A system comprising very-large-scale integrated (VLSI) circuits is being developed as a means of bioinformatics-oriented analysis and recognition of patterns of fluorescence generated in a microarray in an advanced, highly miniaturized, portable genetic-expression- assay instrument. Such an instrument implements an on-chip combination of polymerase chain reactions and electrochemical transduction for amplification and detection of deoxyribonucleic acid (DNA).

The Biochip Collects Fluorescence Inputs from the microarray and feeds them to the ANN processor chip, which strives to recognize a bioinformatic pattern of interest.
Commonly, the design of such an instrument provides for a sample and a reference channel, so that it can be used to perform a dual-label assay for identifying differentially expressed genes. A dual-label assay also reduces spurious variability attributable to aspects of spots in the microarray that affect both the sample and the reference specimen similarly. The logarithm of the relative intensities of the two fluorescent-dye-labeled specimens at each spot is calculated and used in analyzing the fluorescence image of the assay. Heretofore, analysis of the fluorescence image has typically involved sequential, pixel-by-pixel processing in a digital computer. Such processing does not enable real-time recognition of genetic patterns of interest — a significant drawback where, for example, it may be desirable or necessary to recognize dangerous microbes in the field. In contrast, a system like the one now being developed enables robust, real-time recognition.

The system (see figure) includes a chip, denoted a biochip, that contains VLSI circuitry for collecting the fluorescence inputs and generates analog signals proportional to the logarithms of the fluorescence-intensity ratios for the spots in the microarray. The outputs of the biochip are fed as inputs to another chip that contains a VLSI artificial neural network (ANN), which performs the processing for recognition of bioinformatic patterns of interest. The ANN design provides for a combination of massively parallel neural-computing interconnections and mixed-signal (a combination of analog and digital) circuitry characterized by feature sizes in the deep-submicron range, making it possible to implement the ANN as a single VLSI chip. One notable aspect of the design is the use of a parallel row/column data-flow architecture to connect all on-chip subsystems and eliminate data-flow bottlenecks of the type caused by bandwidth limitations in conventional data buses.

The ANN includes input neurons, programmable-weight synapses, summing and inner product cells, output neurons, and an output multi-winner-take-all encoder. The programmable synapse matrix is composed of M×N cells for N×M-dimensional code vectors. There are N output summing neurons that execute a sigmoid-logarithmic (in contradistinction to a conventional sigmoid) transfer function. The synaptic weights are generated by an error-back- propagation supervised-learning algorithm executed by an off-chip host controlling processor. The outputs of the output summing neurons are fed to a multi-winner-take-all block that consists of N competitive circuit cells and uses binary codes to encode N classes.

This work was done by Wai-Chi Fang of Caltech and Jaw-Chyng Lue of University of Southen California for NASA’s Jet Propulsion Laboratory.

In accordance with Public Law 96-517, the contractor has elected to retain title to this invention. Inquiries concerning rights for its commercial use should be addressed to:

Innovative Technology Assets Management
JPL
Mail Stop 202-233
4800 Oak Grove Drive
Pasadena, CA 91109-8099
(818) 354-2240
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Refer to NPO-44155, volume and number of this NASA Tech Briefs issue, and the page number.

Topics:
Biological sciences Imaging and visualization Mathematical models Optics Semiconductors & ICs Simulation and modeling
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VLSI Microsystem for Rapid Bioinformatic Pattern Recognition

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This article first appeared in the August, 2009 issue of NASA Tech Briefs Magazine (Vol. 33 No. 8).

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Overview

The document discusses the development of a VLSI (Very Large Scale Integration) biochip microsystem designed for rapid and robust bioinformatic pattern recognition and analysis, particularly in the context of genetic expression assays. This system aims to facilitate real-time, on-site DNA analysis, which is crucial for applications in bioinformatics and genetic research.

The primary problem addressed is the challenge of extracting genome sequencing information using traditional methods such as PCR (Polymerase Chain Reaction) amplification and capillary electrophoresis. These methods, while effective, often struggle with portability and real-time analysis, especially in hazardous environments. The proposed solution is a hardware microsystem that integrates on-chip PCR with electrochemical transduction functionality, enabling efficient DNA amplification and detection.

The architecture of the biochip microsystem includes a differential logarithm imaging chip and a weight-reconfigurable artificial neural network (ANN) mixed-signal chip. The imaging chip is designed to collect fluorescence inputs and calculate the logarithm of the ratio of sample to reference fluorescence lights, which is essential for analyzing genetic patterns. The ANN component is responsible for sorting and recognizing bioinformatic patterns using massively parallel neural computing interconnections, allowing for efficient data processing.

The ANN is structured to perform recognition tasks with a time complexity of O(1), meaning it can process input vectors rapidly. It consists of input neurons, programmable weight synapses, summing and inner product cells, and output neurons with a sigmoid-logarithmic transfer function. The system employs a multi-winner-take-all encoder to classify outputs into distinct categories. The weights for the ANN are generated through an off-chip supervised learning algorithm, enhancing the system's ability to recognize low fluorescence patterns more effectively than conventional methods.

The novelty of this work lies in the combination of a VLSI biochip microsystem with a new ANN learning algorithm that utilizes a sigmoid-logarithmic transfer function based on error back propagation. This innovative approach is expected to significantly increase the throughput of on-site assay analysis, making it easier to identify desired or suspicious bio-patterns in large datasets.

Overall, the document highlights the potential of this VLSI biochip microsystem to revolutionize genetic analysis by providing a portable, efficient, and robust solution for real-time bioinformatics applications.