Biophysical models of the effect of the physico-chemical environment on genetic networks

物理化学环境对遗传网络影响的生物物理模型

• 批准号: RGPIN-2020-04007
• 负责人: Charlebois, Daniel
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716766
• 关键词: Biophysical; models; effect; physico; chemical

A major challenge in biophysics is to understand how cells cope with stress in their environment. At present, we do not understand the genetic and molecular mechanisms underlying the cellular response to common stressors, including electromagnetic fields (EMFs) and fluctuating chemical conditions, and we lack the quantitative tools needed to study them. The main objective of this interdisciplinary research program is to close this knowledge gap by developing mathematical and yeast model systems, and by performing experiments on genetically engineered yeast cells under a variety of real-world conditions. This work aims to make fundamental advances in the fields of cellular/molecular biophysics and gene network biology, and engineer more robust gene networks for use in basic research and industrial applications in Canada. The theoretical component of this research program will develop biophysical models and computer simulation algorithms based in fundamental physics to predict the effects of EMFs and fluctuating chemical exposure on gene networks. Previously, this approach has been used to make important advances on understanding how gene networks are affected by temperature (Charlebois et al., PNAS, 2018). These multiscale biophysical models and spatial cell population simulation algorithms, which are currently lacking in the field, will guide laboratory experiments on engineered yeast cells. In turn, the data generated from these experiments will be used to develop and validate the mathematical/computational models. The experimental component of this research program will focus on building EMF apparatuses that emulate real-world conditions for living cells and engineered gene networks that mimic natural gene networks. Yeast is an ideal model organism as it has been extensively characterized, grows well in the laboratory, is responsive to genetic manipulation, and yeast biology is highly relevant to other organisms. These EMF apparatuses and engineered yeast strains will allow the function, dynamics, and evolution of gene networks in diverse extracellular conditions to be studied in a more quantitative and controlled manner than is presently possible. The proposed research program has two main goals. First, it aims to fundamentally advance our knowledge of how healthy, living cells respond at the genetic level to cope with stressful conditions using mathematical modeling and experiments on gene networks inside of cells in nonstandard, fluctuating environmental conditions. Second, it aims to provide an inclusive first-rate training opportunity for highly qualified personnel in biophysics. The main expected impacts of this research are the development of new biophysical theories, spatial cell population simulation algorithms, EMF apparatuses for cellular/molecular biophysics, robust engineered gene networks for use in basic research and industrial applications, and a deeper understanding of gene networks and evolution.

生物物理学中的一个主要挑战是了解细胞如何应对环境中的压力。目前，我们还不了解细胞对常见应激源(包括电磁场和波动的化学条件)做出反应的遗传和分子机制，也缺乏研究它们所需的定量工具。这一跨学科研究计划的主要目标是通过开发数学和酵母模型系统，以及在各种现实条件下对基因工程酵母细胞进行实验，来弥合这一知识差距。这项工作旨在在细胞/分子生物物理学和基因网络生物学领域取得根本性进展，并设计出更强大的基因网络，用于加拿大的基础研究和工业应用。 这项研究计划的理论部分将开发基于基础物理学的生物物理模型和计算机模拟算法，以预测电磁场和波动的化学暴露对基因网络的影响。此前，这一方法已被用来在理解基因网络如何受温度影响方面取得重要进展(Charlebois等人，PNAS，2018年)。这些目前该领域缺乏的多尺度生物物理模型和空间细胞种群模拟算法将指导工程酵母细胞的实验室实验。反过来，这些实验产生的数据将用于开发和验证数学/计算模型。 这项研究计划的实验部分将专注于为活细胞建造模拟真实世界条件的电动势设备，以及模拟自然基因网络的工程基因网络。酵母是一种理想的模式生物，因为它已经被广泛地表征，在实验室中生长良好，对基因操作反应灵敏，并且酵母生物学与其他生物高度相关。这些电动势仪和工程酵母菌株将允许以比目前可能的更定量和更可控的方式研究不同细胞外条件下基因网络的功能、动态和进化。 拟议的研究计划有两个主要目标。首先，它的目的是通过数学建模和在非标准、波动的环境条件下对细胞内基因网络的实验，从根本上提高我们对健康的活细胞如何在基因水平上应对应激条件的知识。第二，它旨在为高素质的生物物理学人才提供包容的一流培训机会。这项研究的主要预期影响是发展新的生物物理理论、空间细胞种群模拟算法、用于细胞/分子生物物理的电动势设备、用于基础研究和工业应用的强大的工程基因网络，以及对基因网络和进化的更深层次的理解。