CDI Type II: Collaborative Research: Understanding Complex Biological Networks: A Process Viewpoint

本课题针对复杂网络中最流行的模型之一--布尔模型，提出了一种基于过程视点的新方法。与离散动力系统的标准吸引子-盆地画像不同，过程观点从单个状态序列开始，并解决可能产生该序列的网络类型。状态序列是一个过程，用生物学的术语来说，它对应于一个时间进程，通常是真实系统唯一可用的动力学数据。研究问题的类型包括：什么网络产生了给定的生物过程？这些网络有何不同？它们有什么共同之处？本项目还将考虑进程的空间以及由描述空间特征而产生的问题的类型：某些进程是否更难建立网络？某些进程是否需要与其他进程不同的网络属性？所使用的方法是基于数理逻辑的，并得到了描述给定过程的网络空间的单一表达式。本研究项目的重要性有三个方面。首先，这项研究有可能增加我们对复杂生物网络的理解，其功能是生命系统的基础。其次，这种方法可能会产生一种实用的算法，用于从当今可用的数据类型推断网络结构；这一点很重要，因为用当前技术很难推断出底层的网络结构。精确的网络结构和动力学对于了解细胞内的特定反应是如何工作的至关重要。最后，该项目将有助于增加我们对复杂系统的总体理解。生物复杂性只是一种复杂性；只要我们在构建计算工具来帮助理解这种类型的复杂性方面取得进展，类似的工具很可能有助于其他类型的复杂性。当然，这已被证明是其他复杂性工具的情况，如小世界网络、无标度图和相变。

This project focuses on one of the most popular models of complex networks, Boolean models, and proposes a new approach based on a process-viewpoint. In contrast to the standard attractor-basin portrait of a discrete dynamical system, the process-viewpoint starts with a single sequence of states and addresses the types of networks that might produce that sequence. The sequence of states, a process, corresponds to a time-course in biological terms and is often the only dynamical data available for real systems. The types of research questions include: what networks produce a given biological process? How do those networks differ and what do they have in common? This project will also consider the space of processes and the types of questions that arise from characterizing the space: Are some processes harder to build networks for? Do some processes need different network properties than others? The approach used is based on mathematical logic and results in a single expression characterizing the space of networks for a given process.The importance of this research project is threefold. First, the research has the potential of increasing our understanding of complex biological networks, whose functioning is the basis of living systems. Second, the approach might result in a practical algorithm for inferring network structure from the types of data available today; this is significant because the underlying network structure is very difficult to infer with current technologies. The precise network structure and dynamics is essential to understanding how particular reactions within the cell work. Finally, the project will help increase our understanding of complex systems in general. Biocomplexity is merely one type of complexity; to the extent we make headway in building computational tools to help understanding this type of complexity, similar tools are likely to help with other types of complexity. Certainly, this has proved to be the case for other complexity-tools such as small-world networks, scale-free graphs and phase transitions.