CAREER: A Probabilistic Gene Network Model of Cellular Aging and its Application on the Conserved Lifespan Extension Mechanisms of Dietary Restriction

衰老是生物学中的一个基本问题，尽管经过几十年的研究，其机制仍然难以捉摸。 利用数学方法和实验室实验，这项研究将阐明细胞衰老的基本原理，并为饮食限制如何延长寿命提供新的见解。使用一种新的数学模型，展示了酵母细胞中的衰老是如何出现的，以及涉及哪些基因及其相互作用，将研究细胞衰老与所涉及的基因相互作用的鲁棒性之间的密切联系。 由于缺乏足够的方法来评估基因的变化及其与细胞衰老的相互作用，这种新的数据分析数学方法可以改变我们对细胞衰老的认识。 结合这项研究，该项目的教育部分将为少数民族学生提供跨学科培训，并通过将研究与教学相结合、为工业和应用数学学会设立学生分会、辅导讲习班和广泛传播教育材料，培养他们对定量生物学的兴趣。 此外，一个新的系统生物学课程将有助于在斯佩尔曼学院新的生物信息学和系统生物学未成年人。 研究教程将被上传到YouTube，开放的研究项目将被上传到GitHub，并将开发和维护一个开放的研究博客。 总之，该项目将在一所历史悠久的黑人女子大学为少数民族大学生提供数学、计算机和系统生物学方面的培训。该项目的总体目标是通过综合研究和教学方法，从基因网络的角度关注衰老问题。 细胞老化将使用芽殖酵母，一个单细胞生物体作为模型系统。目前对细胞衰老的认识缺乏一致性，衰老的分子途径和衰老的人口特征之间存在着重大的逻辑差距。 已知有数百种酵母基因会影响寿命，但矛盾的是，没有一种基因可以被认为是衰老的直接原因。当以不同的方式进行实验时，观察到不同甚至相反的途径。尽管衰老的复杂性，许多物种的寿命可以通过饮食限制来延长。这一看似复杂的图景和饮食限制的进化保守的寿命延长效应将通过研究的核心思想来解决，即细胞衰老是随机基因网络的一个新兴特性。 本研究将利用一个细胞衰老的概率基因网络模型来阐明酿酒酵母（Saccharomycescerevisiae）模式生物中饮食限制的保守寿命延长机制，并研究酵母基因网络的组织和动力学如何影响衰老过程。第一个目标是通过分析数百个酵母突变体的寿命数据来验证饮食限制通过提高基因相互作用的可靠性来延长寿命的假设。 第二个目标是进一步发展细胞衰老网络模型的理论基础。

Aging is a fundamental question in biology, yet its mechanism remains elusive despite decades of research. Using mathematical approaches coupled with laboratory experiments, this study will illustrate the basic principles of cellular aging and shed new light on how dietary restriction extends lifespan. Using a novel mathematical model that demonstrates how aging in yeast cells emerges and what genes and their interactions are involved, the close-connection between cellular aging and the robustness of the gene interactions involved will be examined. Given the lack of adequate methods to evaluate changes in genes and their interactions involved in cellular aging, this novel mathematical approach for data analysis could transform what we know about how cells age. Coupled to this research, the educational component of the project will provide cross-disciplinary training to minority students and cultivate their interests in quantitative biology through integrating research with teaching, a student Chapter for the Society of Industrial and Applied Mathematics, tutorial workshops, and broad dissemination of educational materials. Additionally, a new Systems Biology course will contribute to a new Bioinformatics and Systems Biology minor at Spelman College. Research tutorials will be uploaded to YouTube, open research projects will be uploaded to GitHub, and an open research blog will be developed and maintained. In summary, this project will provide training to minority undergraduates in mathematics, computing, and systems biology at a historically black college for women.The overarching goal of this project will focus on aging from the perspective of gene networks through an integrated research and teaching approach. Cellular aging will be addressed using the budding yeast, a single-cell organism as a model system. The current knowledge on cellular aging lacks coherence, and a major logical gap exists between molecular pathways of aging and population characteristics of aging. Hundreds of yeast genes are known to influence lifespan, but paradoxically, not a single gene can be claimed as a direct cause of aging. Different and even opposite pathways have been observed when experiments are performed in different ways. Despite the complexity of aging, the lifespan of many species can be extended by dietary restriction. This seemingly complicated picture and the evolutionarily conserved lifespan extension effect of dietary restriction will be addressed by the core idea of the study, that cellular aging is an emergent property of stochastic gene networks. A probabilistic gene network model for cellular aging will be used to illustrate the conserved lifespan extension mechanism of dietary restriction in the model organism of Saccharomyces cerevisiae, and study how the organization and dynamics of the yeast gene networks will influence the aging process. The first objective is to test a hypothesis that dietary restriction extends lifespan by improving reliability of gene interaction through analyzing lifespan data of hundreds of yeast mutants. The second objective is to further develop the theoretical foundation of the network model for cellular aging.