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: 0850205
• 负责人: Adam Arkin
• 金额: $ 12.74万
• 依托单位: University of California-Berkeley
• 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 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850205&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.

哥伦比亚大学和加利福尼亚大学伯克利分校因开发了新的分析工具而获得赠款，这些工具的开发通过促进了自上而下的统计信号处理理论的协同整合，这些方法与自下而上的方法相结合，从而弥合了工程和生物科学之间的跨学科差距，这些方法将生物学网络作为基础生物分子相互作用的集合而表征了自下而上。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，当需要通过GSP与生物化学/生物物理定律保持一致时，GSP需要对特定生物系统进行分析的推定信号处理机制的数量可能会大大减少。因此，由此产生的生化GSP（BioGSP）方法能够以传统的GSP方法的基础产生结果，但效率更高，并且可以确保符合生物学机制的关键分子特性。生物系统由分子和分子复合物组成，它们的相互作用包括复杂的电路和网络。了解其结构和功能可以导致强大的新方法来控制生物学机制，这可能有可能使新方法在自然生物过程中以及工程Denovo合成生物分子设计中纠正故障。实验技术的最新进步使我们对这些系统的结构如何进行了前所未有的观点。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生化相互作用的非线性性质。对于遗传网络而言，这个问题尤其重要 - 既是因为它们对生物系统开发和运营的重要性，又是由于他们采用的经常复杂的调节模式。有关该项目的更多信息，请参见http://www.ee.columbia.edu/~wangx/和http://genomics.lbl.gov/index.html。