Control engineering and gene regulatory networks

传统生物学的重点是识别单个基因、蛋白质和细胞，并研究它们的特定功能。但这种研究对整个生物体（包括人体）的了解相对有限。打个比方，如果一个人想研究一架飞机，只专注于识别发动机、机翼和安全带，并研究它们的具体功能，那么他就不会对飞机的操作有真实的了解。更重要的是，当飞机出现故障时，人们不知道如何有效地维修飞机。同样，研究生物学和人类健康的传统方法也使我们对整个生物体如何运作以及我们如何最好地预测，预防或补救潜在的健康问题的理解有限。因此，传统的生物学/医学研究人员在治疗癌症、艾滋病和糖尿病等复杂疾病方面取得的成功有限，因为他们的研究通常一次只关注生物体的几个方面。事实上，基因编码蛋白质，其中一些反过来又调节其他基因。这种交互以高度结构化但极其复杂的方式工作，类似于飞机许多部件之间的复杂交互。随着基因表达测量技术和基因组测序技术的进步，在特定的生物过程中，在一系列时间点同时测量细胞中数千个基因的表达水平已经成为可能。这样的时间过程基因表达数据提供了大多数（如果不是全部）感兴趣的基因的快照，并可能导致更好地了解基因调控网络，从而更好地了解生命。我们提出的研究的长期目标是在最基本的水平上推进对细胞系统动态行为的建模和理解（即，基因和蛋白质水平）以及在疾病诊断和治疗中控制细胞系统。

Traditional biology has focused on identifying individual genes, proteins and cells, and studying their specific functions. But that kind of research can yield relatively limited insights about the whole organism, including the human body. As an analogy, if one wanted to study an airplane, and focused on identifying the engine, wings, and seat belts, and studied their specific functions, one would have no real understanding of how an airplane operates. More importantly, one would have no understanding of how to effectively service the airplane when something malfunctions. So too, a traditional approach to studying biology and human health has left us with a limited understanding of how the whole organism operates, and how we can best predict, prevent, or remedy potential health problems. As a result, traditional biological/medical researchers have had a limited success in curing complex diseases such as cancer, HIV AIDS, and diabetes because their research generally looks at only a few aspects of an organism at a time. Actually, genes encode proteins, some of which in turn regulate other genes. Such interactions work in highly structured but incredibly complex ways, similar to the complex interactions among many parts of an airplane. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data provides a snapshot of most (if not all) of the interesting genes and may lead to a better understanding of gene regulatory networks, and thus a better understanding of life. The long-term objective of our proposed research is to advance the modeling and understanding of the dynamic behaviour of a cellular system at its most fundamental level (i.e., the gene and protein level) and to control the cellular system in disease diagnosis and treatment.