Gavin Conant

The application of network models to biological systems has helped elucidate the structure of the genotype to phenotype map (4). However, we still do not understand the rules governing the associations between genes that shape their evolution on timescales of millions of years. A key type of such association are dosage interactions, whereby one gene���s function is compromised by insufficient or excessive copies of another. The importance of such interactions is illustrated by the diseases of copy number (5-8) and the detrimental effects of aneuploidy across eukaryotes (9). Aneuploidy, including that responsible for Down syndrome, is harmful not because of the excess of the genes on the aneuploid chromosome but because that excess creates imbalances between those genes and their partners on other chromosomes. These same dosage effects drive in the convergent response of genomes from across the eukaryotic tree to polyploidy, or genome doubling (1, 10, 11). After independent polyploidies in yeasts, vertebrates, and angiosperms, we see a convergent return to single copy of DNA repair genes (12-14) and the convergent retention of duplicate genes coding for transcription factors, ribosomal proteins and kinases (14-19).

Bovine respiratory disease (BRD) is an economically important and sometimes fatal disease of cattle. It is the most common cause of mortality in cattle in both the US and Ireland. The proposal seeks to combine resources and expertise across the US and Ireland for the evaluation of BRD. The objectives of this proposal are: 1) investigate the prevalence and distribution of species in the respiratory microbiome and virome associated with BRD in beef and dairy herds in the US and Ireland; 2) employ next-generation sequencing, bioinformatic technologies, and high throughput sensitive and rapid diagnostics to identify respiratory viral and bacterial agents associated with BRD; and 3) elucidate the dynamics of secondary viral and bacterial infection by monitoring experimentally virus infected animals in longitudinal studies.

Rapid global changes are placing unprecedented pressure on plants in agricultural and natural landscapes. This project explores a poorly understood area of plant biology: how genome and biological complexity are related and how that relationship can be manipulated to design responses such as increased tolerance to abiotic and biotic stresses for agricultural applications. The future promises rich synthetic biology applications, if the next generation of scientists is appropriately trained. To address this gap, this project will provide research training in systems biology and predictive modeling for postdoctoral fellows, and graduate and undergraduate students. This team will partner with computer scientists, modeling experts, and journalism students to provide a vision of integrated systems biology for the new century. All research and outreach activities proposed are integrated with recruiting and mentoring students from underrepresented groups. This project will expose trainees and other students through daily dialogues, joint seminars, team-taught courses, and other venues. In addition, this project will develop novel computational and genomic methods that will start to integrate genotype with phenotype, thus transforming comparative biology. Access to all data, computational tools and resources generated in this project will be provided to the broader research community through long-term repositories and through CABBAGE: Community Assets for Brassica Biology And Genome Evolution, a web portal that will be integrated with the cyberinfrastructure developed by the NSF-supported iPlant Collaborative. Brassica crops have tremendous morphological and chemical diversity and are ideal for studying domestication and other economically important plant processes. The goal of this project is to explore the relationship between polyploidy (the merger and doubling of two genomes) and plasticity using the crop Brassicas. By integrating comparative genomics, networks, and genetic models, this project confronts the key question "Did a whole genome triplication in the crop Brassicas facilitate their domestication and adaptability?". This project leverages the systems biology and -omics resources of Arabidopsis to focus on two hypotheses regarding how whole genome triplication (WGT) affected the Brassicas. The first is whether polyploidy in the crop Brassicas are associated with global alterations to the metabolic and gene expression networks, possibly allowing faster growth through duplication of core, high-flux, enzymes. The second is whether polyploidy and subsequent domestication by human farmers altered the networks related to biotic stress in the crop Brassicas. The specific objectives are to: (1) map post-polyploid duplicated genes using comparative genomics (across species of Brassica and Arabidopsis) and identify post-polyploid changes in gene co-expression and metabolism; (2) search for signatures of selection and recent adaptations to biotic stress during the parallel domestications of Brassica oleracea (broccoli, cabbage, cauliflower, Brussels sprouts, and kohlrabi) and B. rapa (turnips, Chinese cabbage, Pak Choi, and oilseeds); and (3) survey gene expression and glucosinolate levels in F1 hybrids of B. oleracea morphotypes to identify functional elements in genetic response to stress. Analyses of genome organization and gene expression will improve the current understanding of the genetic basis of metabolic innovation. Analyses of transcriptome and metabolic data will identify the types of variation selected during domestication and describe the resulting changes to metabolism, growth, and biotic stress response. Further, this project will describe the evolution of gene expression patterns in Brassica and test genome-scale models of metabolic networks in Brassica, which will be refined with glucosinolate measurements.

Thanks to recent advances in DNA sequencing technology, a number of genomic analysis tasks - such as reference-based and de novo sequence assembly, taxa identifications in metagenomic sequences, orthology inference and regulatory motif search - can nowadays operate on increasingly large volumes of data. All these applications perform, at their core, some kind of pattern matching operations, a computation that maps naturally onto finite automata abstractions. It has been shown that large scale automata processing can be efficiently accelerated on streaming architectures such as Field Programmable Gate Arrays (FPGA). However, the low level programming interface of these devices has hampered their widespread adoption within the bioinformatics community. As an alternative, Micron Technology has recently announced its SDRAM-based Automata Processor (AP), which will come with an automata-based programming interface. However, the position that this emerging technology will take in the realm of existing streaming accelerators is unclear: in particular, its capabilities in handling big data and diverse computations as well as its programmability must still be understood. In this research we aim to study novel programmatic descriptions of several genomic analysis tasks obtained by re-describing each operation using an automata-based programming model, and map such this programming model onto FPGA platforms and onto Micron������������������s AP. Our goal is two-fold: on one hand, we aim to facilitate the adoption of these accelerators within the scientific community; on the other, we seek to investigate the benefits and limitations of these technologies when targeting a variety of pattern matching operations at large scale.