Collaborative Research: Integrating Multiple Analyses to Understand Gene Regulatory Networks

合作研究：整合多种分析以了解基因调控网络

• 批准号: 1517309
• 负责人: Mark Marten
• 金额: $ 68.55万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1517309&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Multiple; Analyses

Gene regulatory networks play a vital role in nearly every process of life as they are responsible for receiving environmental stimuli and translating these into activity inside the cell. In this project, the PIs will develop new strategies for computer-based modeling, and hence understanding and predicting, behavior of complex gene regulatory networks. The computer programs (algorithms) that will be developed represent a novel strategy integrating scientific discovery with computational innovation. Although the algorithm is being used to explore the gene regulatory networks of a fungal cell's response to stress it should lead to principles applicable to other biological systems. The model will be validated by comparing model predictions with data collected from experiments and this data will then be used to further refine the model. This project also includes initiatives to advance undergraduate education by employing an interdisciplinary problem-based learning approach that will be comprised of multi-state teams that also involve graduate students. The goal of this project is to develop a new approach for modeling gene regulatory networks. The PIs will test the hypothesis that initial experimental characterization of a network subset will permit identification of the biomolecular constituents and their connectivity, thus establishing network topology. System wide time-course measurements can then be used to refine this network into a reaction kinetic model capable of making accurate system predictions. The cell wall integrity signaling pathway of the experimentally tractable model fungus Aspergillus nidulans will serve as a model. This pathway responds to cell wall damage by activating repair mechanisms that restore cell integrity. Because protein kinases play a pivotal role in mediating cellular regulatory activities, the PIs will focus on a subset of kinases and the discovery of their associated substrates to initially assemble a rudimentary network. Subsequently the system will be experimentally perturbed for measuring its dynamic response using a robust transcriptomic, proteomic and phosphoproteomic platform. Using this data, the PIs will take a two-step approach to developing the dynamic system of coupled ordinary differential equations able to describe dynamic behavior of a model gene regulatory network. First, an ensemble approach of approximate models will be tested and refined. In the second step, the ensemble will act as the seed population for use in an evolutionary algorithm to generate a more refined and accurate model. The PIs will then validate the model by iterative comparisons of in silico predictions with experimental results.This award is co-funded by programs in Systems and Synthetic Biology (Directorate for Biological Sciences) and Biotechnology and Biochemical Bioengineering (Directorate for Engineering).

基因调控网络在生命的几乎每个过程中都发挥着至关重要的作用，因为它们负责接收环境刺激并将其转化为细胞内的活动。在这个项目中，PI 将开发基于计算机的建模的新策略，从而理解和预测复杂基因调控网络的行为。将开发的计算机程序（算法）代表了一种将科学发现与计算创新相结合的新颖策略。尽管该算法被用来探索真菌细胞对应激反应的基因调控网络，但它应该会产生适用于其他生物系统的原理。将通过将模型预测与实验收集的数据进行比较来验证模型，然后使用该数据进一步完善模型。该项目还包括通过采用跨学科的基于问题的学习方法来推进本科教育的举措，该方法将由也涉及研究生的多州团队组成。该项目的目标是开发一种新的基因调控网络建模方法。 PI 将测试以下假设：网络子集的初始实验表征将允许识别生物分子成分及其连接性，从而建立网络拓扑。然后可以使用系统范围的时间过程测量将该网络细化为能够做出准确系统预测的反应动力学模型。实验上易处理的模型真菌构巢曲霉的细胞壁完整性信号通路将作为模型。该途径通过激活恢复细胞完整性的修复机制来响应细胞壁损伤。由于蛋白激酶在介导细胞调节活动中发挥着关键作用，因此 PI 将重点关注激酶的子集以及其相关底物的发现，以最初组装一个基本网络。随后，系统将通过实验扰动，使用强大的转录组、蛋白质组和磷酸蛋白质组平台测量其动态响应。利用这些数据，PI 将采取两步方法来开发耦合常微分方程的动态系统，该系统能够描述模型基因调控网络的动态行为。首先，将测试和改进近似模型的集成方法。在第二步中，集成将充当种子群体，用于进化算法，以生成更精细和准确的模型。然后，PI 将通过计算机预测与实验结果的迭代比较来验证模型。该奖项由系统和合成生物学（生物科学理事会）以及生物技术和生物化学生物工程（工程理事会）项目共同资助。