Infectious diseases are a major cause of death and economic impact worldwide. A more robust, adaptable, and scalable infrastructure would improve the capability to respond to epidemics. Because engineers contribute to the design and implementation of infrastructure, there are opportunities for innovative solutions to infectious disease response within existing systems that have utility, and therefore resources, before a public health emergency. Examples of innovative leveraging of infrastructure, technologies to enhance existing disease management strategies, engineering approaches to accelerate the rate of discovery and application of scientific, clinical, and public health information, and ethical issues that need to be addressed for implementation are presented.

BACKGROUND: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. RESULTS: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. CONCLUSION: Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems.

There has been a significant increase, fueled by technologies front the human genome project, in the availability of nucleic acid sequence information for viruses and bacteria. This paper presents a computer-assisted process that begins with nucleic acid sequence information and produces highly specific pathogen signatures. When combined with instrumentation using the polymerase chain reaction, the resulting diagnostics are both specific and sensitive. The computational and engineering aspects of converting raw sequence data into pathogen-specific and instrument-ready assays are presented. Examples and data are presented for specific pathogens, including foot-and-mouth disease virus and the human immunodeficiency virus.

DNA chips and microarrays are used to profile gene transcription. Unfortunately, the initial fabrication cost for a chip and the reagent costs to amplify thousands of open reading frames for a microarray are over $100K for a typical 4 Mbase bacterial genome. To avoid these expensive steps, a matrix formulation of a universal hybrid chip-microarray approach to transcript profiling is demonstrated for synthetic data. Initial considerations for application to the 4.3 Mbase bacterium Yersinia pestis are also presented. This approach can be applied to arbitrary bacteria by recalculating a matrix and pseudoinverse. This approach avoids the large upfront expenses associated with DNA chips and microarrays.

The DNA sequence of the human genome has been determined. This significant scientific milestone has been a multidisciplinary effort sponsored by both public and private investments. Engineering and computer science contributions have been essential to success, especially with the accelerated schedules of the Human Genome Project. A tutorial summary of the biological and health implications of sequencing the human genome is presented together with examples of how genome data from human and other organisms are already used. Engineering contributions to sequencing are identified as well as predictions of how engineering methods may contribute to the "postsequencing" era of biology.

Air- and space-borne platforms using synthetic aperture radars (SAR) have made interferometric measurements by using either two physical antennas mounted on one air-frame or two passes of one antenna over a scene. A new interferometric technique using one pass of a single-antenna SAR system is proposed and demonstrated on data collected by the NASA-JPL AirSAR. Remotely sensed L-band microwave data are used to show the sensitivity of this technique to ocean surface features and provide a baseline for comparison with work by others using two-antenna systems.>

A method has been developed to reduce large two-dimensional images to significantly smaller feature lists. These feature lists overcome the problem of storing and manipulating large amounts of data. A new artificial neural network using conjugate gradient training methods, operating on sets of feature lists, was successfully trained to determine the presence or absence of wakes in synthetic aperture radar images. A comparison has been made between the different conjugate gradient and steepest-descent training methods and has demonstrated the superiority of the former over the latter.>