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

这个项目的重点是复杂网络的最流行的模型之一，布尔模型，并提出了一个新的方法的基础上的过程的观点。与离散动力系统的标准吸引子盆画像相反，过程观点从单个状态序列开始，并解决了可能产生该序列的网络类型。状态序列，一个过程，对应于生物学术语中的时间过程，并且通常是真实的系统唯一可用的动态数据。研究问题的类型包括：什么样的网络产生一个给定的生物过程？这些网络有什么不同，它们有什么共同点？这个项目还将考虑过程的空间和从描述空间中产生的问题类型：一些过程更难建立网络吗？某些进程需要的网络属性是否与其他进程不同？所使用的方法是基于数学逻辑和结果在一个单一的表达式表征空间的网络为一个给定的过程。首先，这项研究有可能增加我们对复杂生物网络的理解，这些网络的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中推断网络结构;这很重要，因为底层网络结构很难用当前技术推断。精确的网络结构和动力学对于理解细胞内的特定反应如何工作至关重要。最后，该项目将有助于增加我们对复杂系统的理解。生物复杂性只是复杂性的一种类型;在某种程度上，我们在构建计算工具以帮助理解这种类型的复杂性方面取得了进展，类似的工具可能有助于其他类型的复杂性。当然，这已经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.