本课题组致力于跨尺度解析复杂生物网络的自组织规律，特别关注微生物相互作用网络。面对海量的组学数据与错综复杂的生态博弈，我们以“微生物的次级代谢与铁争夺（Iron-Net）”为切入点，深度融合定量计算、统计物理与实验验证，重点关注以下三大研究方向：

1. 从序列到分子的化学空间挖掘： 解码次级代谢（如非核糖体肽合成酶 NRPS,PKS）的生物合成逻辑，跨越从基因到表型的映射鸿沟，预测并挖掘微生态中的化学暗物质；

2. 微生态互作网络的重构与演化： 以多种定量方法高精度重构大尺度的群落相互作用网络，并利用非线性动力学解析竞争、合作与“欺骗”的底层物理与生态法则；

3. 复杂生物系统的定量建模： 提炼普适性的数学理论，探究细胞命运决定、空间斑图生成及其他多细胞与生态复杂系统的拓扑结构与动力学特征。

Our laboratory is dedicated to decoding the complex interaction networks and self-organization of microbial communities across molecular, ecological, and evolutionary scales. Utilizing microbial secondary metabolism, specifically, the competition and cooperation for iron ("The Iron-Net"), as our quantitative anchor, we bridge data-driven computation, statistical physics, and experimental validation to explore:

1. Sequence-to-Molecule Mapping: Decoding the assembly logic of secondary metabolism (e.g., NRPS, PKS) to bridge the genotype-phenotype gap and discover chemical "dark matter" from genomic big data;

2. Microbial Ecology & Network Dynamics: Quantitatively uncover the physical and evolutionary laws governing competition, cooperation, and "cheating" in microbiomes;

3. Theoretical Modeling of Complex Systems: Applying non-linear dynamics to investigate cell fate decisions, spatial pattern formation, and the overarching design principles of broader biological and ecological networks.