Reprogramming cell-fate decisions through predictive modeling and synthetic biology

项目摘要 合成生物学的进展为探究生物学与生物学之间的复杂关系提供了强有力的工具。 网络结构和功能。在这项研究中，我们将联合收割机合成生物学与计算建模相结合， 研究网络介导的细胞损伤和退化的调节，这是一个复杂的生物学过程。作为 在哺乳动物中进行类似的研究是时间和资源密集型的，我们选择专注于 酿酒酵母，这已被证明是一个遗传上易于处理的模型，为许多基本的 有丝分裂细胞中的过程，并允许鉴定许多调节细胞命运的保守基因 真核生物的决定。新出现的问题包括这些基因如何相互作用以及相互作用如何变化 动态地驱动多代细胞劣化动态。我们最近发现了两种不同的表型 在基因相同的酵母细胞中，当它们接近细胞死亡时：一个核糖体DNA（rDNA）减少， 沉默和核仁下降（模式1），而另一个与血红素耗竭和线粒体下降（模式 2）。我们发现，随机性在选择两条路径中的一条时起着重要作用，但一旦命运决定 它几乎总是不可逆转的。我们发现了一个核心分子回路， 去乙酰化酶Sir 2和血红素激活蛋白（HAP）转录复合物，决定 选择这两条路径之一。基于该模型，我们能够设计细胞遵循第三条路径， 显著延长的生长和存活期，无恶化（模式3）。在本提案中，我们将 扩大这些努力，系统地扰乱和重新连接控制细胞命运的核心电路， 重新规划其决策过程。在目标1中，我们将使用化学诱导型启动子来控制 Sir 2和HAP的表达，从而调节同基因细胞中的细胞命运决定。我们将使用微流体技术 产生不同的，动态模式的Sir 2和HAP表达，并评估其对损伤的影响 积累、生理变化和细胞衰退。在目标2中，我们将基因重组核心Sir 2-HAP 电路的指导下，计算建模，并检查这些工程电路如何管理细胞， 命运决定和细胞退化动力学。在目标3中，我们将使用高通量微流体技术来识别 与模式1，模式2和模式3相关的基因表达程序，并检查如何扰动的模式， 这些方案影响多代人的恶化动态。这些分析将揭示基因， Sir 2-HAP核心电路和下游模块之间缺失连接的基础过程 导致细胞死亡的细胞衰退。它们将使我们能够扩展我们的计算模型， 提高其预测能力。在整个研究中，我们将构建确定性和随机性模型， 将产生可测试的预测，并指导合成基因电路的工程。如果成功，这项研究将 促进对细胞命运决定和细胞退化的定量和预测性理解。

Project Summary Advances in synthetic biology provide powerful tools to interrogate the complex relationship between network structure and function. In this study, we will combine synthetic biology with computational modeling to investigate network-mediated regulation of cell damage and deterioration, a complex biological process. As similar studies in mammals are prohibitively time- and resource-intensive, we choose to focus on Saccharomyces cerevisiae, which has proven to be a genetically tractable model for many fundamental processes in mitotic cells and has allowed identification of many conserved genes that regulate cell-fate decisions in eukaryotes. Emerging questions include how these genes interact and how the interactions change dynamically to drive multi-generational cell deterioration dynamics. We recently found two distinct phenotypes in genetically identical yeast cells as they approach cell death: one with decreased ribosomal DNA (rDNA) silencing and nucleolar decline (Mode 1) whereas the other with heme depletion and mitochondrial decline (Mode 2). We found that stochasticity plays an important role in choosing one of the two paths, but once the fate decision is made, it is almost always irreversible. We identified a core molecular circuit, consisting of the lysine deacetylase Sir2 and the heme-activated protein (HAP) transcriptional complex, that governs the decision to select one of these two paths. Based on the model, we were able to engineer cells to follow a third path with a dramatically extended period of growth and survival, free of deterioration (Mode 3). In this proposal, we will expand these efforts and systematically perturb and rewire the core circuit that controls cell fate in order to reprogram its decision-making process. In Aim 1, we will use chemically-inducible promoters to control expression of Sir2 and HAP and thereby modulate cell-fate decisions in isogenic cells. We will use microfluidics to generate distinct, dynamic patterns of Sir2 and HAP expression and evaluate their effects on damage accumulation, physiological changes, and cellular decline. In Aim 2, we will genetically rewire the core Sir2-HAP circuit under the guidance of computational modeling and examine how these engineered circuits govern cell- fate decisions and cell deterioration dynamics. In Aim 3, we will use high-throughput microfluidics to identify the gene expression programs associated with Mode 1, Mode 2, and Mode 3 and examine how perturbations of these programs affect multi-generational deterioration dynamics. These analyses will uncover the genes and processes that underlie the missing connections between the Sir2-HAP core circuit and downstream modules that underlie cellular decline leading to cell death. They will enable us to expand our computational model and improve its predictive power. Throughout the study, we will construct deterministic and stochastic models, which will produce testable predictions and guide engineering of synthetic gene circuits. If successful, this research will advance a quantitative and predictive understanding of cellular fate decisions and cell deterioration.