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

点击翻译按钮获取中文摘要

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.