Dynamics of Non-equalibrium Cell State Transitions in Cell Populations

细胞群中非平衡细胞状态转变的动力学

• 批准号: 8819019
• 负责人: Sui Huang
• 金额: $ 36.16万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8819019
• 关键词: Accounting; Address; Advocacy; Affect; Antineoplastic Agents; Behavior; Biological; Biological Models; Biophysics; Cancer cell line; Case Study; Cell Culture System; Cell Culture Techniques; Cells; Characteristics; Complex; Critical Pathways; Cultured Tumor Cells; DNA Sequence Alteration; Dose; Drug Evaluation; Drug Industry; Equilibrium; Evolution; Exhibits; Failure; Future; Generic Drugs; Genetic Heterogeneity; Goals; Growth; Health; Heterogeneity; Homeostasis; Link; Malignant Neoplasms; Mathematics; Measurable; Measurement; Measures; Modeling; Molecular; Monitor; Mutation; Outcome; Pathway interactions; Pattern; Pharmaceutical Preparations; Phenotype; Population; Population Distributions; Population Dynamics; Population Heterogeneity; Process; Property; Protocols documentation; Relative (related person); Relaxation; Resistance; Series; Stable Populations; Structure; System; Systems Biology; Testing; Time; Video Microscopy; cancer cell; cancer genome; cell behavior; cell growth; cell killing; cost; digital; genome sequencing; insight; interest; killings; leukemia; mRNA Differential Displays; malignant breast neoplasm; mathematical model; neoplastic cell; non-genetic; prevent; research study; resilience; response; theories; tool; tumor progression

Non-genetic heterogeneity of (clonal) cell populations, implying the coexistence of multiple subpopulations in an apparently uniform cancer cell population, is a hallmark of tumor cells. These subpopulations represent discretely distinct (attractor) states of a multi-stable system and establish a dynamical equilibrium of the population distribution. When the population is perturbed, e.g. by elimination of one subpopulation or a drug treatment, the original population distribution is robustly reestablished after a characteristic “relaxation” process, indicating that the cell population is a complex, non-equilibrium homeostatic state. Indeed, the subpopulations exhibit distinct biological properties. We and others recently found that the subpopulations display differential growth rates and malignancy potential, that they can switch (transition) between each other, spontaneously or in response to perturbations (including therapy), and influence each other’s growth and switching rate. Moreover in the presence of anticancer drug this phenotype plasticity and relative growth rates of subpopulations can shift, for instance, to favor the subpopulation that confers resilience to the treatment. All this adds a layer of complexity not yet fully appreciated until recently –which makes the common practice of studying cancer drugs by measuring the “kill curve” (% of a presumably homogenous population killed as function of drug dose) overtly simplistic. Thus, the goal of this collaborative interdisciplinary project, involving mathematicians and experimentalists, are first, to develop a generic mathematical modeling framework that takes into account the above complications due to the dynamic heterogeneity that entail a departure from the traditional notion of uniform homogeneous cell populations (Aim 1); and second, to validate the theory in a series cell culture experiments (Aim 2). The model system consists of isogenic cell populations with two distinct subpopulations displaying differential growth and transition rates. It is amenable to analytic models, which albeit mathematically simple, already makes interesting counterintuitive predictions. We have established the baseline-characteristics of three cancer cell lines (two breast cancer and one leukemia) that exhibit all the above complexities of non-genetic dynamic heterogeneity and thus will provide a suited platform to (i) validate qualitatively distinct and quantitative model predictions through the reliably measurable population relaxation time courses, and (ii) provide the directly measured parameter values for proliferation and state transition rates through longitudinal monitoring of single-cell behaviors in digital video- microscopy. The practical outcome is an analysis framework of broad utility that thanks to the theory relies only on the readily measurable subpopulation relaxation to extract information about the type of population heterogeneity and associated potential of drugs to either “kill off” or to stimulate resistance. This will cost- effectively expand current primitive kill-curve measurements to take into account cell population heterogeneity and plasticity which are the major culprits of failure in drug treatment.

（克隆）细胞群的非遗传异质性，意味着多个亚群共存 明显均匀的癌细胞群是肿瘤细胞的标志。这些亚群代表 多稳定系统的离散不同（吸引子）状态并建立系统的动态平衡 人口分布。当人们感到不安时，例如通过消除一个亚群或一种药物 治疗后，原始人口分布在特征性的“松弛”后得到稳健重建 过程，表明细胞群处于复杂的、非平衡的稳态。确实， 亚群表现出独特的生物学特性。我们和其他人最近发现亚人群 显示不同的生长速度和恶性潜力，它们可以在每个细胞之间切换（转变） 自发地或对扰动（包括治疗）做出反应，并影响彼此的成长 和切换率。此外，在存在抗癌药物的情况下，这种表型可塑性和相对生长 例如，亚群体的比率可能会发生变化，以利于那些赋予经济恢复能力的亚群体。 治疗。所有这些都增加了一层复杂性，直到最近才得到充分认识——这使得常见的 通过测量“杀死曲线”（假定同质人群的百分比）来研究癌症药物的实践 死亡是药物剂量的函数）过于简单化。因此，这个跨学科合作项目的目标， 涉及数学家和实验学家，首先开发通用数学模型 该框架考虑了由于动态异质性而导致的上述复杂性，这需要 背离统一同质细胞群的传统概念（目标 1）；其次， 在一系列细胞培养实验中验证该理论（目标 2）。该模型系统由等基因细胞组成 具有两个不同亚群的种群，表现出不同的生长和转变率。它是顺从的 分析模型虽然在数学上很简单，但已经做出了有趣的违反直觉的预测。 我们已经建立了三种癌细胞系（两种乳腺癌和一种乳腺癌）的基线特征 白血病）表现出所有上述非遗传动态异质性的复杂性，因此将提供 一个合适的平台，（i）通过可靠的方法验证定性和定量的模型预测 可测量的群体松弛时间过程，以及（ii）提供直接测量的参数值 通过数字视频中单细胞行为的纵向监测来测量增殖和状态转换率 显微镜。实际结果是一个具有广泛实用性的分析框架，该框架依赖于理论 仅基于易于测量的亚总体松弛来提取有关总体类型的信息 药物“杀死”或刺激耐药性的异质性和相关潜力。这将花费—— 有效扩展当前的原始杀伤曲线测量，以考虑细胞群异质性 和可塑性是药物治疗失败的主要原因。