Multi-scale modeling of asymmetric cell division

描述(申请人提供)：不对称的细胞分裂对于干细胞同时产生新的后代和自我更新是必不可少的。一个错综复杂的监管网络控制着时间和空间上的不对称划分。该网络的损伤可导致不受限制的复制，导致不典型增生和肿瘤的发生。采用综合系统生物学的方法来理解细胞不对称分裂的机制细节。将计算模型和稳健性分析相结合，生成可通过实验验证的假设，并将验证后的假设添加到模型中，将产生对系统的更多见解。这种方法将首先使用多尺度混合模型来帮助研究细菌模型系统Caulbacter，其中反应根据它们的速率和调节功能被分类，并用不同的数值技术进行模拟，这对于模拟像不对称细胞分裂这样复杂的系统是必不可少的。然后，通过使用多层稳健性分析框架在不同级别上检查模型的稳健性，来识别在不对称分裂期间导致细胞命运变化的关键因素。为了进一步表征和理解细胞命运决定因素的不对称定位的功能，导致子代细胞之间的细胞命运分叉，模块化的蛋白质相互作用结构域和蛋白质支架被开发出来在空间上对它们进行扰动。这是由合成生物学设计的部件和设备可以用于研究系统生物学的第一个演示。然后将使用相同的计算方法来研究结肠癌起始细胞(CCIC)。CCIC是一种能够自我更新并形成肿瘤的癌症干细胞。一项创新的技术使体外CCIC细胞株能够保持其自我更新和成瘤能力。首次证实在CCIC中Notch信号通路水平升高，抑制Notch信号通路导致不对称性丧失，最终导致细胞凋亡。细胞命运决定子Numb是一种缺口抑制因子，在有丝分裂过程中不对称地定位，表明不对称分裂在肿瘤形态中起着关键作用。利用计算系统生物学，Noch和WNT等信号通路以及细胞命运决定因素将被系统地扰乱。基于对其下游靶标高通量转录组变化的计算分析，包括统计分析、聚类、利用生物信息学数据库和网络可视化工具，将建立一个多尺度系统模型，以帮助理解CCIC的不对称分裂并发现新的调控功能。这项拟议的研究将有助于更好地了解癌症起始细胞，以便确定癌症治疗的新靶点。综合系统生物学方法的计算和实验技术将随时可供生物医学界用于研究其他系统。

DESCRIPTION (provided by applicant): Asymmetric cell division is essential for stem cells to simultaneously generate new progeny and self- renew. An intricate regulatory network controls asymmetric division in both time and space. Impairments in this network can cause unrestrained replication, leading to dysplasia and tumorigenesis. An integrative systems biology approach is adopted to understand the mechanistic details of asymmetric cell division. Computational models and robustness analysis are combined to generate hypotheses that can be verified by experiments, and the addition of the verified hypotheses into the model will generate more insights into the system. This approach will first help study a bacterial model system, Caulobacter, using a multiscale hybrid model, where reactions are classified into categories based on their rates and regulatory functions and simulated by different numerical techniques, which is essential for modeling a system as complex as asymmetric cell division. The critical factors responsible for switching cell fate during asymmetric division will then be identified by checking the robustness of the model on different levels using a multi-tiered robustness analysis framework. To further characterize and understand the function of asymmetric localization of cell fate determinants, which are essential for causing the bifurcation of cell fates between the daughter cells, modular protein interaction domains and protein scaffolds are developed to perturb them spatially. This is the first demonstration that parts and devices designed from synthetic biology can be used to study systems biology. Colon cancer-initiating cells (CCIC) will then be studied using the same computational approach. CCIC are cancer stem cells that can self-renew and form tumors. An innovative technology enables in vitro CCIC cell lines to maintain their self-renewal and tumor formation capability. It is demonstrated for the first time that the level of the Notch signaling pathway is elevated in CCIC, the inhibition of which causes loss of asymmetry and eventually leads to apoptosis. The cell fate determinant NUMB, a notch inhibitor, is shown to asymmetrically localize during mitosis, indicating that asymmetric division plays a crucial role in cancer morphology. Using computational systems biology, signaling pathways like NOTCH and WNT, and cell fate determinants will be systematically perturbed. Based on the computational analysis of hight-throughput transcriptome changes in their downstream targets, which involves statistical analysis, clustering, utilizing bioinformatics databases and network visualization tools, a multiscale systems model will be built to help understand the CCIC asymmetric division and find novel regulatory functions. The proposed research will lead to a better understanding of cancer initiating cells in order to identify novel targets for cancer therapy. The computational and experimental techniques for the integrative systems biology approach will be readily available to the biomedical community to study other systems.