在研课题 / Research projects:
1. 微生物从演化到群落互作中的定量科学
从陆地到海洋，从工业生产至人体菌群，微生物影响着人类生存的各个方面。尽管关于微生物群落的测序手段在近十年蓬勃发展，“微生物群落中的定量规律”这一学科仍处于探索的初期，很多问题尚待回答——例如，如何定量微生物塑造并适应其微环境境的过程；微生物群落的生态和演化是否和传统生态学存有本质不同；群落中的合作如何在代谢博弈中维持，等等。课题组专注于“微环境-微生物”的反馈回路，以数据挖掘和动力学模型为手段，力图在几个典型体系中建立从演化到细胞策略到群落生态的完整定量图像。
微生物以分泌次级代谢产物的方式主动塑造微环境，而非核糖体肽合成酶（non-ribosomal peptide synthetase，NPRS）是其中占据已知次代产物基因簇一半以上的模块化合成酶的代表。自青霉素起，多样的NRPS产物为人类药物学发展提供了素材，而其“模块-蛋白域”的流水线结构也激励研究者们对NRPS进行人工设计。然而，迄今为止，NRPS的人工设计仍囿于研究者的经验和直觉，而NRPS的产物预测的精确度也限于有限的物种。课题组已以NRPS中公认的保守序列片段为“锚点”，发展了从数据库中提取NRPS序列并对其进行标准化和数分析的完整流程。以此标准注释为基础，课题组正在从海洋到人体致病菌等多个微生物体系中探知演化模式和序列-功能的关联。
从动力学系统的角度，基于次级代谢产物的互作也为生态网络模型提供了新的维度——传统生态理论的竞争模型中，群落结构受限于系统的资源维度。然而，在诸如分泌铁载体的过程中，微生物主动地增加着微环境中的资源维度，造成有趣的生态和演化动态。我们正试图以数学模型来探索此类过程中微生物博弈和演化的普遍规律。
1. Quantitative microbial ecology
Microbes influence all elements of human well-being, from land to sea, from industrial production to human microbiomes. Although sequencing techniques have blossomed in the last decade, the field of "quantitative microbial ecology" is still in its infancy, with many problems remain unanswered: how do microorganisms shape and adapt to their niches? To what extend does microbial ecology differ from classical ecology? And in the cooperator-cheater games, how is community collaboration maintained? Using data mining and dynamical modeling as tools, our lab focuses on the "microenvironment-microbe" feedback, attempting to construct a comprehensive quantitative picture from evolution to metabolic strategy to community ecology in several model systems.
Microorganisms actively shape their niches with secondary metabolites. Non-ribosomal peptide synthetase (NPRS) is a class of modular synthetases that contribute to more than half of the known secondary metabolite gene clusters. Diverse NRPS products have provided inspirations for the development of human medications since penicillin, and its assembly-line structure has also encouraged researchers to reengineer novel NRPSs.
Our group has created comprehensive techniques for parsing and analyzing NRPS sequences using conserved motifs. Using this standardized architecture, we are investigating evolutionary patterns and sequence-function connections in a variety of microbial systems, spanning from sea microbes to human pathogenic bacteria.
2. 多细胞体系中模式生成的数学规律
在诸如胚胎发育、器官发生、微生物群落形成等多细胞相互作用的系统中，具有同样的基因组的细胞经由分裂和分化，自发地生成在多种细胞命运时空中规律排布的有序模式（pattern）。模式形成（pattern formation）是系统生物学自20世纪中叶以来就着重探索的领域。近年来，随着多组学技术的蓬勃发展与合成生物学的兴盛，细胞命运决定过程的基因表达动态以及相关转录因子的调控关系都得到了更加细致的刻画；然而，此领域中,细胞生成多重命运并在时空中自发地规律排布的深层机制尚待系统性的阐释: 究竟是什么提供了发育系统的复杂度？什么样的“命运编码”能从所有细胞都同质的基因网络和信号转导网络里稳定而规则地建立异质性？
此课题中，课题组以网络动力学模型结合组学数据，正在尝试阐释多重细胞命运和模式自发生成中的底层规律，为合成生物学提供蓝图。其中,具体项目包括：
2.1. 单一细胞的多重命运与基因调控网络的关联。细胞命运受相互作用的转录因子网络调控，可被认为对应着非线性系统中的吸引子。迄今为止，大部分非线性模型大多侧重于刻画两种不同的分化命运之间的选择，然而，分化发育往往和几十上百种不同的细胞命运相关联。如何从转录调控网络的层面理解和预测细胞在大于三种的多重命运中的选择，是我们课题组感兴趣的方向之一。
2.2. 多细胞生长系统中模式形成的理解和设计。胚胎发育和器官发生的过程中，多细胞体系的复杂度往往独立于外界输入自发增加。以网络分析与数学建模的方法，课题组正在探索自单细胞开始的“生长-胞间通讯”体系中，以“位置信息”定量的复杂度如何通过胞间通讯和胞内基因调控网络实现无外界输入的自发增长。
2. Collective cell fate decisions and pattern formation
In a multicellular development system, such as embryonic development, organogenesis, and microbial community formation, cells with identical genomes spontaneously establish highly organized patterns. Since the mid-twentieth century, systems biology has been focusing on pattern creation. Despite the rapid development of multi-omics technology in recent years, there are still many questions to be answered: what might be the "source of information" that offers complexity to developmental systems? What type of "fate encoding" can stably and consistently establish spatial patterns from identical gene-regulatory and cell-signaling networks?
Our lab is attempting to explore the fundamental laws in spontaneous pattern formation through statistical and mechanistic modeling, in the following two areas:
2.1. Mapping relationship between gene regulatory networks and multiple fates of a single cell. Cell destiny is controlled by a network of interacting transcription factors, which can be thought of as nonlinear attractors. Differentiation and development are frequently coupled with dozens or hundreds of distinct cell fates. One of our research groups' interests is in understanding and predicting the choice of cells in more than three potential fates at the transcriptional regulatory network level.
2.2. Investigating pattern formation in the multicellular development system. understanding and design The complexity of multicellular systems typically grow spontaneously throughout embryonic development and organogenesis, with little external input. The research group is investigating how the complexity quantified with "positional information" can be generated through intercellular communications and intracellular gene regulation networks, using network analysis and mathematical modeling methods.
3. 生物大数据挖掘的方法建设
随着各种测量手段的蓬勃发展，生命科学中正产生出海量的数据，而如何从数据中洞见机制性的底层规律也是我们感兴趣的基本问题。从多名合作者的具体实例中，我们试图探寻数据挖掘中的共通方法。
3. Exploratory data analysis towards big data in biology
With the rapid development of various biotechnologies, the life sciences are generating a vast amount of data. Gaining insight into the underlying mechanism from the data is a core concern of ours research. From the examples of multiple collaborators, we attempt to explore some common rules in data mining methedologoies.
4. 传染病动力学和对非药物干预的建模
传染病是对人类健康的重大威胁。基于疾病动力学以及进化动力学对疫情进行的建模和分析，能一定程度上反映疫情的运行规律，并对有效的防控措施做出预测。在新冠疫情爆发初期，我们开始与多名研究者合作，进行了针对于新冠的传染病动力学研究，特别关注于对诸如口罩佩戴的非药物干预的作用的探索。
4. Dynamical models of epidemics and non-pharmaceutical interventions.
Infectious diseases pose a significant danger to public health. Epidemic models based on transmission and evolutionary dynamics can, to a certain extent, offer predictions for appropriate preventative and control measurements. In the early days of the COVID-19 outbreak, we began collaborating with a number of researchers on epidemic models of COVID-19, with a focus on examining the role of non-pharmaceutical interventions such as public mask wearing.