分子信号传输是生物系统赖以协调工作的基础，深入理解高动态复杂场景下的分子信息传输机制，对于阐释深层生物机理、调控特定信号通路、实施精准医疗，具有重要的理论指导意义。本课题从信息科学角度出发，针对最基本的三种细胞通信模式，研究传导细胞-靶细胞点对点通信、基于信号传导网络的协同传输和多靶细胞协作信息接收等问题，以期解决复杂生物场景下的可靠信息检测理论难题。区别于已有信号处理方案，充分发掘复杂的生理模型与生物机理，设计无需信道状态信息的低复杂度、高效能信号处理方法，包括联合参数估计与相干信号检测、信号发射/接收模式优化、基于机器学习的非相干分集接收、传导网络高效信息采集和协同处理、分布式多信宿协作信号接收等。充分考虑分子信号传导过程中诸多非理想因素，从检测可靠性、容量最大化、计算复杂度等方面出发，探索新型仿生分子信号检测与信息提取理论，相关的非线性信号处理机制对未来无线传输技术设计具有启发作用。

Molecular signaling is the foundation of harmonization operation of various biological systems. Comprehensive understanding of reliable molecular signaling processing and information transferring in complex biological conditions is of theoretical significance to the explanation of related biological mechanisms, the control of specific signaling pathway as well as the implementation of precision medicine in future. From the perspective of information science, this proposal aims to study three essential ingredient signaling model in cell communications, and focus on the following three problems, i.e. point-to-point communication between transduction cell and target cell, the signal transduction network (STN) assisted cooperative transmissions, and the cooperative signal reception of multiple target cells. The main purpose of this research is to solve the problem of conveying molecular information reliably in complex and highly dynamical biological environments. In contrast to most existing signal processing concepts developed for wireless telecommunications, this research would be rooted on realistic physiological models and specific biological mechanisms, and design a class of low complexity whilst high accuracy signal detection methods that are practically independent of channel state information. Among these include joint parameter estimation and signal detection, optimization of cell transduction mode, machine-learning powered non-coherent diversity detection, and collaborative transmission with the help of STN, as well as the distributed cooperative signal reception among multiple target cells, and so on. We will take full consideration of non-ideal factors in cell signaling transductions, and explore the bio-inspired molecular signal processing theorem and information extraction methods from various quantitative angles, such as detection reliability, capacity maximization and implementation complexities. Meanwhile, the involved nonlinear signaling processing mechanisms are also useful to enlighten the design of new advanced signal processing methods for future wireless transmission technologies.