Multi-scale modeling of asymmetric cell division

不对称细胞分裂的多尺度建模

• 批准号: 8536853
• 负责人: Xiling Shen
• 金额: $ 28.77万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-19 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8536853
• 关键词: Adopted; Affect; Affinity; Algorithms; Apoptosis; Architecture; Bacterial Model; Binding; Bioinformatics; Biological Assay; Biological Models; Biology; Categories; Caulobacter; Caulobacter crescentus; Cell Cycle; Cell Line; Cell division; Cells; Cluster Analysis; Colon Carcinoma; Communities; Competitive Binding; Complex; Computer Analysis; Computer Simulation; Databases; Device Designs; Dysplasia; Future; Gene Expression Profile; Hybrids; Image; Imagery; Immunofluorescence Immunologic; Impairment; In Vitro; Individual; Inherited; Label; Lead; Link; Logic; Malignant Neoplasms; Measures; Methods; Mitosis; Modeling; Morphology; Notch Signaling Pathway; Pathway interactions; Play; Property; Protein Binding Domain; Proteins; Reaction; Recruitment Activity; Research; Role; Signal Pathway; Signal Transduction; Simulate; Stem cells; System; Systems Biology; Techniques; Testing; Time; Variant; WNT Signaling Pathway; base; cancer stem cell; cancer therapy; cellular engineering; computerized tools; daughter cell; design; improved; in vivo; inhibitor/antagonist; innovation; innovative technologies; insight; mathematical model; multi-scale modeling; notch protein; novel; polarized cell; research study; response; role model; scaffold; self-renewal; simulation; synthetic biology; synthetic protein; tool; tumor; tumorigenesis

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.

描述（由申请人提供）：不对称细胞分裂对于干细胞同时产生新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分工。该网络的损伤可导致不受限制的复制，导致发育异常和肿瘤发生。采用综合系统生物学方法来了解细胞不对称分裂的机制细节。计算模型和鲁棒性分析相结合，以生成可以通过实验验证的假设，并将验证的假设添加到模型中，将生成对系统的更多见解。 这种方法将首先帮助研究一个细菌模型系统，柄杆菌，使用多尺度混合模型，其中反应被分类为基于它们的速率和调节功能的类别，并通过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至关重要。然后，将通过使用多层稳健性分析框架在不同水平上检查模型的稳健性来确定在不对称分裂期间负责切换细胞命运的关键因素。为了进一步表征和理解细胞命运决定簇不对称定位的功能（这对于导致子细胞之间细胞命运的分叉至关重要），开发了模块化蛋白质相互作用结构域和蛋白质支架来在空间上扰乱它们。这是第一次证明合成生物学设计的部件和设备可以用于研究系统生物学。 然后将使用相同的计算方法研究结肠癌起始细胞（CCIC）。CCIC是能够自我更新并形成肿瘤的癌症干细胞。一项创新技术使体外CCIC细胞系能够保持其自我更新和肿瘤形成能力。这是第一次证明，在CCIC中Notch信号通路的水平升高，其抑制导致不对称性的丧失，并最终导致细胞凋亡。细胞命运决定子NUMB是一种缺口抑制剂，在有丝分裂过程中显示出不对称定位，表明不对称分裂在癌症形态中起着至关重要的作用。使用计算系统生物学，NOTCH和WNT等信号通路以及细胞命运决定因素将被系统地干扰。基于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提供给生物医学界研究其他系统。