细胞信息处理的网络进化原理研究

The complexity of organisms critically depends on its information processing capacity. On the level of individual cells, a multitude of external and internal cues are sensed and integrated through various signal transduction pathways that eventually interface the gene regulatory network to alter cellular state, metabolism or cellular fate. Compared to electronic circuits, cellular networks are distinctively non-orthogonal, in that transcription regulators and signaling proteins in major pathways form families within which paralogous proteins exhibit highly similar DNA sequence-binding specificities and enzymatic specificities, respectively. Yet, instead of generating significant cross-talk, paralogous proteins seem to have facilitated robust complex signal computations. To understand the balance between cross-talk noise and signaling capacity, I will take an evolutionary approach to study the expansion of cellular networks from a few inter-connected family funders through successive gene duplication and functional divergence events. Biophysically realistic cellular network models enable a probabilistic description of systems-level noise and signal processing capacity as the network grows and distributes its signal channels, which then combines with analysis of network dynamics to yield the evolutionary trajectories of cellular networks in terms of mutational robustness and phenotypic plasticity. Unveiling the evolutionary constraints and adaptive features of cellular networks shall provide guidance for the design of synthetic cellular circuits that are more efficient, compatible and evolutionarily stable within living organisms.

生物细胞网络通过整合信号转导通路、转录调控网络以及其他调控方式，将多种胞外或胞内的信号计算并输出为细胞蛋白量的变化，从而改变细胞命运或生理代谢模式。虽然信号转导或转录调控网络往往包含一系列化学性质极为相似的同源调控因子，但这种家族化的结构并不显著提高噪音，反而具有卓越的信息处理能力和进化稳定性。本项目将结合生物物理模型、网络动力学分析、和网络全局进化模型来研究细胞网络的信号处理复杂度和稳定性的进化路径，同时通过重构的NFkB信号转导通路实验以及生物信息学分析对模型做出评估。相关的理论研究将加深细胞信息处理网络的“设计”原理的理解，推进进化系统生物学的发展，使我们对伴随细胞网络复杂化而发生的复杂生命体进化有更深层次的了解，为合成生物学复杂细胞网络的设计提供理论依据、最终实现稳定可控的细胞智能改造。

生物网络通过整合信号转导通路、转录调控网络以及其他调控方式，将多种胞外或胞内的信号计算并输出为细胞蛋白量的变化，从而改变细胞命运或生理代谢模式。虽然信号转导或转录调控网络往往包含一系列化学性质极为相似的同源调控因子，但这种家族化的结构并不显著提高噪音，反而具有卓越的信息处理能力和进化稳定性。本项目以生物网络的信息熵为出发点，通过模型定量表征网络串扰对于网络噪音和计算复杂度的影响，结合信号蛋白家族进化模型，理解天然生物网络中串扰的分布；同时，在一个重构的NFkB信号转导通路中实验解析其计算复杂度。此外，项目进行了一系列扩展研究，包括建立了细胞生长状态影响其信号转导功能的模型，建立了一套细胞间通讯的分子系统和一套基于自组装的DNA信息存储系统等。本项目的理论研究将加深细胞信息处理网络的“设计”原理的理解，推进进化系统生物学的发展，使我们对伴随细胞网络复杂化而发生的复杂生命体进化有更深层次的了解，为合成生物学复杂细胞网络的设计提供理论依据、最终实现稳定可控的细胞智能改造。