Engineering topics

Taylor, Ronald C

Publications and source records attributed to Taylor, Ronald C.

7 records

DOE OSTI2015

Genomic expansion of domain archaea highlights roles for organisms from new phyla in anaerobic carbon cycling.

Archaea represent a significant fraction of Earth's biodiversity, yet they remain much less well understood than Bacteria. Gene surveys, a few metagenomic studies, and some single-cell sequencing projects have revealed numerous little-studied archaeal phyla. Certain lineages appear to branch deeply and may be part of a major phylum radiation. The structure of this radiation and the physiology of the organisms remain almost unknown.We used genome-resolved metagenomic analyses to investigate the diversity, genomes sizes, metabolic capacities, and potential roles of Archaea in terrestrial subsurface biogeochemical cycles. We sequenced DNA from complex sediment and planktonic consortia from an aquifer adjacent to the Colorado River (USA) and reconstructed the first complete genomes for Archaea using cultivation-independent methods. To provide taxonomic context, we analyzed an additional 151 newly sampled archaeal sequences. We resolved two new phyla within a major, apparently deep-branching group of phyla (a superphylum). The organisms have small genomes, and metabolic predictions indicate that their primary contributions to Earth's biogeochemical cycles involve carbon and hydrogen metabolism, probably associated with symbiotic and/or fermentation-based lifestyles.The results dramatically expand genomic sampling of the domain Archaea and clarify taxonomic designations within a major superphylum. This study, in combination with recently published work on bacterial phyla lacking cultivated representatives, reveals a fascinating phenomenon of major radiations of organisms with small genomes, novel proteome composition, and strong interdependence in both domains.

Castelle, Cindy J↗

DOE OSTI2012

Modeling Dynamic Regulatory Processes in Stroke.

The ability to examine in silico the behavior of biological systems can greatly accelerate the pace of discovery in disease pathologies, such as stroke, where in vivo experimentation is lengthy and costly. In this paper we describe an approach to in silico examination of blood genomic responses to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) relating regulators and functional clusters from the data. These ODEs were used to develop dynamic models that simulate the expression of regulated functional clusters using system dynamics as the modeling paradigm. The dynamic model has the considerable advantage of only requiring an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. The manipulation of input model parameters, such as changing the magnitude of gene expression, made it possible to assess the behavior of the networks through time under varying conditions. We report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different preconditioning paradigms.

dynamic modeling↗

DOE OSTI2011

Data Standards for Omics Data: the Basis of Data Sharing and Reuse.

To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Modular Semantic Tagging of Medline Abstracts and its Use in Inferring Regulatory Networks

We describe MedstractPlus, a resource for mining relations from the Medline bibliographic database that is currently under construction. It was built on the remains of Medstract, a previously created resource that included a biorelation server and an acronym database. MedstractPlus uses simple and scalable natural language processing modules to structure text, is designed with reusability and extendibility in mind, and adheres to the philosophy of the Linguistic Annotation Framework. We show how MedstractPlus has been used to provide seeds for a novel approach to inferring transcriptional regulatory networks from gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant links between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Reverse Engineering Adverse Outcome Pathways

The toxicological effects of many stressors are mediated through unknown, or poorly characterized, mechanisms of action. We describe the application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows. Gene expression changes in fathead minnow ovaries in response to 7 different chemicals, over different times, doses, and in vivo versus in vitro conditions were captured in a large data set of 868 arrays. We examined potential AOPs of the antiandrogen flutamide using two mutual information theory methods, ARACNE and CLR to infer gene regulatory networks and potential adverse outcome pathways. Representative networks from these studies were used to predict a network path from stressor to adverse outcome as a candidate AOP. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biologic processes, biomarkers or alternative endpoints, which could be used to monitor an adverse outcome pathway. Finally, we identify the unique challenges facing the application of this approach in ecotoxicology, and attempt to provide a road map for the utilization of these tools. Key Words: mechanism of action, toxicology, microarray, network inference

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2010

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics

Bioinformatics researchers are increasingly confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date.

43 PARTICLE ACCELERATORS↗