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

细胞感知并响应外部和内部的扰动，并必须做出决定来响应这些刺激。理解细胞如何处理导致表型反应的生化信号的挑战主要是由于复杂的多维生化数据能够识别分子相互作用和表型之间的关系。因此，了解细胞决策过程具有广泛的适用性，以解决生物学中的基础和应用问题。该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表型的细胞信号转换，以便从动态程序驱动的模型中提取有用的机制见解，这些模型将用于指导实验。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）提供了跨学科的培训机会，他们将通过整合物理，化学，统计，数学和计算机科学方法进行跨学科研究，以组成细胞信号传导过程的计算模型。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，有可能进一步应用于其他应用，如气候建模，通信和经济学。细胞不断感知和响应外部和内部扰动，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，“组学”技术的出现，结合统计方法，已经产生了基因相互作用，蛋白质表达和表型之间的相关理解。然而，目前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理解。这种将基因型和表型相关联的基本知识差距主要是由于对细胞重编程、合成生物学应用和微生物生物工程等应用所需的复杂生物化学信号网络的管理和分析。该项目将使理论和计算工具的开发和实施，基于严格的物理，化学，数学和统计方法，以了解细胞如何致力于程序性细胞死亡（即凋亡）。该奖项由分子和细胞生物科学部的细胞动力学和功能群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.