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

哥伦比亚大学和加州大学伯克利分校获得赠款，用于开发新的分析工具，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集成，弥合工程学和生物科学之间的跨学科差距。自下而上的方法将生物网络描述为基本生物分子相互作用的集合。前者是新兴的工程学科：基因组信号处理(GSP)的主题，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，当需要符合生化/生物物理规律时，需要由普惠制对给定生物系统进行分析的假定信号处理机制的数量可以大大减少。由此产生的生物化学约束GSP(BioGSP)方法能够产生与传统GSP方法相同的结果，但其效率明显更高，并且确保符合生物机制的关键分子属性。生物系统由分子和分子络合物组成，它们的相互作用由复杂的电路和网络组成。对其结构和功能的了解可以导致控制生物机制的强大新方法，这可能使新的方法能够弥补自然生物过程中的缺陷，以及设计全新的合成生物分子设计。最近实验技术的进步让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，这在很大程度上是因为所涉及的网络的规模和复杂性，以及不同分子物种之间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尖锐--既是因为它们对生物系统开发和运行的重要性，也是因为它们采用的往往是复杂的监管模式。有关该项目的更多信息可在PI网站上找到，网址为：http://www.ee.columbia.edu/~wangx/和http://genomics.lbl.gov/index.html.。

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.