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.
Accurate and rapid identification of pathogenic micro-organisms is of critical importance in disease treatment, public health, and national security. Unfortunately the conventional ways and means for bacterial sensing are time-consuming, expensive, and tedious for clinical purposes, especially in emergency situations. In this project, we have applied the recently developed theory of compressive sensing (CS) to the problem of bacteria detection and classification. CS is based on the realization that a small collection of measurements can be sufficient to accurately encode the information in a large data set when the data set has what is called a "sparse" structure. By sparse, we mean that the large data set is controlled by only small number of parameters. CS enables new data acquisition protocols that directly acquire just the salient information about the signal or image of interest. Its implications are promising for many applications and enable the design of new kinds of analog-to-digital converters, tomographic imaging systems, cameras, and distributed processing and coding algorithms for sensor networks and content distribution. An intriguing aspect is the central role played by randomization; that is, in some sense the "best" measurements to take have a random character. CS fuses the theories of approximation, compression, inverse problems, and random matrices and is being developed by a multidisciplinary community of researchers from signal processing, applied mathematics, and computer science. Inspired by the ideas of CS, this project is designing a new framework for bacteria detection and classification in which they are identified by their unique response to a set of fluorescent DNA probes. Our key research hypothesis is that bacterial and other organisms can be detected and classified according to how they interact (hybridize) with a small set of DNA probes. Leveraging the mathematics of CS, this approach promises to deliver high accuracy and stable results even when the number of probes is far smaller (only logarithmic) in the number of possible bacteria in a sample. Using both simulations and in wet lab experiments, we have demonstrated that it is possible to accurately detect and differentiate the presence of a bacterial species in a sample using only very few CS-based DNA probes. Indeed, thanks to the CS framework, the number of probes required only grows logarithmically with the number of target bacteria. Follow-on research is constructing a system that could potentially be commercialized in order to make low-cost, near-real-time bio-sensing a practical reality.
