Negative information for motif discovery.

We discuss a method of combining genome-wide transcription factor binding data, gene expression data, and genome sequence data for the purpose of motif discovery in S. cerevisiae. Within the word-counting algorithmic approach to motif discovery, we present a method of incorporating information from negative intergenic regions where a transcription factor is thought not to bind, and a statistical significance measure which account for intergenic regions of different lengths. Our results demonstrate that our method performs slightly better than other motif discovery algorithms. Finally, we present significant potential new motifs discovered by the algorithm.

Serial regulation of transcriptional regulators in the yeast cell cycle.

Genome-wide location analysis was used to determine how the yeast cell cycle gene expression program is regulated by each of the nine known cell cycle transcriptional activators. We found that cell cycle transcriptional activators that function during one stage of the cell cycle regulate transcriptional activators that function during the next stage. This serial regulation of transcriptional activators forms a connected regulatory network that is itself a cycle. Our results also reveal how the nine transcriptional regulators coordinately regulate global gene expression and diverse stage-specific functions to produce a continuous cycle of cellular events. This information forms the foundation for a complete map of the transcriptional regulatory network that controls the cell cycle.

Blazing pathways through genetic mountains.

It is now widely accepted that high-throughput data sources will shed essential understanding on the inner workings of cellular and organism function. One key challenge is to distill the results of such experiments into an interpretable computational form that will be the basis of a predictive model. A predictive model represents the gold standard in understanding a biological system and will permit us to investigate the underlying cause of diseases and help us to develop therapeutics. Here I explore how discoveries can be based on high-throughput data sources and discuss how independent discoveries can be assembled into a comprehensive picture of cellular function.

Fast optimal leaf ordering for hierarchical clustering.

We present the first practical algorithm for the optimal linear leaf ordering of trees that are generated by hierarchical clustering. Hierarchical clustering has been extensively used to analyze gene expression data, and we show how optimal leaf ordering can reveal biological structure that is not observed with an existing heuristic ordering method. For a tree with n leaves, there are 2(n-1) linear orderings consistent with the structure of the tree. Our optimal leaf ordering algorithm runs in time O(n(4)), and we present further improvements that make the running time of our algorithm practical.

Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.

We propose a model-driven approach for analyzing genomic expression data that permits genetic regulatory networks to be represented in a biologically interpretable computational form. Our models permit latent variables capturing unobserved factors, describe arbitrarily complex (more than pair-wise) relationships at varying levels of refinement, and can be scored rigorously against observational data. The models that we use are based on Bayesian networks and their extensions. As a demonstration of this approach, we utilize 52 genomes worth of Affymetrix GeneChip expression data to correctly differentiate between alternative hypotheses of the galactose regulatory network in S. cerevisiae. When we extend the graph semantics to permit annotated edges, we are able to score models describing relationships at a finer degree of specification.

Design and implementation of computational systems based on programmed mutagenesis.

In a new system for DNA computation called programmed mutagenesis, DNA strands are rewritten according to 'rules' that are sequence specific. In a programmed mutagenesis reaction DNA sequences undergo programmed changes, and these changes are implemented by an in-vitro mutagenesis system that is based on thermal cycling the rewriting reaction. We describe experimental results that demonstrate the key aspects of programmed mutagenesis. These results include the creation of full-length product DNA molecules that have embedded rewriting, the ability for later sequence changes to depend on earlier sequence changes, and the ability for multiple oligonucleotides to be active in close proximity on a template sequence. We also discuss the application of programmed mutagenesis to computational problems.

Automated constraint-based nucleotide sequence selection for DNA computation.

We present techniques for automating the design of computational systems built using DNA, given a set of high-level constraints on the desired behavior and performance of the system. We have developed a program called SCAN that exploits a previously implemented computational melting temperature primitive to search a 'nucleotide space' for sequences satisfying a pre-specified set of constraints, including hybridization discrimination, primer 5' end and 3' end stability, secondary structure reduction, and prevention of oligonucleotide dimer formation. The first version of SCAN utilized 24 h of computer time to search a space of over 7.5 billion unary counter designs and found only nine designs satisfying all of the pre-specified constraints. One of SCAN's designs has been implemented in the laboratory and has shown a marked improvement in performance over the products of previous attempts at manual design. We conclude with some novel ideas for improving the overall speed of the program that offer the promise of an efficient method for selecting optimal nucleotide sequences in an automated fashion.

Monthly incidence of stroke in rural Kansas.

The relationship between the principal clinical diagnosis of cerebrovascular accident (CVA) and the month of admission to a regional medical center in Northwestern Kansas was investigated. The retrospective study identified 472 charts of subjects that had been admitted to a rural regional medical center in Northwestern Kansas over a six year period with the principal clinical diagnosis of stroke. Spearman's correlation coefficients indicated there was no significant relationship between the total number of admissions per month or by number of admissions in each classification of disease coding group. The study demonstrated a peak occurrence in July (n = 55) and the lowest occurrence in September (n = 25). These findings indicate that environmental factors characteristic of Northwestern Kansas may have no effect on the occurrence of stroke.