Publications

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A multi-laboratory ontology construction effort during the summer and fall of 2009 prototyped an ontology for counterfeit semiconductor manufacturing. This effort included an ontology development team and an ontology validation methods team. Here the third team of the Ontology Project, the Data Analysis (DA) team reports on their approaches, the tools they used, and results for mining literature for terminology pertinent to counterfeit semiconductor manufacturing. A discussion of the value of ontology-based analysis is presented, with insights drawn from other ontology-based methods regularly used in the analysis of genomic experiments. Finally, suggestions for future work are offered.

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Realistic cell models could greatly accelerate our ability to engineer biochemical pathways and the production of valuable organic products, which would be of great use in the development of biofuels, pharmaceuticals, and the crops for the next green revolution. However, this level of engineering will require a great deal more knowledge about the mechanisms of life than is currently available. In particular, we need to understand the interactome (which proteins interact) as it is situated in the three dimensional geometry of the cell (i.e., a situated interactome), and the regulation/dynamics of these interactions. Methods for optical proteomics have become available that allow the monitoring and even disruption/control of interacting proteins in living cells. Here, a range of these methods is reviewed with respect to their role in elucidating the interactome and the relevant spatial localizations. Development of these technologies and their integration into the core competencies of research organizations can position whole institutions and teams of researchers to lead in both the fundamental science and the engineering applications of cellular biology. That leadership could be particularly important with respect to problems of national urgency centered around security, biofuels, and healthcare.

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An experiment was conducted comparing the effectiveness of individual versus group electronic brainstorming in order to address difficult, real world challenges. While industrial reliance on electronic communications has become ubiquitous, empirical and theoretical understanding of the bounds of its effectiveness have been limited. Previous research using short-term, laboratory experiments have engaged small groups of students in answering questions irrelevant to an industrial setting. The present experiment extends current findings beyond the laboratory to larger groups of real-world employees addressing organization-relevant challenges over the course of four days. Employees and contractors at a national security laboratory participated, either in a group setting or individually, in an electronic brainstorm to pose solutions to a 'wickedly' difficult problem. The data demonstrate that (for this design) individuals perform at least as well as groups in producing quantity of electronic ideas, regardless of brainstorming duration. However, when judged with respect to quality along three dimensions (originality, feasibility, and effectiveness), the individuals significantly (p<0.05) out-performed the group working together. When idea quality is used as the benchmark of success, these data indicate that work-relevant challenges are better solved by aggregating electronic individual responses, rather than electronically convening a group. This research suggests that industrial reliance upon electronic problem solving groups should be tempered, and large nominal groups might be the more appropriate vehicle for solving wicked corporate issues.

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An experiment was conducted comparing the effectiveness of individual versus group electronic brainstorming in addressing real-world 'wickedly difficult' challenges. Previous laboratory research has engaged small groups of students in answering questions irrelevant to an industrial setting. The current experiment extended this research to larger, real-world employee groups engaged in addressing organization-relevant challenges. Within the present experiment, the data demonstrated that individuals performed at least as well as groups in terms of number of ideas produced and significantly (p < .02) outperformed groups in terms of the quality of those ideas (as measured along the dimensions of originality, feasibility, and effectiveness).

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The present paper explores group dynamics and electronic communication, two components of wicked problem solving that are inherent to the national security environment (as well as many other business environments). First, because there can be no ''right'' answer or solution without first having agreement about the definition of the problem and the social meaning of a ''right solution'', these problems (often) fundamentally relate to the social aspects of groups, an area with much empirical research and application still needed. Second, as computer networks have been increasingly used to conduct business with decreased costs, increased information accessibility, and rapid document, database, and message exchange, electronic communication enables a new form of problem solving group that has yet to be well understood, especially as it relates to solving wicked problems.

An experiment is proposed which will compare the effectiveness of individual versus group brainstorming in addressing difficult, real world challenges. Previous research into electronic brainstorming has largely been limited to laboratory experiments using small groups of students answering questions irrelevant to an industrial setting. The proposed experiment attempts to extend current findings to real-world employees and organization-relevant challenges. Our employees will brainstorm ideas over the course of several days, echoing the real-world scenario in an industrial setting. The methodology and hypotheses to be tested are presented along with two questions for the experimental brainstorming sessions. One question has been used in prior work and will allow calibration of the new results with existing work. The second question qualifies as a complicated, perhaps even wickedly hard, question, with relevance to modern management practices.

An experiment was conducted comparing the effectiveness of individual versus group electronic brainstorming in order to address difficult, real world challenges. While industrial reliance on electronic communications has become ubiquitous, empirical and theoretical understanding of the bounds of its effectiveness have been limited. Previous research using short-term, laboratory experiments have engaged small groups of students in answering questions irrelevant to an industrial setting. The current experiment extends current findings beyond the laboratory to larger groups of real-world employees addressing organization-relevant challenges over the course of four days. Findings are twofold. First, the data demonstrate that (for this design) individuals perform at least as well as groups in producing quantity of electronic ideas, regardless of brainstorming duration. However, when judged with respect to quality along three dimensions (originality, feasibility, and effectiveness), the individuals significantly (p<0.05) out performed the group working together. The theoretical and applied (e.g., cost effectiveness) implications of this finding are discussed. Second, the current experiment yielded several viable solutions to the wickedly difficult problem that was posed.

A web-based brainstorm was conducted in the summer of 2007 within the Sandia Restricted Network. This brainstorming experiment was modeled around the 'yellow sticky' brainstorms that are used in many face-to-face meetings at Sandia National Laboratories. This document discusses the implementation and makes suggestions for future implementations.

Techniques for high throughput determinations of interactomes, together with high resolution protein collocalizations maps within organelles and through membranes will soon create a vast resource. With these data, biological descriptions, akin to the high dimensional phase spaces familiar to physicists, will become possible. These descriptions will capture sufficient information to make possible realistic, system-level models of cells. The descriptions and the computational models they enable will require powerful computing techniques. This report is offered as a call to the computational biology community to begin thinking at this scale and as a challenge to develop the required algorithms and codes to make use of the new data.3

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A novel hyperspectral fluorescence microscope for high-resolution 3D optical sectioning of cells and other structures has been designed, constructed, and used to investigate a number of different problems. We have significantly extended new multivariate curve resolution (MCR) data analysis methods to deconvolve the hyperspectral image data and to rapidly extract quantitative 3D concentration distribution maps of all emitting species. The imaging system has many advantages over current confocal imaging systems including simultaneous monitoring of numerous highly overlapped fluorophores, immunity to autofluorescence or impurity fluorescence, enhanced sensitivity, and dramatically improved accuracy, reliability, and dynamic range. Efficient data compression in the spectral dimension has allowed personal computers to perform quantitative analysis of hyperspectral images of large size without loss of image quality. We have also developed and tested software to perform analysis of time resolved hyperspectral images using trilinear multivariate analysis methods. The new imaging system is an enabling technology for numerous applications including (1) 3D composition mapping analysis of multicomponent processes occurring during host-pathogen interactions, (2) monitoring microfluidic processes, (3) imaging of molecular motors and (4) understanding photosynthetic processes in wild type and mutant Synechocystis cyanobacteria.

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Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

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In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.

Molecular analysis of cancer, at the genomic level, could lead to individualized patient diagnostics and treatments. The developments to follow will signal a significant paradigm shift in the clinical management of human cancer. Despite our initial hopes, however, it seems that simple analysis of microarray data cannot elucidate clinically significant gene functions and mechanisms. Extracting biological information from microarray data requires a complicated path involving multidisciplinary teams of biomedical researchers, computer scientists, mathematicians, statisticians, and computational linguists. The integration of the diverse outputs of each team is the limiting factor in the progress to discover candidate genes and pathways associated with the molecular biology of cancer. Specifically, one must deal with sets of significant genes identified by each method and extract whatever useful information may be found by comparing these different gene lists. Here we present our experience with such comparisons, and share methods developed in the analysis of an infant leukemia cohort studied on Affymetrix HG-U95A arrays. In particular, spatial gene clustering, hyper-dimensional projections, and computational linguistics were used to compare different gene lists. In spatial gene clustering, different gene lists are grouped together and visualized on a three-dimensional expression map, where genes with similar expressions are co-located. In another approach, projections from gene expression space onto a sphere clarify how groups of genes can jointly have more predictive power than groups of individually selected genes. Finally, online literature is automatically rearranged to present information about genes common to multiple groups, or to contrast the differences between the lists. The combination of these methods has improved our understanding of infant leukemia. While the complicated reality of the biology dashed our initial, optimistic hopes for simple answers from microarrays, we have made progress by combining very different analytic approaches.

High throughput instruments and analysis techniques are required in order to make good use of the genomic sequences that have recently become available for many species, including humans. These instruments and methods must work with tens of thousands of genes simultaneously, and must be able to identify the small subsets of those genes that are implicated in the observed phenotypes, or, for instance, in responses to therapies. Microarrays represent one such high throughput method, which continue to find increasingly broad application. This project has improved microarray technology in several important areas. First, we developed the hyperspectral scanner, which has discovered and diagnosed numerous flaws in techniques broadly employed by microarray researchers. Second, we used a series of statistically designed experiments to identify and correct errors in our microarray data to dramatically improve the accuracy, precision, and repeatability of the microarray gene expression data. Third, our research developed new informatics techniques to identify genes with significantly different expression levels. Finally, natural language processing techniques were applied to improve our ability to make use of online literature annotating the important genes. In combination, this research has improved the reliability and precision of laboratory methods and instruments, while also enabling substantially faster analysis and discovery.

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to ''achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life.'' While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, ''Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling.'' This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.

Collaboration between Sandia National Laboratories and the University of New Mexico Biology Department resulted in the capability to train students in microarray techniques and the interpretation of data from microarray experiments. These studies provide for a better understanding of the role of stationary phase and the gene regulation involved in exit from stationary phase, which may eventually have important clinical implications. Importantly, this research trained numerous students and is the basis for three new Ph.D. projects.

VxInsight provides a visual mechanism for browsing, exploring and retrieving information from a database. The graphical display conveys information about the relationship between objects in several ways and on multiple scales. In this way, individual objects are always observed within a larger context. For example, consider a database consisting of a set of scientific papers. Imagine that the papers have been organized in a two dimensional geometry so that related papers are located close to each other. Now construct a landscape where the altitude reflects the local density of papers. Papers on physics will form a mountain range, and a different range will stand over the biological papers. In between will be research reports from biophysics and other bridging disciplines. Now, imagine exploring these mountains. If we zoom in closer, the physics mountains will resolve into a set of sub-disciplines. Eventually, by zooming in far enough, the individual papers become visible. By pointing and clicking you can learn more about papers of interest or retrieve their full text. Although physical proximity conveys a great deal of information about the relationship between documents, you can also see which papers reference which others, by drawing lines between the citing and cited papers. For even more information, you can choose to highlight papers by a particular researcher or a particular institution, or show the accumulation of papers through time, watching some disciplines explode and other stagnate. VxInsight is a general purpose tool, which enables this kind of interaction with wide variety of relational data: documents, patents, web pages, and financial transactions are just a few examples. The tool allows users to interactively browse, explore and retrieve information from the database in an intuitive way.

In the last decade there has been interest and research in the area of designing circuits with genetic algorithms, evolutionary algorithms, and genetic programming. However, the ability to design circuits of the size and complexity required by modern engineering design problems, simply by specifying required outputs for given inputs has as yet eluded researchers. This paper describes current research in the area of designing logic circuits using an evolutionary algorithm. The goal of the research is to improve the effectiveness of this method and make it a practical aid for design engineers. A novel method of implementing the algorithm is introduced, and results are presented for various multiprocessing systems. In addition to evolving standard arithmetic circuits, work in the area of evolving circuits that perform digital signal processing tasks is described.