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David J Galas

Publications and source records attributed to David J Galas.

7 recordsLinked to original sources
PubMed2005

Isothermal DNA amplification coupled with DNA nanosphere-based colorimetric detection.

We present a simple, rapid method for detecting short DNA sequences that combines a novel isothermal amplification method (EXPAR) with visual, colorimetric readout based on aggregation of DNA-functionalized gold nanospheres. The reaction is initiated by a trigger oligonucleotide, synthetic in nature for this proof-of-principle study, which is exponentially amplified at 55 degrees C and converted to a universal reporter oligonucleotide capable of bridging two sets of DNA-functionalized gold nanospheres. This reaction provides >10(6)-fold amplification/conversion in under 5 min. When combined with a solution containing DNA nanospheres, the bridging reporter causes nanosphere aggregation. The resulting color change from red to dark purple or blue is enhanced through spotting the solution onto a C18 reversed-phase thin-layer chromatography plate. The reaction can easily be adapted for detection of different trigger oligonucleotides using the same set of DNA nanospheres. It permits detection of as low as 100 fM trigger oligonucleotide in under 10 min total assay time, with minimal reagent consumption and requirement for instrumentation. We expect that combining this simple, versatile assay with trigger generation from a genomic target DNA sequence of interest will be a powerful tool in the development of rapid and simple point-of-care molecular diagnostic applications.

Base Sequence↗
PubMed2003

Isothermal reactions for the amplification of oligonucleotides.

We have devised a class of isothermal reactions for amplifying DNA. These homogeneous reactions rapidly synthesize short oligonucleotides (8-16 bases) specified by the sequence of an amplification template. Versions of the reactions can proceed in either a linear or an exponential amplification mode. Both of these reactions require simple, constant conditions, and the rate of amplification depends entirely on the molecular parameters governing the interactions of the molecules in the reaction. The exponential version of the reaction is a molecular chain reaction that uses the oligonucleotide products of each linear reaction to create producers of more of the same oligonucleotide. It is a highly sensitive chain reaction that can be specifically triggered by given DNA sequences and can achieve amplifications of >10(6)-fold. Several similar reactions in this class are described here. The robustness, speed, and sensitivity of the exponential reaction suggest it will be useful in rapidly detecting the presence of small amounts of a specific DNA sequence in a sample, and a range of other applications, including many currently making use of the PCR.

Base Sequence↗
PubMed2003

Duplication models for biological networks.

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N(k) approximately k(-beta)), yet the exponents, beta, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

Internet↗
PubMed2003

An historical perspective on genomic technologies.

Genomic technologies are best defined as technologies used to manipulate and analyze genomic information. The evolution of this collective power began in earnest with the invention of DNA cloning in the 1970's and most of the technology derives from the last quarter of the 20th century. The historical impact of these technologies is clearly immense. With the genome sequence becoming available for many organisms, including humans, another new view of biology has recently emerged. This review examines the shape and texture of this recent evolution, with a particular emphasis on new technology: DNA cloning, macromolecular structure analysis (X-ray crystallography and NMR), DNA sequencing, DNA synthesis, amplification by the polymerase chain reaction, and transgenic animals (bacteria through mammals).

Animals↗
PubMed2002

A duplication growth model of gene expression networks.

MOTIVATION: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network. RESULTS: Several whole-genome expression time series data sets from yeast microarray experiments were analyzed using a Markov-modeling method (Dewey and Galas, FUNC: Integr. Genomics, 1, 269-278, 2001) to infer an approximation to the underlying genetic network. We found that the global statistical properties of all the resulting networks are similar. The overall structure of these biological networks is distinctly different from that of other recently studied networks such as the Internet or social networks. These biological networks show hierarchical, hub-like structures that have some properties similar to a class of graphs known as small world graphs. Small world networks exhibit local cliquishness while exhibiting strong global connectivity. In addition to the small world properties, the biological networks show a power law or scale free distribution of connectivities. An inverse power law, N(k) approximately k(-3/2), for the number of vertices (genes) with k connections was observed for three different data sets from yeast. We propose network growth models based on gene duplication events. Simulations of these models yield networks with the same combination of global graphical properties that we inferred from the expression data.

Algorithms↗
PubMed2002

Defining the mandate of proteomics in the post-genomics era: workshop report.

Research in proteomics is the next step after genomics in understanding life processes at the molecular level. In the largest sense proteomics encompasses knowledge of the structure, function and expression of all proteins in the biochemical or biological contexts of all organisms. Since that is an impossible goal to achieve, at least in our lifetimes, it is appropriate to set more realistic, achievable goals for the field. Up to now, primarily for reasons of feasibility, scientists have tended to concentrate on accumulating information about the nature of proteins and their absolute and relative levels of expression in cells (the primary tools for this have been 2D gel electrophoresis and mass spectrometry). Although these data have been useful and will continue to be so, the information inherent in the broader definition of proteomics must also be obtained if the true promise of the growing field is to be realized. Acquiring this knowledge is the challenge for researchers in proteomics and the means to support these endeavors need to be provided. An attempt has been made to present the major issues confronting the field of proteomics and two clear messages come through in this report. The first is that the mandate of proteomics is and should be much broader than is frequently recognized. The second is that proteomics is much more complicated than sequencing genomes. This will require new technologies but it is highly likely that many of these will be developed. Looking back 10 to 20 years from now, the question is: Will we have done the job wisely or wastefully? This report summarizes the presentations made at a symposium at the National Academy of Sciences on February 25, 2002.

Computational Biology↗