Collaborative Research: CDI-Type II: Extracting Population and Stochastic Effects on Signaling Activity from Transcription Factor Profiles

该奖项由2009年美国复苏和再投资法案(公法111-5)资助。提案编号：0941313PI：尤尔根·哈恩研究所：德克萨斯工程实验站提案编号：0941287PI：马丁·雅穆什研究所：罗格斯大学信号转导通路在许多细胞功能和细胞间通讯中发挥关键作用。然而，阐明信号转导途径的确切机制并不容易：不同的信号转导途径之间存在串扰，一组细胞内的反应可能显著不同，并且只有有限的测量能力可用于观察细胞内信号。一个突出信号转导的重要性以及它如何受到细胞群体影响的具体例子是干细胞分化。由此产生的细胞类型受到激活不同信号转导途径的细胞群和细胞间通讯的影响。这个项目的重点是开发一种新的计算框架，使PI能够研究细胞群在信号转导中的作用。为了做到这一点，他们将获得一些技术，使他们能够区分随机成分和总体效应。与他们过去只研究平均特性的工作不同，他们将专注于开发技术，考虑种群中单个细胞的信息，并利用这些信息来研究种群对信号转导活动的影响。智力价值：这项工作包括以下部分：(A)开发问题公式和算法，可以解决反问题，考虑细胞群体，而不仅仅是整体平均，受制于在研究信号转导路径时通常发现的高水平测量噪声。(B)在给定细胞分布的现有数据并考虑模型中的不确定性的情况下，推导出确定在非线性信号转导通路模型中估计的最佳参数集的新方法。(C)开发一种跨群体大规模参数估计的计算技术，以确定细胞间通信如何影响单个细胞的信号转导，从而更好地了解细胞行为并改进实验设计。这包括在实验中确定细胞的数量及其空间位置，以避免由于没有考虑细胞群体效应而导致的结果偏差。总而言之，这项工作将开发和整合数学、计算和实验方法来划分随机和总体效应，最终目标是开发改进的信号转导路径模型。这些技术将应用于Jak/STAT和Erk-C/EBP？信号通路，它们在许多细胞反应中发挥重要作用，如干细胞分化和肝脏的炎症反应。更广泛的影响：可以通过整合系统生物学领域的研究和教学努力，以及通过在参与建模和实验生命科学的研究小组之间建立长期合作来产生协同作用。其中两名专业人员共同教授关于系统生物学的高级本科生/研究生选修课，这门课结合了生物系统建模和分析所需的理论和实验方面。该课程与系课程改革计划相一致，并将包括几个模块，这些模块也可用于其他课程和外展活动。互动和基于网络的学习辅助工具将与模块一起开发，并纳入整个课程。此外，将致力于以软件、案例研究、本科生教育和培训以及向代表性不足的群体推广方案的形式传播研究成果。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Proposal Number: 0941313PI: Juergen HahnInstitution: Texas Engineering Experiment StationProposal Number: 0941287PI: Martin YarmushInstitution: Rutgers UniversitySignal transduction pathways play a key role in many cellular functions as well as intercellular communication. However, elucidating the exact mechanisms involved in signal transduction pathways is non-trivial: crosstalk exists between different pathways, the response within a population of cells can vary significantly, and only limited measurement capabilities are available for observing intracellular signals. One specific example highlighting the importance of signal transduction and how it is affected by cell population is stem cell differentiation. The resulting cell type is affected by the cell population and intercellular communication that activates different signal transduction pathways. This project is focused on the development of a new computational framework that enable the PIs to investigate the role of cell populations on signal transduction. In order to do so, they will derive techniques that allow them to distinguish between stochastic components and population effects. Unlike their past work, which dealt with average properties only, they will focus on developing techniques that consider information about individual cells within a population and use this information for investigating population effects on signal transduction activity. Intellectual Merit: This work includes the following portions: (a) Development of problem formulations and algorithms that can solve inverse problems considering cell populations, rather than just bulk averages, subject to the high level of measurement noise commonly found when studying signal transduction pathways. (b) Derivation of a new approach for determining the optimal set of parameters to estimate in a nonlinear signal transduction pathway model given the available data for a distribution of cells and considering uncertainty in the model. (c) Development of a computational technique for large-scale parameter estimation across populations to determine how intercellular communication affects signal transduction in individual cells, leading to a greater understanding of cellular behavior and improved experimental design. This includes determining the number of cells and their spatial location in experiments in order to avoid results that are skewed because cell population effects have not been considered. In summary, this work will develop and integrate mathematical, computational, and experimental approaches to partition stochastic and population effects with the ultimate goal of developing improved models of signal transduction pathways. These techniques will be applied to the Jak/STAT and the Erk-C/EBPâ signaling pathways which play an important role in many cellular responses, such as stem cell differentiation and the inflammatory response of the liver. Broader Impact: Synergies can be created by integrating research and teaching efforts in the area of systems biology as well as by establishing long-term collaborations between research groups involved in modeling and in the experimental life sciences. Two of the PIs coteach a senior-level undergraduate/graduate elective class on systems biology which integrates theoretical and experimental aspects required for modeling and analysis of bio-systems. The class aligns with departmental curriculum reform plans and will include several modules which can also be used in other courses and outreach activities. Interactive and web-based learning aids will be developed along with the modules and incorporated throughout the course. Additionally, significant effort will be devoted to disseminating research results in the form of software, case studies, undergraduate student education and training, and outreach programs to underrepresented groups.