MBioTS项目和导师

生活在自然界的物种经历着复杂的、空间结构的环境. 细菌在一小块土壤中生长和移动, 在高山云雾林中生活和互动的大猩猩, organisms living in structured environments interact more often with other individuals that are nearby and may experience a range of different conditions depending on their specific location within their habitat. In this project, the biological members will use microbial experiments to quantify the effects of spatial structure and heterogeneity on ecological and evolutionary dynamics. Data from experiments will be utilized by the mathematical members to both formulate and calibrate mathematical models of bacterial movement. Evolutionary changes will be simulated by manipulating growth rates and diffusion coefficients in a time dependent manner, 目的是将实验结果与模拟环境中的结果进行比较. 拟建微生物实验装置, 伴随着模拟, 将用于深入了解环境异质性对入侵动力学的影响.

Cell migration plays a critical role in a number of biological processes such as embryonic development, wound healing, 免疫反应. 细胞运动的改变也可能是病理条件的特征, for example, 癌细胞侵入健康组织. 运动是由细胞的性质和细胞外环境共同决定的. Traditional assays struggle with evaluating cell phenotypes over time and correlating time-dependent responses with changes in the extracellular environment. An alternate approach is to track the dynamics of live cells through time-lapse imaging and utilize knowledge based data-driven modeling to understand the individual and collective behavior of cells at high spatio-temporal precision.   Biological members will be involved in image acquisition and analysis of time-lapse images of cultured cells.  Mathematical members will utilize both mechanistic principles and individual cell data to discover interaction rules in many-particle biological systems that depend on both local interactions and environmental factors.  A main goal will be to use both mathematical modeling and learning techniques to understand fundamental scientific principles by which cells interact, 这种相互作用如何取决于当地的微环境.

重复模式, 比如毛囊和刷毛, 是否对感知环境的上皮细胞很重要. 优化组织有助于正常的组织功能, 并且可以给动物空间映射的环境刺激输入. Although many local signaling mechanisms that drive the formation of repeating patterns are understood, 这些局部信号如何升级到组织范围的模式仍然不清楚. A genetically tractable system for the study of repeating patterns are the sensory bristles in the fruit fly Drosophila melanogaster. In this project, biological members will work to generate in vivo confocal imaging data of the developing spot pattern, 以及在胸背产生表达RNAi靶向修饰物的蛹. 学生将使用可见标记按基因型对蛹进行分类, 年龄到适当的阶段, 并准备样品用于共聚焦显微镜的实时成像. Mathematical members will be responsible for implementing clustering algorithms to quantify bristle pattern data and detect pattern differences. 重复斑点模式将基于三种空间分布度量来表征, 相对感觉区, 相对簇距离和相对簇大小. 通过结合这些措施来定义一个感官模式评分, the team member will test the efficacy of this sensory pattern score using data on fully developed sensory bristle patterns from the fruit flies. These scores will give precise quantitative measures for comparison between fruit flies with different gene knockdowns throughout different stages of development, and allow for a better understanding of the processes and mechanisms driving repeated pattern formation in this system.

数学生物学研究的一个重要组成部分依赖于习得, analysis, 并将现有的公开数据整合到数学模型中. This project aims to introduce a team to utilizing publicly available data sets which would otherwise be impossible to collect during a 10-week program. Members will use open-source clinical pharmaceutical data to retrieve clinical data and employ both statistical and theoretical models to test hypotheses. Biological members will focus on understanding the details of the clinical data set and the approaches used to generate these data. A possible data set is published data from a clinical trial studying the efficacy of adjuvant chemotherapy in combination with a monoclonal antibody for resected non-small-cell lung cancer (NSCLC) patients. Here biological team members will obtain a detailed understanding of the mechanisms of action of the therapies, 因此，研究小组可能会对联合治疗提高疗效的原因进行假设. The biological team will also be responsible for retrieving,processing, and analyzing the data set. The mathematical members will utilize the retrieved and processed data through information visualization. 定性模式将指导模型的构建, and quantitative data will be used to identify model-specific parameters and possibly their distributions. The team will employ validated models to both evaluate previous as well as propose novel hypotheses; in the NSCLC case, we may hypothesize mechanisms as to the reason bevacizumab is ineffective when applied in early-stage disease, 但在晚期非小细胞肺癌的无病进展方面改善了预后.