Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays

一个活细胞在其生命周期中所执行的各种功能是通过复杂的基因和蛋白质相互作用网络来控制和调节的。网络中基因的任何不规则行为模式都可能导致细胞功能失调、细胞死亡或癌症等疾病的出现。因此，识别错误的基因相互作用模式并将其与健康细胞中的基因相互作用模式进行比较至关重要。对于这种类型的研究，最常用的生物工程系统之一是众所周知的DNA微阵列设备。DNA微阵列由包含每个被测基因的独特遗传标识符的点网格组成，能够在选择性DNA序列退火方面生成基因活性快照。微阵列在分子生物学领域也有许多其他应用，最显著的是用于检测食物、水和空气中的有害微生物。当前微阵列设计的主要缺点之一是，就全基因组研究而言，它们严重未得到充分利用；同样，对于生物传感应用，由于技术限制，现有的微阵列系统不能用于同时识别大量微生物及其菌株。研究人员研究了新的阵列架构，称为压缩传感DNA微阵列。这项研究包括寻找DNA探针，作为微生物类别的群体标识符；设计DNA组标识符的稀疏感知矩阵；开发压缩感知重建算法，能够处理由于高药剂浓度水平引起的饱和效应；表征非线性阵列的畸变和传感器尺寸之间的基本权衡；并分析了将压缩传感微阵列集成到现有生物传感器网络中的复杂性。

The diverse functions performed by a living cell during her life cycle are controlled and regulated through complicated gene- and protein- interaction networks. Any pattern of irregular behavior of genes in the network can lead to cell malfunctioning, cell death, or the emergence of diseases like cancer. It is therefore of crucial importance to recognize erroneous gene interaction patterns and compare them to those in healthy cells. For this type of study, one of the most frequently used bioengineering systems is the well known DNA microarray device. DNA microarrays consist of grids of spots containing unique genetic identifiers for each of the tested genes, capable of generating snapshots of gene activity in terms of selective DNA sequence annealing. Microarrays have also found many other applications in the field of molecular biology, most notably for the purpose of detecting hostile microbial agents in food, water, and in the air. One of the main drawbacks of current microarray designs is that they are, for the purpose of whole genome studies, severely underutilized; similarly, for biosensing applications, existing microarray systems cannot be used for simultaneous identification of a large number of microorganisms and their strains due to technological limitations.The investigators study novel array architectures, termed compressed sensing DNA microarrays. The research involves finding DNA probes that serve as group identifiers for classes of microorganisms; designing sparse sensing matrices for DNA group identifiers; developing compressed sensing reconstruction algorithms capable of handling saturation effects arising due to high agent concentration levels; characterizing the fundamental trade-offs between distortion and sensor dimension for non-linear arrays; and, analyzing the complexity of integrating compressed sensing microarrays into existing biosensor networks.