A Mechanistic Modeling Framework to Link Biochemical Network Signaling and Cell-Decision Processes

细胞感知并对外部和内部的干扰做出反应，并必须对这些刺激做出决定。理解细胞如何处理导致表型反应的生化信号的挑战在很大程度上是由于能够识别分子相互作用和表型之间关系的复杂的多维生化数据。因此，了解细胞决策过程对于解决生物学中的基本和应用问题具有广泛的适用性。这个项目将通过开发必要的工具来研究哺乳动物细胞中从增殖到程序化细胞死亡的细胞信号转换，方法是开发必要的工具，从将用于指导实验的动态程序驱动模型中提取有用的机制见解。该项目为本科生以及研究生和博士后助理(包括代表性不足的群体成员)提供了一个跨学科培训机会，他们将通过整合物理、化学、统计、数学和计算机科学方法来构建细胞信号过程的计算模型，从而进行跨学科研究。此外，作为该奖项的一部分开发的计算工具和方法将免费提供给研究界，具有进一步应用于其他应用的潜力，如气候建模、通信和经济学。细胞不断地感知和响应外部和内部扰动，这些扰动触发细胞内生化反应网络，导致细胞表型结果。在过去的几十年里，随着“组学”技术的出现，结合统计学方法，人们对基因相互作用、蛋白质表达和表型之间的相互关系有了更深入的了解。然而，目前还缺乏对生化相互作用网络动力学和细胞对给定结果的承诺的机械性理解。这种将基因型和表型联系起来的基本知识差距在很大程度上是由于管理和分析复杂的生化信号网络，这是细胞重新编程、合成生物学应用和微生物生物工程等应用所必需的。该项目将基于严格的物理、化学、数学和统计方法，开发和实施理论和计算工具，以了解细胞如何致力于细胞程序性死亡(即细胞凋亡)。挑战是了解增殖细胞如何处理外部信号，并将生化信号通路从生长转变为死亡。该奖项由分子和细胞生物科学部的细胞动力学和功能簇以及数学科学部的数学生物学项目共同支持。

Cells sense and respond to external and internal perturbations and must make decisions in response to these stimuli. The challenge to understand how cells process biochemical signals that lead to phenotypic responses is largely due to the complex multi-dimensional biochemical data able to identify the relationship between molecular interactions and phenotypes. Thus, understanding the cell-decision processes has wide applicability to address fundamental and applied problems in biology. This project will study the cellular signaling switch from proliferation to programed cell death phenotypes in mammalian cells by developing tools necessary to extract useful mechanistic insights from dynamic program-driven models that will be used to guide experiments. The project offers an interdisciplinary training opportunity for undergraduate students as well as graduate students and post-doctoral associates (including members of underrepresented groups) who will conduct interdisciplinary research by integrating physical, chemical, statistical, mathematical, and computer science methods to compose computational models of cell signaling processes. In addition, the computational tools and methods developed as part of this award will be made freely available to the research community, having the potential to be further employed in other applications, such as climate modeling, communication and economics.Cells constantly sense and respond to external and internal perturbations that trigger intracellular biochemical reaction networks leading to cellular phenotypic outcomes. The advent of 'omics' technologies in the last decades, in combination with statistical approaches, have yielded a correlative understanding between gene interactions, protein expression, and phenotypes. However, a mechanistic understanding of biochemical interaction network dynamics and cellular commitment to a given outcome is currently lacking. This fundamental knowledge gap to associate genotype and phenotype is largely due to the management and analysis of complex biochemical signaling networks that is needed for applications such as cell reprogramming, synthetic biology applications, and microorganism bioengineering. This project will enable the development and implementation of theoretical and computational tools, based on rigorous physical, chemical, mathematical, and statistical methods, to understand how cells commit to programmed cell death (i.e. apoptosis). The challenge is to understand how proliferating cells process external cues, and shift biochemical signaling pathways from growth to death.This award is supported jointly by the Cellular Dynamics and Function Cluster in the Division of Molecular and Cellular Biosciences and by the Program in Mathematical Biology in the Division of Mathematical Sciences.