Understanding physical constraints on cell biology through multiscale modeling

通过多尺度建模了解细胞生物学的物理限制

• 批准号: RGPIN-2021-02747
• 负责人: Stroberg, Tom
• 金额: $ 2.99万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760285
• 关键词: Understanding; physical; constraints; cell; biology

Research over the past several decades has made clear that spatial restriction and heterogeneity within cells is an integral components of biochemistry. Understanding and controlling biochemical processes necessarily requires understanding the physical constraints in which they take place. However, the crowded and confined cellular environment makes it difficult to translate measurements made outside of cells into a mechanistic understanding within cells. Similarly, measurements made directly in cells can be difficult to interpret due to the complexity of the cellular interior. Computational and mathematical modeling can meet this challenge by offering a bottom-up approach for connecting experimental observations to underlying physical processes. This program seeks to develop and apply computational models to an archetypal example of physically-constrained biochemistry: protein folding homeostasis in the endoplasmic reticulum (ER), provide valuable insight into how physical constraints within the ER affect its function. The ER is an intracellular organelle that consists of a dynamic network of narrow sheet-like and tubular structures. It is responsible for a broad range of functions including the synthesis, folding and transport of many proteins. Environmental perturbations can lead to a disruption of ER function, called ER stress, which is marked by misbalance between nascent peptides waiting to fold and the chaperones and enzymes needed to fold them. When this occurs, the ER initiates a broad transcriptional program called the unfolded protein response (UPR) to restore homeostasis. Failure of the UPR to mitigate ER stress leads to cell death and is implicated in many diseases such as diabetes, Alzheimer's disease and cancer. The UPR is controlled by three proteins in the ER membrane that detect ER stress through interactions with ER-resident proteins, and transmit stress signals to the nucleus. The objective of this program is to understand how these sensory proteins integrate various physico-chemical features of the ER into a coherent measure of ER stress. To address this objective, we will develop computational models at scales ranging from the atomistic description of proteins to organelle-scale modeling of ER morphology. These models will provide quantitative predictions of the how the ER shape, composition and contents affect the ER stress response, allowing observations made downstream of the ER to be translated into difficult-to-probe mechanisms controlling the stress response within the ER. Furthermore, the tools and methods developed for connecting ER morphology and crowding to stress sensor activity will be widely applicable to processes in other subcellular structures. Lastly, since this research program sits between computational science and cell biology, trainees will be equipped with highly sought-after interdisciplinary skillsets, positioning them at the forefront of computational biology.

过去几十年的研究表明，细胞内的空间限制和异质性是生物化学的组成部分。理解和控制生物化学过程必然需要理解它们发生的物理约束。然而，拥挤和受限的细胞环境使得难以将细胞外的测量转化为细胞内的机械理解。类似地，由于细胞内部的复杂性，直接在细胞中进行的测量可能难以解释。计算和数学建模可以通过提供一种自下而上的方法来将实验观察与底层物理过程联系起来，从而应对这一挑战。该计划旨在开发和应用计算模型的物理约束的生物化学的一个典型例子：蛋白质折叠内质网（ER）的稳态，提供了有价值的洞察ER内的物理约束如何影响其功能。ER是一种细胞内细胞器，由狭窄的片状和管状结构的动态网络组成。它负责广泛的功能，包括许多蛋白质的合成，折叠和运输。环境干扰可导致ER功能破坏，称为ER应激，其特征是等待折叠的新生肽与折叠它们所需的伴侣和酶之间的失衡。当这种情况发生时，ER启动一个广泛的转录程序，称为未折叠蛋白反应（UPR），以恢复稳态。UPR不能减轻ER应激导致细胞死亡，并与许多疾病如糖尿病、阿尔茨海默病和癌症有关。 UPR由ER膜中的三种蛋白控制，其通过与ER驻留蛋白的相互作用检测ER应激，并将应激信号传递到细胞核。该计划的目的是了解这些感觉蛋白如何将ER的各种物理化学特征整合到ER应激的连贯措施中。 为了实现这一目标，我们将开发从蛋白质的原子描述到ER形态的细胞器尺度建模的计算模型。这些模型将提供ER形状、组成和内容物如何影响ER应激反应的定量预测，允许将ER下游的观察转化为难以探测的控制ER内应激反应的机制。此外，开发的连接ER形态和拥挤的压力传感器活动的工具和方法将广泛适用于其他亚细胞结构的过程。最后，由于该研究项目介于计算科学和细胞生物学之间，学员将获得备受追捧的跨学科技能，使他们处于计算生物学的最前沿。