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

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。提案号：0941313PI: Juergen HahnInstitution: Texas Engineering Experiment station提案号：0941287PI: Martin YarmushInstitution: Rutgers university信号转导途径在许多细胞功能以及细胞间通信中起着关键作用。然而，阐明信号转导途径的确切机制并非易事：不同途径之间存在串扰，细胞群体内的反应可能差异很大，并且只有有限的测量能力可用于观察细胞内信号。强调信号转导的重要性及其如何受细胞群影响的一个具体例子是干细胞分化。所产生的细胞类型受细胞群和激活不同信号转导途径的细胞间通讯的影响。这个项目的重点是开发一个新的计算框架，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，他们将推导出能够区分随机成分和总体效应的技术。与他们过去的工作不同，他们只处理平均特性，他们将专注于开发技术，考虑群体中单个细胞的信息，并利用这些信息来研究群体对信号转导活动的影响。智力优势：这项工作包括以下部分：(a)开发问题公式和算法，可以解决考虑细胞群的逆问题，而不仅仅是在研究信号转导途径时常见的受高水平测量噪声影响的大量平均值。(b)在给定细胞分布的可用数据并考虑到模型中的不确定性的情况下，推导出一种确定非线性信号转导通路模型中要估计的最优参数集的新方法。(c)发展一种跨种群大规模参数估计的计算技术，以确定细胞间通讯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。这包括确定实验中细胞的数量及其空间位置，以避免由于未考虑细胞群体效应而导致结果偏差。总之，这项工作将发展和整合数学、计算和实验方法来划分随机和群体效应，最终目标是开发改进的信号转导途径模型。这些技术将应用于Jak/STAT和Erk-C/ ebp<e:2>信号通路，这些信号通路在许多细胞反应中起着重要作用，如干细胞分化和肝脏炎症反应。更广泛的影响：通过整合系统生物学领域的研究和教学工作，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，可以产生协同效应。两位pi共同教授系统生物学的高级本科生/研究生选修课，该课程整合了生物系统建模和分析所需的理论和实验方面。本课程与院系课程改革计划保持一致，将包括几个模块，这些模块也可用于其他课程和外展活动。交互式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，将投入大量努力，以软件、案例研究、本科生教育和培训的形式传播研究成果，并向代表性不足的群体推广项目。

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.