Computational Models of the Human Cell Cycle to Reveal Disease Mechanism and Inform Treatment

项目概要/摘要 该项目的总体目标是开发预测人类细胞周期的计算模型 对临床相关干扰如放疗、靶向治疗、致癌突变有反应， 定向分化。这些模型将填补我们对这些机制的理解中的一个重要空白 是涉及异常细胞增殖的疾病的起始、进展和治疗的基础。我们 方法是使用定量单细胞成像来测量增殖细胞的分子状态， 将这些数据整合到预测建模框架中。我们组建了一个跨机构的团队 包括一名计算生物学家，两名细胞生物学家和一名专门从事以下研究的医生科学家： 放射肿瘤学该团队与六个联合出版物有着强大而富有成效的合作历史， 约会目的1探讨视网膜上皮细胞对辐射诱导的DNA反应的机制 在S阶段损坏以执行G2停搏。延时成像和确定性建模将预测： 对DNA损伤的反应延迟到S/G2转换; DNA的小分子抑制剂如何 修复-目前参与临床试验-加强逮捕反应;以及如何损失的肿瘤 抑制剂p53使细胞对联合治疗难治。目的2询问胰腺上皮细胞如何与 KRAS突变逃脱了永久的细胞周期停滞。我们将使用高内涵成像来分析多个 在表达致癌KRAS的单细胞中的信号传导活性。这些数据将用于构建流形 代表细胞周期进程，跨越致癌KRAS介导的两周时间过程， 转型对流形几何形状的计算分析将识别分子分支点 G1决定胰腺细胞的增殖/停滞决定，我们将验证这些预测 通过小分子和基因操作。目的3验证人类胚胎干细胞 细胞从先前的G2期继承细胞周期特异性基因产物（特别是G1调节子）， 促进子细胞的多能性。我们将联合收割机结合有丝分裂特异性染色质分析和卷积 基于神经网络的图像分析，以确定干细胞维持快速增殖的机制 和多个细胞周期世代的多能性。每个目标都产生基础知识和应用知识， 提供了对扰动下细胞周期进程的基本见解， 分子预测为新的治疗方案提供信息。着眼于未来， 人类细胞周期将使患者特异性治疗由异常细胞驱动的疾病成为可能 增殖

PROJECT SUMMARY / ABSTRACT The overall goal of this project is to develop computational models that predict how the human cell cycle responds to clinically-relevant perturbations such as radiotherapy, targeted therapy, oncogenic mutation, and directed differentiation. These models will fill a significant void in our understanding of the mechanisms underlying the initiation, progression, and treatment of diseases that involve abnormal cell proliferation. Our approach is to use quantitative single-cell imaging to measure the molecular states of proliferating cells and to integrate these data into predictive modeling frameworks. We have assembled a cross-institutional team comprising a computational biologist, two cell biologists, and a physician scientist with specialization in radiation oncology. The team has a strong and productive history of collaboration with six joint publications to date. Aim 1 investigates the mechanism by which retinal epithelial cells respond to radiation-induced DNA damage during S phase to execute G2 arrest. Time-lapse imaging and deterministic modeling will predict: how the response to DNA damage is delayed until the S/G2 transition; how a small-molecule inhibitor of DNA repair—currently involved in clinical trial—intensifies the arrest response; and how loss of the tumor suppressor p53 renders cells refractory to combination therapy. Aim 2 asks how pancreatic epithelial cells with mutations in KRAS escape permanent cell cycle arrest. We will use high-content imaging to profile multiple signaling activities in single cells expressing oncogenic KRAS. These data will be used to construct a manifold representation of cell cycle progression that spans a two-week time course of oncogenic KRAS-mediated transformation. Computational analysis of the manifold’s geometry will identify molecular branching points in G1 that govern the proliferation/arrest decision in pancreatic cells, and we will validate these predictions through small molecules and genetic manipulation. Aim 3 tests the hypothesis that human embryonic stem cells inherit cell-cycle-specific gene products (specifically, G1 regulators) from the previous G2 phase to promote pluripotency in daughter cells. We will combine mitosis-specific chromatin profiling with convolutional neural network-based image analysis to identify the mechanisms by which stem cells sustain rapid proliferation and pluripotency over multiple cell-cycle generations. Each aim yields both basic and applied knowledge, providing fundamental insights into cell cycle progression under perturbation and generating specific, molecular predictions to inform new treatment schemes. With an eye toward the future, predictive models of the human cell cycle will enable patient-specific treatments for diseases that are driven by abnormal cell proliferation.