithms that will allow us to shed light in direct versus indirect regulation in cells. In particular, we aim to construct a set of small scale networks that will be stably integrated in mammalian cells. Subsequently, the individual nodes of these networks will be weakly perturbed from their steady state. The pre- and post-perturbation steady states will be measured and fed into reverse engineering algorithms to predict the network structure. The results of the algorithm will be compared against the known connectivities, and will be used to adjust the parameters of the algorithm and more generally the experiment. These parameters include the magnitude of the perturbations, the data collection and processing techniques, as well as the details of computational processing. Developing automated and rigorously validated methodologies for unraveling the complexity of bimolecular networks in human cells is one of the central challenges to life scientists and engineers. Our research agenda proposes an innovative experimental platform to transform the way in which this challenge is addressed by the scientific community, and we believe it has the potential to greatly influence basic biological research. We will generate a collection of bimolecular networks integrated in human cells freely available to the broad scientific community, thus available for a wide spectrum of studies. Using these cells we will create novel methods for reverse engineering and characterization of biological networks incorporating newly- developed experimental techniques and developing theoretical tools for interpreting the data. The results will be used towards identifying general principles and laws of biological systems, in particular focusing on delineating the properties of networks and distinguishing direct versus indirect effects.