Collaborative Research - Biochemically-constrained Genomic Signal Processing (BioGSP): A Multi-Scale Interdisciplinary Approach to Regulatory Network Inference

合作研究 - 生化约束基因组信号处理 (BioGSP)：一种多尺度跨学科监管网络推理方法

• 批准号: 0850030
• 负责人: Xiaodong Wang
• 金额: $ 61.53万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850030&HistoricalAwards=false
• 关键词: Collaborative; Research; Biochemically; constrained; Genomic

Columbia University and the University of California Berkley are awarded grants for the development of novel analytical tools that bridge interdisciplinary gaps between engineering and biological sciences by facilitating a synergistic integration of top-down statistical signal processing theory approaches with bottom-up methods that characterize biological networks as collections of basic biomolecular interactions. The former are the subject of the emerging engineering discipline: Genomic Signal Processing (GSP), while the latter are the domain of classical biochemistry/biophysics. The investigators recognize that the number of putative signal processing mechanisms that needs to be analyzed by GSP for a given biological system could be significantly reduced when their consistency with biochemical/biophysical laws is demanded. The resulting Biochemically-constrained GSP (BioGSP) approach is thus able to produce results on par with traditional GSP methods, but is significantly more efficient as well as assured to be in compliance with key molecular properties of biological mechanisms.Biological systems consist of molecules and molecular complexes, whose interactions comprise intricate circuits and networks. Knowledge of their structure and function can lead to powerful new ways of controlling biological mechanisms, which may potentially enable new approaches to remedying faults in natural biological processes as well as to engineering denovo synthetic biomolecular designs. Recent advancements in experimental techniques have allowed us an unprecedented view of how these systems are structured. However, detailed understanding their function remains a challenge due, in large part to the scale and complexity of networks involved as well as the nonlinear nature of biochemical interactions among the various molecular species. This issue is particularly acute for genetic networks - both because of their importance to biological systems development and operation as well as due to the often complex regulatory patterns they employ. Further information about the project may be found at the PI web sites at http://www.ee.columbia.edu/~wangx/ and http://genomics.lbl.gov/index.html.

哥伦比亚大学和加州伯克利大学获得了开发新型分析工具的赠款，这些工具通过促进自上而下的统计信号处理理论方法与自下而上的方法的协同整合，弥合了工程和生物科学之间的跨学科差距，这些方法将生物网络表征为基本生物分子相互作用的集合。前者是新兴工程学科的主题：基因组信号处理（GSP），而后者是经典生物化学/生物物理学的领域。研究人员认识到，当需要与生物化学/生物物理定律保持一致时，对于给定的生物系统，需要由GSP分析的假定信号处理机制的数量可以显着减少。由此产生的生物化学约束的GSP（BioGSP）的方法，因此能够产生的结果与传统的GSP方法，但显着更有效，以及确保符合生物机制的关键分子特性。生物系统由分子和分子复合物，其相互作用包括复杂的电路和网络。对它们的结构和功能的了解可以导致控制生物机制的强大的新方法，这可能会使新的方法来弥补自然生物过程中的缺陷以及工程从头合成生物分子设计成为可能。实验技术的最新进展使我们对这些系统的结构有了前所未有的了解。然而，详细了解它们的功能仍然是一个挑战，这在很大程度上是由于所涉及的网络的规模和复杂性，以及各种分子物种之间的生化相互作用的非线性性质。这一问题对于遗传网络来说尤其严重，因为它们对生物系统的发展和运作至关重要，而且它们采用的调控模式往往很复杂。有关该项目的更多信息，可在PI网站http://www.ee.columbia.edu/~wangx/和http://genomics.lbl.gov/index.html上找到。