Collaborative Research: Analysis of spatiotemporal signal processing in developmental patterning

合作研究：发育模式中的时空信号处理分析

• 批准号: 0718604
• 负责人: Stanislav Shvartsman
• 金额: $ 14.5万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0718604&HistoricalAwards=false
• 关键词: Collaborative; Research; Analysis; spatiotemporal; signal

Embryonic development is driven by highly regulated spatial and temporal patterns of gene expression. These patterns emerge in a sequential process, with simple patterns providing inputs to molecular networks, which, in turn, transform these inputs into more complex outputs. Due to the recent advances in the molecular studies of development, these networks can now be modeled at mechanistic level. Exploring complex mechanistic models is critically dependent on the use of computational techniques; at the same time, the behavior of large-scale models can often be understood through analysis of simplified models, providing a "bridge" between computational and real experiments. Rigorous mathematical techniques for analyzing the dynamics of signal processing in developmental patterning are yet to be developed. To this end, the PIs propose to pursue a number of analytical approaches for studying the spatiotemporal dynamics in patterning networks. These approaches, which rely heavily on the techniques from the calculus of variations, will be applied in the context of epithelial pattern formation in the Drosophila egg, an established experimental model for studying how simple inputs establish complex spatial patterns in development. The proposed analytical and computational work will build on the modeling and experimental results obtained by the PIs during the previous funding period. Within the framework of this application, the PIs will focus on signal processing by two-dimensional patterning networks with positive feedback loops.The proposed research is closely linked to the experimental work on pattern formation mediated by the Epidermal Growth Factor Receptor (EGFR), a key regulator of epithelial tissues across species. Given the highly conserved nature of EGFR signaling and the generality of inductive patterning events, the work of the PIs will provide insights into a large class of development problems. In addition, the mathematical techniques developed in this proposal will be applicable to a wide range of problems modeled by nonlinear parabolic partial differential equations. This research is strongly aligned with the educational effort of the PIs that involves further development of undergraduate and graduate courses on mathematical biology of cell communication systems and interdisciplinary training of mathematics and engineering graduate and undergraduate students at NJIT and Princeton.

胚胎发育是由高度调节基因表达的时空模式驱动的。 这些模式在顺序过程中出现，简单模式为分子网络提供了输入，从而将这些输入转换为更复杂的输出。 由于开发分子研究的最新进展，这些网络现在可以在机械水平上进行建模。 探索复杂的机械模型在严重取决于计算技术的使用。同时，通常可以通过对简化模型的分析来理解大型模型的行为，从而在计算和真实实验之间提供“桥”。严格的数学技术用于分析发育模式中信号处理的动力学尚未开发。 为此，PIS建议采用多种分析方法来研究模式网络中的时空动力学。 这些方法在很大程度上依赖于变异的计算技术，将在果蝇卵中的上皮模式形成的背景下应用，这是一个既定的实验模型，用于研究简单输入如何在开发中建立复杂的空间模式。 所提出的分析和计算工作将基于PIS在上一个资金期间获得的建模和实验结果。 在本应用的框架内，PI将重点关注具有正反馈循环的二维图案网络的信号处理。拟议的研究与表皮生长因子受体（EGFR）介导的模式形成的实验性研究密切相关，这是跨物种上皮组织的关键调节剂。 鉴于EGFR信号传导的高度保守性和归纳模式事件的一般性，PI的工作将提供有关大量发展问题的见解。 此外，本提案中开发的数学技术将适用于由非线性抛物线偏微分方程建模的广泛问题。 这项研究与PI的教育工作密切一致，PIS涉及进一步发展细胞通信系统数学生物学的本科和研究生课程，以及NJIT和PRINCETON的数学和工程学研究生和本科生的数学和工程研究生跨学科培训。