Mitogenesis Networks

有丝分裂信号网络项目的总体目标是开发高水平的统计和特定的物理化学模型，描述ErbB受体和致癌K-ras激活的有丝分裂信号网络的关键特征。在过去的4年中，我们在开发各种细胞类型中ErbB家族促有丝分裂信号网络的模型方面取得了重大进展，包括描述各种ErbB家族成员表达增加的影响的统计和动力学模型。在接下来的五年里，我们将扩展这些模型，以包括由EGFR和K-Ras突变亚型产生的促有丝分裂信号网络，这些突变亚型与中枢神经和呼吸系统人类癌症的不良预后直接相关。将在各种规模下开发和测试模型，包括体外细胞培养系统、鼠异种移植物和小鼠癌症模型。此外，由于我们目前的ICBP资助的一个试点项目的成功，我们将扩展这些模型，以整合转录调控网络，提供一个更全球化的，定量的细胞调控模型，以应对致癌突变。 由于治疗耐药性是突变EGFR和突变Ras驱动的肺和脑肿瘤的标志之一，在本项目的下一阶段，我们将量化和建模各种治疗方法（包括经典化疗，靶向治疗和放射治疗）治疗引起的信号传导和转录网络改变。该项目的目标是了解肿瘤细胞在发展治疗抗性中使用的适应机制，以针对这些适应性机制。 恢复抵抗的机制。对治疗剂的促有丝分裂信号传导网络响应的定量模型将应用于人肿瘤，以测试它们预测人肿瘤对所选化疗剂的响应性的能力。该项目将促进有丝分裂信号网络模型与项目2中开发的DNA损伤反应模型的整合，从而产生更集成的细胞调控网络模型。

The overall goal of the Mitogenic Signaling Networks project is the development of high level statistical and specific physico-chemical models that describe key features of mitogenic signaling networks activated by ErbB receptors and by oncogenic K-ras. Over the past 4 years we have made significant progress in developing models of ErbB family mitogenic signaling networks in a variety of cell types, including statistical and kinetic models describing the effects of increased expression of various ErbB family members. Over the next five years we will extend these models to include mitogenic signaling networks resulting from mutant isoforms of EGFR and K-Ras that are directly associated with poor prognosis in human cancers of the central nervous and respiratory systems. Models will be developed and tested at a variety of scales, including in vitro cell culture systems, murine xenografts, and mouse cancer models. In addition, due to the success of a pilot project funded from our current ICBP, we will extend these models to integrate transcriptional regulatory networks, providing a more global, quantitative model of cellular regulation in response to oncogenic mutation. Since therapeutic resistance is one of the hallmarks of lung and brain tumors driven by mutant EGFR and mutant Ras, in the next phase of this project we will quantify and model signaling and transcriptional network alterations resulting from treatment with a variety of therapeutics, including classical chemotherapeutics, targeted therapeutics, and radiation. The goal of this project is to understand adaptation mechanisms used by tumor cells in developing therapeutic resistance in order to target these adaptive mechanisms to revert resistance. Quantitative models of mitogenic signaling network responses to therapeutics will be applied to human tumors to test their ability to predict responsiveness of human tumors to selected chemotherapeutic agents. This project will facilitate the integration of mitogenic signaling network models with DNA damage response models developed in Project 2, leading to more integrated models of cellular regulatory networks.