CIF: Small: Efficient Signal Processing Algorithms for Inference of Gene Regulatory Networks

“这项奖励是根据2009年美国复苏和再投资法案（公法111-5）资助的。”目前，分子生物学、生物信息学和系统生物学中遇到的最重要的研究问题之一是破译基因调控网络的基础机制。基因调控网络的重要性是由于它们在控制和操作活细胞中发生的过程中的基本作用。了解基因调控网络的结构和运作，有助于识别和理解细胞中大分子的功能，发现疾病和器官发育的生物学机制，开发有效的疾病诊断和治疗手段。该项目的目的是建立一个计算效率高的信号处理框架，以全面了解基因调控网络的结构和功能。在这个项目中有两个主要的研究重点。第一个研究重点是开发信息理论工具，用于有效推断基因表达之间的因果关系，并确定基因调控网络的全局拓扑结构。第二个研究重点是在整合大量异构数据源的基础上，开发一个用于基因调控网络推断的贝叶斯信息理论框架。变分贝叶斯抽样形式也被建立以克服与蒙特卡罗技术家族相关的难以处理的计算复杂性和收敛问题。该项目具有重要的科学、技术和教育贡献。通过将微阵列数据与先前的生物学知识和其他数据源相结合，所提出的计算工具有可能揭示控制转录控制和基因、蛋白质和其他大分子之间相互作用的逻辑的新方面。

"This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5)."Currently, one of the most important research problems encountered in molecular biology, bioinformatics, and systems biology consists in deciphering the mechanisms that lie at the basis of gene regulatory networks. The importance of gene regulatory networks is due to their fundamental role in the control and operation of the processes taking place in the living cell. Learning the structure and operation of gene regulatory networks facilitates the identification and understanding of the functions of macromolecules in cells, finding out the biological mechanisms of diseases and organ development, and developing efficient disease diagnosis and therapeutics means. The aim of this project is to build a computationally efficient signal processing framework for global understanding of the structure and functionality of gene regulatory networks. Two major research thrusts are addressed in this project. The first research thrust develops information theoretic tools for efficient inference of causal regulations between gene expressions, and determination of global topologies for gene regulatory networks. The second research thrust develops a Bayesian information theoretic framework for inference of gene regulatory networks based on the integration of a multitude of heterogeneous data sources. A variational Bayes sampling formalism is also built to overcome the intractable computational complexity and convergence issues associated with the family of Monte-Carlo techniques. This project brings important scientific, technological and educational contributions. By combining microarray data with prior biological knowledge and other data sources, the proposed computational tools have the potential of uncovering new aspects of the logic that governs the transcriptional control and interactions between genes, proteins and other macromolecules.