Mark Daley

Circular contextual insertions/deletions with applications to biomolecular computation

Insertions and deletions of small circular DNA strands into long linear DNA strands are phenomena that happen frequently in nature and thus constitute an attractive paradigm for biomolecular computing. The paper presents a new model for DNA-based computation that involves circular as well as linear molecules, and that uses the operations of insertion and deletion. After introducing the formal model, we investigate its properties and prove in particular that the circular insertion/deletion systems are capable of universal computation. We also give the results of an experimental laboratory implementation of our model. This shows that rewriting systems of the circular insertion/deletion type are viable alternatives in DNA computation.

Template-guided DNA recombination

The family of stichotrichous ciliates have received a great deal of study due to the presence of scrambled genes in their genomes. The mechanism by which these genes are descrambled is of interest both as a biological process and as a model of natural computation. Several formal models of this process have been proposed, the most recent of which involves the recombination of DNA strands based on template guides. We generalize this template-guided DNA recombination model proposed by Prescott, Ehrenfeucht and Rozenberg to an operation on strings and languages. We then proceed to investigate the properties of this operation with the intention of viewing ciliate gene descrambling as a computational process.

Regulated RNA rewriting: Modelling RNA editing with guided insertion

RNA editing is an important alternative genetic processing event that is known to take place in all higher eukaryotes. We study a model of string rewriting based on the sophisticated RNA editing mechanism found in trypanosome kinetoplasts. We demonstrate basic properties of three principal variants of this model which we show to form a strict hierarchy in terms of expressive power. We also present a method and software for simulating real biological RNA editing via this model and apply the theoretical results to suggest real biological constraints on this process.

Families of languages defined by ciliate bio-operations

We investigate families of languages de%ned by closure under operations generalized from models of gene descrambling in stichotrichous ciliates. We speci%cally consider languages that are closed under the synchronized insertion and deletion operations as well as languages closed under the hairpin inversion (hi) operation. Biologically, this studies sets of genes that cannot be further descrambled. In addition, we show that every trio closed under hairpin inversion is also closed under the double loop with alternating direct pointers (dlad)-excision/reinsertion bio-operation.

On computational properties of template-guided DNA recombination

The stichotrichous ciliates have attracted the attention of both biologists and computer scientists due to the unique genetic mechanism of gene descrambling. It has been suggested that it would perhaps be possible to co-opt this genetic process and use it to perform arbitrary computations in vivo. Motivated by this idea, we study here some basic properties and the computational power of a formalization inspired by the template-guided recombination model of gene descrambling proposed by Ehrenfeucht, Prescott and Rozenberg. We demonstrate that the computational power of a system based on template-guided recombination is quite limited. We then extend template-guided recombination systems with the addition of “deletion contexts” and show that such systems have strictly greater computational power than splicing systems [1, 2].

DNA Computing: Models and Implementations

As the fabrication of integrated circuits continues to take place on increasingly smaller scales, we grow closer to several fundamental limitations on electronic computers. For many classes of problems, computing devices based on biochemical reactions present an attractive alternative to conventional computing paradigms. We present here a survey of the theory and implementation of biologically and biochemically based computers.

Segmentation of epithelium in H&E stained odontogenic cysts

An algorithm for the automated segmentation of epithelial tissue in digital images of histologic tissue sections of odontogenic cysts (cysts originating from residual odontogenic epithelium) is presented. The algorithm features an image standardization process that greatly reduces variation in luminance and chrominance between images due to variations in sample preparation. Segmentation of the epithelial regions of images uses an algorithm based on binary graph cuts where graph weights depend on probabilities obtained from colour histogram models of epithelium and stroma image regions. Algorithm training used a data set of 38 images of four types of odontogenic cyst and was tested using a separate data set of 35 images of the same four cyst types. The best parameters for the segmentation algorithm were determined using a response‐surface optimizer. The best parameter set resulted in an overall mean (± std. dev.) sensitivity of 91.5 ± 17% and overall mean specificity of 85.1 ± 18.6% on the training set. Particularly good results were obtained for dentigerous and odontogenic keratocysts for which the mean sensitivities/specificities were 91.9 ± 6.15%/97.4 ± 2.15% and 96.1 ± 1.98%/98.7 ± 3.16%, respectively. Our method is potentially applicable to many pathological conditions in similar tissues, such as skin and mucous membranes where there is a clear microscopic distinction between epithelium and connective tissues.

Gene co-citation networks associated with worker sterility in honey bees

BackgroundThe evolution of reproductive self-sacrifice is well understood from kin theory, yet our understanding of how actual genes influence the expression of reproductive altruism is only beginning to take shape. As a model in the molecular study of social behaviour, the honey bee Apis mellifera has yielded hundreds of genes associated in their expression with differences in reproductive status of females, including genes directly associated with sterility, yet there has not been an attempt to link these candidates into functional networks that explain how workers regulate sterility in the presence of queen pheromone. In this study we use available microarray data and a co-citation analysis to describe what gene interactions might regulate a worker’s response to ovary suppressing queen pheromone.ResultsWe reconstructed a total of nine gene networks that vary in size and gene composition, but that are significantly enriched for genes of reproductive function. The networks identify, for the first time, which candidate microarray genes are of functional importance, as evidenced by their degree of connectivity to other genes within each of the inferred networks. Our study identifies single genes of interest related to oogenesis, including eggless, and further implicates pathways related to insulin, ecdysteroid, and dopamine signaling as potentially important to reproductive decision making in honey bees.ConclusionsThe networks derived here appear to be variable in gene composition, hub gene identity, and the overall interactions they describe. One interpretation is that workers use different networks to control personal reproduction via ovary activation, perhaps as a function of age or environmental circumstance. Alternatively, the multiple networks inferred here may represent segments of the larger, single network that remains unknown in its entirety. The networks generated here are provisional but do offer a new multi-gene framework for understanding how honey bees regulate personal reproduction within their highly social breeding system.

HD-CNV

SUMMARY Copy number variants (CNVs) are a major source of genetic variation. Comparing CNVs between samples is important in elucidating their potential effects in a wide variety of biological contexts. HD-CNV (hotspot detector for copy number variants) is a tool for downstream analysis of previously identified CNV regions from multiple samples, and it detects recurrent regions by finding cliques in an interval graph generated from the input. It creates a unique graphical representation of the data, as well as summary spreadsheets and UCSC (University of California, Santa Cruz) Genome Browser track files. The interval graph, when viewed with other software or by automated graph analysis, is useful in identifying genomic regions of interest for further study. AVAILABILITY AND IMPLEMENTATION HD-CNV is an open source Java code and is freely available, with tutorials and sample data from http://daleylab.org. CONTACT jcamer7@uwo.ca

Structure and function of gene regulatory networks associated with worker sterility in honeybees.

Abstract A characteristic of eusocial bees is a reproductive division of labor in which one or a few queens monopolize reproduction, while her worker daughters take on reproductively altruistic roles within the colony. The evolution of worker reproductive altruism involves indirect selection for the coordinated expression of genes that regulate personal reproduction, but evidence for this type of selection remains elusive. In this study, we tested whether genes coexpressed under queen‐induced worker sterility show evidence of adaptive organization within a model brain transcriptional regulatory network (TRN). If so, this structured pattern would imply that indirect selection on nonreproductive workers has influenced the functional organization of genes within the network, specifically to regulate the expression of sterility. We found that literature‐curated sets of candidate genes for sterility, ranging in size from 18 to 267, show strong evidence of clustering within the three‐dimensional space of the TRN. This finding suggests that our candidate sets of genes for sterility form functional modules within the living bee brains TRN. Moreover, these same gene sets colocate to a single, albeit large, region of the TRNs topology. This spatially organized and convergent pattern contrasts with a null expectation for functionally unrelated genes to be haphazardly distributed throughout the network. Our meta‐genomic analysis therefore provides first evidence for a truly “social transcriptome” that may regulate the conditional expression of honeybee worker sterility.