Analysis of Early Mouse Cerebellar Neuron Developmental Control Pathways

描述（由申请人提供）：细胞和生物体通过遗传或表观遗传变异产生可遗传替代策略的能力尚未得到很好的理解，这是进化和疾病控制中的一个核心问题。网络复杂性对新细胞机制的可进化性的贡献与各种医学问题直接相关。例如，癌细胞具有令人难以置信的能力，可以逃避旨在阻止其生长的治疗。已知病原体使用表观遗传和遗传修饰来快速切换细胞表面蛋白的表达以逃避免疫系统的检测。定量基因组医学的一个重要任务是表征细胞对药物或突变干扰的适应性反应，然后用模型量化它们并预测适应模式。 将采用系统的方法来表征芽殖酵母细胞中复杂的遗传或表观遗传变化，以响应有丝分裂出口网络的扰动。有丝分裂出口网络（MEN）是一个复杂的调控网络，与其他几个重要的途径相互作用，以控制有丝分裂出口和胞质分裂的时间。基本目标是确定当MEN通路中的关键基因被删除时，是否在抑制子中表现出特征模式和组织原则。 遗传杂交，活细胞成像和微阵列表达测量将用于表征适应。然后，将最近出版物中的数据与来自多个来源的确证数据相结合，以建立MEN和适应的计算模型，并对其进行定量分析和比较，以发现模式。这可能有助于深入了解网络复杂性如何有助于激活新的细胞机制，并可能在预测细胞对靶向药物治疗的可能反应方面具有实际应用。

DESCRIPTION (provided by applicant): The ability of cells and organisms to generate heritable alternative strategies through genetic or epigenetic variations is not well understood and is a central issue in evolution and disease control. The contribution of network complexity to evolvability of new cellular mechanisms is directly relevant to a variety of medical concerns. For example, cancer cells have incredible abilities to escape treatments that are intended to block their growth. Pathogens are known to use epigenetic and genetic modifications to rapidly switch expression of cell-surface proteins to escape detection by the immune system. An important task for quantitative genomic medicine is to characterize the adaptive response of cells to perturbations by drugs or mutation, then to quantify them with models and predict the patterns of adaptation. A systems approach will be adopted to characterize the complex genetic or epigenetic changes in budding yeast cells in response to perturbations of the mitotic exit network. The mitotic exit network (MEN) is a complicated regulatory network that interacts with several other important pathways to control the timing of mitotic exit and cytokinesis. The fundamental goal is to determine if characteristic patterns and organizational principles are exhibited in suppressors when key genes are deleted from the MEN pathway. Genetic crosses, live cell imaging, and microarray expression measurements will be used to characterize adaptations. Then data from recent publications will be integrated with corroborating data from several sources to build computational models of the MEN and adaptations, which will be analyzed and compared quantitatively to discover patterns. This may provide insights into how network complexity contributes to the activation of new cellular mechanisms and may have practical applications for predicting the possible response of cells to targeted drug treatments.