在研课题 / Research projects:
1. 微生物次级代谢与“铁互作网络”生态学
微生物的生存与繁衍深刻影响着地球生态与人类健康。在复杂的群落中，微生物通过分泌次级代谢产物（如用于竞争铁元素的铁载体 Siderophore）来主动塑造微环境，并与其他菌株进行复杂的博弈。尽管测序技术极大丰富了我们对群落组成的认知，但如何建立从基因组序列到分子功能，再到宏观网络演化的跨尺度定量模型，仍是微生态学中的难点。
课题组以微生物的铁元素博弈为定量模型系统（Iron-Net），结合数据挖掘、演化分析与非线性动力学模型，致力于在以下三个层级构建定量生物学图像：
1. 从序列到化学产物的映射：
解析非核糖体肽合成酶（NRPS）等复杂组装线的合成逻辑，建立标准化的化学指纹数据库，从基因组“暗物质”中预测次级代谢分子的结构与功能；
2. 基于共进化信号的网络推断
利用铁载体与受体之间的分子协同演化规律，开发计算方法，直接从基因组数据重构大尺度、有向的微生物生态互作网络；
3. 微生态群落的自组织动力学
从理论物理与统计力学的视角，探索次级代谢物作为“公共物品”如何改变群落的资源维度，揭示“欺骗者”与合作者在演化博弈中的共存规律。
1. Microbial Secondary Metabolism and the "Iron-Net" Ecology
Microbes profoundly influence all aspects of the biosphere and human health. Within complex communities, microorganisms actively shape their microenvironments by secreting secondary metabolites (such as siderophores for iron scavenging) and engaging in intricate games of production, cooperation, and exploitation. Although sequencing techniques have mapped community compositions, building cross-scale quantitative models remains a core challenge in microbial ecology.
Using microbial iron competition as a quantitative model system (the "Iron-Net"), our group integrates data mining, evolutionary analysis, and non-linear dynamical modeling to build quantitative frameworks across three levels:
1. Sequence-to-Molecule Mapping：
Decoding the assembly logic of complex machineries like non-ribosomal peptide synthetases (NRPS), constructing standardized chemical fingerprint databases, and predicting the structure and function of secondary metabolites from genomic "dark matter."
2. Network Inference via Co-evolutionary Signals
Exploiting the molecular co-evolution between siderophores and their receptors to develop computational methods that directly reconstruct large-scale, directed microbial ecological networks from genomic data.
3. Self-organized Dynamics of Microbial Communities
Approaching microbial interactions from the perspective of theoretical physics and statistical mechanics. We explore how secondary metabolites, acting as public goods, alter the resource dimensionality of communities and reveal the coexistence rules between "cheaters" and cooperators in evolutionary games.
Selected Publications in this area:
1. Shaohua Gu, Jiqi Shao, Ruolin He, Guanyue Xiong, Zeyang Qu, Yuanzhe Shao, Linlong Yu, Di Zhang, Fanhao Wang, Ruichen Xu, Peng Guo, Ningbo Xi, Yinxiang Li, Yanzhao Wu, Zhong Wei, 𝐙𝐡𝐢𝐲𝐮𝐚𝐧 𝐋𝐢*. Forging the iron-net: Towards a quantitative understanding of microbial communities via siderophore-mediated interactions. Quantitative Biology, 13(2):e84 (2025). doi: https://doi.org/10.1002/qub2.84.
2. Shaohua Gu, Zhengying Shao, Zeyang Qu, Shenyue Zhu, Yuanzhe Shao, Di Zhang, Richard Allen, Ruolin He, Jiqi Shao, Guanyue Xiong, Alexandre Jousset, Ville-Petri Friman, Zhong Wei*, Rolf Kümmerli*, 𝐙𝐡𝐢𝐲𝐮𝐚𝐧 𝐋𝐢*. Siderophore synthetase-receptor gene coevolution reveals habitat- and pathogen-specific bacterial iron interaction networks. Science Advances, 11:eadq5038 (2025). doi: https://doi.org/10.1126/sciadv.adq5038.
3. Ruolin He#, Shaohua Gu#, Jiazheng Xu, Xuejian Li, Haoran Chen, Zhengying Shao, Fanhao Wang, Jiqi Shao, Wen-Bing Yin, Long Qian*, Zhong Wei*, 𝐙𝐡𝐢𝐲𝐮𝐚𝐧 𝐋𝐢*. SIDERITE: Unveiling hidden siderophore diversity in the chemical space through digital exploration. iMeta, 3(2): e192 (2024). doi: https://doi.org/10.1002/imt2.192.
2. 细胞命运决定与空间斑图的动力学规律
在胚胎发育、器官发生或生物被膜的形成过程中，具有相同基因组的细胞群能够自发地生成高度有序、在时空中规律排布的异质性结构，即模式形成（Pattern formation）。尽管单细胞测序等技术使得基因表达动态被高通量记录，但局部细胞如何通过非线性物理过程“协商”出宏大的空间秩序，其深层机制仍待系统性阐释。
课题组利用网络动力学结合组学数据，探索多重细胞命运与空间斑图自发生成的底层数学规律：
1. 单一细胞的多重命运与基因调控网络：
细胞命运受转录因子网络调控，可被视为非线性动力系统中的“吸引子”。我们致力于建立数学模型，理解并预测高维转录调控网络如何引导细胞在几十种不同命运间的路径选择；
2. 多细胞生长系统中的斑图形成
探索在自单细胞起始的“生长-胞间通讯”体系中，定量化的“位置信息”如何独立于外界输入，仅通过胞间信号传递与胞内基因调控网络的耦合，实现系统复杂度的自发增长。
2. Dynamics of Cell Fate Decisions and Pattern Formation
During embryonic development, organogenesis, or biofilm formation, cells with identical genomes spontaneously establish highly organized, heterogeneous patterns in space and time. Despite multi-omics technologies providing high-resolution records of gene expression dynamics, the fundamental mechanisms by which localized cells "negotiate" grand spatial orders via non-linear physical processes remain to be systematically elucidated.
Our group utilizes network dynamics and omics data to explore the underlying mathematical laws governing multiple cell fates and spontaneous pattern formation:
1. Multiple Fates of a Single Cell and Gene Regulatory Networks：
Cell fate is governed by interconnected transcription factor networks, conceptually corresponding to "attractors" in non-linear dynamical systems. We focus on developing mathematical models to understand and predict how high-dimensional regulatory networks guide cells through dozens of distinct differentiation pathways.
2. Pattern Formation in Growing Multicellular Systems
We investigate how quantifiable "positional information" emerges spontaneously in growing cell populations， without external input. We model how the coupling of intercellular communication and intracellular gene regulatory networks drives the autonomous increase of system complexity.
Selected Publications in this area:
1. Xiaoyi Zhang, 𝐙𝐡𝐢𝐲𝐮𝐚𝐧 𝐋𝐢*, Lei Zhang*. Constructing a holistic map of cell fate decision by hyper solution landscape. Cell Systems, 17(4):101562 (2026). doi: https://doi.org/10.1016/j.cels.2026.101562.
2. Gang Xue, Xiaoyi Zhang, Wanqi Li, Lu Zhang, Zongxu Zhang, Xiaolin Zhou, Di Zhang, Lei Zhang*, 𝐙𝐡𝐢𝐲𝐮𝐚𝐧 𝐋𝐢*. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife, 12:RP88742 (2024). doi: https://doi.org/10.7554/eLife.88742.3.
3. Lu Zhang, Gang Xue, Xiaolin Zhou, Jiandong Huang*, 𝐙𝐡𝐢𝐲𝐮𝐚𝐧 𝐋𝐢*. A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems. PLoS Computational Biology, 20(6):e1011882 (2024). doi: https://doi.org/10.1371/journal.pcbi.1011882.