A further challenge in ultrasensitive data acquisition in living cells is that the substances of interest, particularly proteins, occur at a wide range of concentrations (varying by many orders of magnitude). For many important proteins, this may be as few as hundreds of individual molecules. Detection and analysis at such low levels must work even in the face of wide statistical fluctuation, transient modifications, and a wide range of physical and chemical properties.33

At the finest grain, detection and analysis of single molecules could provide further understanding of cellular mechanisms. Again, although there are current techniques to analyze molecular structure (such as nuclear magnetic resonance and X-ray crystallography), these work on large, static samples. To achieve more precise understanding of cellular mechanisms, it is necessary to detect the presence and activity of very small concentrations, even single molecules, dynamically within living cells. Making progress in this field will require advances in chemistry, instrumentation, sensors, and image analysis algorithms.34

Embedded networked sensor (ENS) systems will ride the cost reduction curve that characterizes much of modern electronic systems. Based on microsensors, on-board processing, and wireless communications, ENS systems can monitor phenomena “up close.” Nevertheless, taken as a whole, ENS systems present challenges with respect to longevity, autonomy, scalability, performance, and resilience. For example, off-the-shelf sensors embedded in heterogeneous soil for monitoring soil moisture and nitrate levels raise issues related to calibration when embedded in a previously unknown environment. In addition, the uncertainty in the data they provide must be characterized. Interesting theoretical issues arise with respect to the statistical and information-theoretic foundations for adaptive sampling and data fusion. Also, of course, programming abstractions, common services, and tools for programming the network must be developed.

To illustrate a specific application, consider some of the computing challenges in deploying ENS systems for marine microorganisms. The ultimate goal is to deploy large groups of autonomous, mobile microrobots capable of identifying and tracking microorganisms in real time in the marine environment, while measuring the relevant environmental conditions at the required temporal and spatial scales. Sensors must be mobile to track microorganisms and assess their abundance with a reasonable number of sensors. They must be small, so that they are able to gather information at a spatial scale comparable to the size of the microorganisms and to avoid disturbing them. They must operate in a liquid environment—combined with small sensor size, operation in such an environment raises many difficult issues of mobility, communications, and power, which in turn strongly impact network algorithms and strategies. Also, sensors must be capable of in situ, real-time identification of microorganisms, which requires the development of new sensors with considerable on-board processing capability. Progress in this application—monitoring marine environments and single-cell identification—is expected to be applicable to other liquid environments, such as the circulatory system of higher organisms, including humans.


R.D. Smith et al., “Application of New Technologies for Comprehensive, Quantitative and High Throughput Microbial Proteomics,” abstracts of the Department of Energy’s (DOE) Genomes to Life Systems-Biology Projects on Microbes Sequenced by the U.S. DOE’s Microbial Genome Program, available at http://doegenomestolife.org/pubs/2004abstracts/html/Tech_Dev.shtml#_VPID_289.


See, for example, the text of the NIH Program Announcement PA-01-049, “Single Molecule Detection and Manipulation,” released February 12, 2001, available at http://grants.nih.gov/grants/guide/pa-files/PA-01-049.html.

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement