Detection and identification of important biological targets, such as DNA, proteins, and diseased human cells are crucial for early diagnosis and prognosis. The key to discriminate healthy cells from the diseased cells is the biophysical properties that differ radically. Micro and nanosystems, such as solid-state micropores and nanopores can measure and translate these properties of biological targets into electrical spikes to decode useful insights. Nonetheless, such approaches result in sizable data streams that are often plagued with inherit noise and baseline wanders. Moreover, the extant detection approaches are tedious, time-consuming, and error-prone, and there is no error-resilient software that can analyze large data sets instantly. The ability to effectively process and detect biological targets in larger data sets lie in the automated and accelerated data processing strategies using state-of-the-art distributed computing systems. In this dissertation, we design and develop techniques for the detection and classification of biological targets and a distributed detection framework to support data processing from multiple bio-nano devices. In a distributed setup, the collected raw data stream on a server node is split into data segments and distributed across the participating worker nodes. Each node reduces noise in the assigned data segment using moving-average filtering, and detects the electric spikes by comparing them against a statistical threshold (based on the mean and standard deviation of the data), in a Single Program Multiple Data (SPMD) style. Our proposed framework enables the detection of cancer cells in a mixture of cancer cells, red blood cells (RBCs), and white blood cells (WBCs), and achieves a maximum speedup of 6X over a single-node machine by processing 10 gigabytes of raw data using an 8-node cluster in less than a minute, which will otherwise take hours using manual analysis.
Diseases such as cancer can be mitigated, if detected and treated at an early stage. Micro and nanoscale devices, such as micropores and nanopores, enable the translocation of biological targets at finer granularity. These devices are tiny orifices in silicon-based membranes, and the output is a current signal, measured in nanoamperes. Solid-state micropore is capable of electrically measuring the biophysical properties of human cells, when a blood sample is passed through it. The passage of cells via such pores results in an interesting pattern (pulse) in the baseline current, which can be measured at a very high rate, such as 500,000 samples per second, and even higher resolution. The pulse is essentially a sequence of temporal data samples that abruptly falls below and then reverts back to a normal baseline with an acceptable predefined time interval, i.e., pulse width. The pulse features, such as width and amplitude, correspond to the translocation behavior and the extent to which the pore is blocked, under a constant potential. These features are crucial in discriminating the diseased cells from healthy cells, such as identifying cancer cells in a mixture of cells.
|Advisor:||Butt, Ali R.|
|School:||Virginia Polytechnic Institute and State University|
|Department:||Computer Science and Applications|
|School Location:||United States -- Virginia|
|Source:||DAI-B 78/09(E), Dissertation Abstracts International|
|Keywords:||Bionano sensing, DNA, Distributed systems, Human cells, Paralell computing, Pattern classification, Pattern detection, Runtime systems|
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