http://precedings.nature.com/tags/modeling Recently posted documents tagged with 'modeling' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3967.1 Increasing public engagement in volunteer science1, either through data collection2 or processing3, is both raising public awareness of science and gathering useful information for scientists. While the payoffs of citizen science4 are potentially large, achieving them requires new approaches to data management and analysis that can only result from strong cross-disciplinary collaborations. This is especially true in ecology and conservation biology, where historically the understanding of species’ responses to environmental change has been constrained by the limited spatial5 or temporal scale6 of available data. Here we describe collaborative research in ecology, computer science, and statistics to generate essential information for conservation management of North American birds: accurate dynamic bird distributions models based on habitat associations across much of North America. Unique is our ability to describe the broad-scale dynamics of seasonal bird distributions and the associated seasonal patterns of habitat use. Our source of bird distribution data is eBird7, an online bird checklist program that currently gathers more than 74,000 checklists monthly from a large network of contributors. Our results were made possible through a data intensive scientific workflow8 that includes analytical methods merged from the fields of machine learning and statistics. We believe that this novel approach of data collection, synthesis, analysis, and visualization will serve as a hallmark for future research initiatives, with broad applicability across many scientific domains. http://dx.doi.org/10.1038/npre.2009.3967.1 Fri, 13 Nov 2009 15:05:44 UTC Obtaining New Insights for Biodiversity Conservation from Broad-Scale Citizen Science Data doi:10.1038/npre.2009.3967.1 2009-11-13 Steve Kelling Nature Precedings 2009-11-13T15:05:44Z Manuscript Ecology http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3776/version/1 The emergence of tuberculosis resistant to multiple, first- and second-line antibiotics poses challenges to a global control strategy that relies on standard drug treatment regimens. The high drug-resistant strains of Mycobacterium tuberculosis (Mtb) have been implicated in outbreaks and have been found throughout the world; a comprehensive understanding, the magnitude of this threat requires an accurate assessment of the worldwide burden of resistance. In an attempt to design anti-TB drugs, the target chosen is a key enzyme of Mtb, O-Succinylbenzoate synthase (OSBS), which is an attractive target for its role in electron transport chain as OSBS is not available in humans. An attempt has been to built the 3-D structure of Mtb-OSBS using online Swiss model server. With sequence alignment and scan motif identification, the importance of evolutionarily significant residues that are of functional importance for ligand binding and that form active sites were well established. Molecular simulation calculations of Mtb-OSBS model indicated evolutionarily conserve residues (Lys110 and Lys212) are the best in molecular interaction with substrate 2-succinyl-6-hydroxy-2,4-cyclohexadiene-1-carboxylate (SHCHC). The in silico mutational analysis of Mtb-OSBS model showed the evolutionarily conserved residues that are essential for catalytic activity. It has been found that active site amino acids of Mtb-OSBS are very important to maintain activity of the enzyme, which provides a novel approach to design new pharmacophore SHCHC substrate analogs against Mtb-OSBS. A series of SHCHC substrate analogs (1–100) compounds have been docked with the amino acid residues at the active site of the Mtb-OSBS enzyme, using AutoDock 4.0, a program employed to perform automated molecular docking. The free energies of binding (∆G) and inhibition constants (Ki) of the docked compounds were calculated by the Lamarckian Genetic Algorithm (LGA). Excellent to good correlations between the calculated and experimental Ki values were reported. http://precedings.nature.com/documents/3776/version/1 Fri, 18 Sep 2009 16:35:16 UTC Prediction of Evolutionarily important catalytic amino acid of Mycobacterium tuberculosis O-Succinylbenzoate synthase through in silico mutational analysis hdl:10101/npre.2009.3776.1 2009-09-18 Suresh Kumar Chitta Nature Precedings 2009-09-18T16:35:16Z Poster Biotechnology Microbiology Pharmacology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3721.1 Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps. http://dx.doi.org/10.1038/npre.2009.3721.1 Mon, 07 Sep 2009 07:52:01 UTC Systems Biology Graphical Notation: Process Description language Level 1 doi:10.1038/npre.2009.3721.1 2009-09-07 Stuart L. Moodie Nature Precedings 2009-09-07T07:52:01Z Manuscript Biotechnology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3719.1 Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Descriptions, the Entity Relationships and the Activity Flows. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Entity Relationship language has been publicly released. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps. http://dx.doi.org/10.1038/npre.2009.3719.1 Fri, 04 Sep 2009 15:21:22 UTC Systems Biology Graphical Notation: Entity Relationship language Level 1 doi:10.1038/npre.2009.3719.1 2009-09-04 Nicolas Le Novère Nature Precedings 2009-09-04T15:21:22Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3633.1 Background/Question/MethodsMicro-organisms, including Bacteria, Archaea, and Fungi, control major processes throughout the Earth system. Recent advances in microbial ecology and microbiology have revealed an astounding level of genetic and metabolic diversity in microbial communities. However, a framework for interpreting the meaning of this diversity has lagged behind the initial discoveries. Microbial communities have yet to be included explicitly in any major biogeochemical models in terrestrial ecosystems, and have only recently broken into ocean models. Although simplification of microbial communities is essential in complex systems, omission of community parameters may seriously compromise model predictions of biogeochemical processes. Two key questions arise from this tradeoff: 1) When and where must microbial community parameters be included in biogeochemical models? 2) If microbial communities are important, how should they be simplified, aggregated, and parameterized in models? To address these questions, a literature survey was conducted to determine if microbial communities are sensitive to four environmental disturbances that are associated with global change. Results/ConclusionsFor all environmental perturbations, community composition changed significantly following disturbance. However, the implications for ecosystem function were unclear in most of the published studies. Therefore, I developed a simple model framework to illustrate the situations in which microbial community changes would affect rates of biogeochemical processes. These scenarios could be quite common, but powerful predictive models cannot be developed without much more information on the functions and disturbance responses of microbial taxa. Small-scale models that explicitly incorporate microbial communities also suggest that process rates strongly depend on microbial interactions and disturbance responses. The challenge is to scale up these models to make predictions at the ecosystem and global scales based on measurable parameters. Meeting this challenge will require a coordinated effort to develop a series of nested models at scales ranging from the micron to the globe in order to optimize the tradeoff between model realism and feasibility. http://dx.doi.org/10.1038/npre.2009.3633.1 Mon, 17 Aug 2009 17:22:01 UTC Integration of microbial communities into large-scale ecosystem models doi:10.1038/npre.2009.3633.1 2009-08-17 Steven D. Allison Nature Precedings 2009-08-17T17:22:01Z Presentation Ecology Earth & Environment http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3555.1 Models of epidemics allow decision makers to explore the consequences of different interventions. The Models of Infectious Disease Agent Study (MIDAS) project has been collecting studies, models, data supporting the models, and publications providing historical evidence about epidemics.An ontology has been developed for MIDAS to support the collection, documentation, and dissemination of models. It uses relations to link taxonomies (including a subset of the infectious disease ontology) that define the scope of its models and supporting documentation.The ontology is used to aid in the navigation process that is part of the user interface for identifying which studies and publications are available in the MIDAS repository (MREP) that are consistent with the many parameters associated with a particular study. http://dx.doi.org/10.1038/npre.2009.3555.1 Fri, 07 Aug 2009 09:09:20 UTC An Ontology for Designing Models of Epidemics doi:10.1038/npre.2009.3555.1 2009-08-07 Geoffrey Frank Nature Precedings 2009-08-07T09:09:20Z Presentation Immunology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3432/version/2 Expression of the Arabidopsis HARDY (hrd) DNA binding transcription factor (555 bp present on chromosome 2) has been shown to increase WUE in rice by Karaba et al 2007 (PNAS, 104:15270–15275). We conducted a detail analysis of the complete sorghum genome for the similarity/presence of either DNA, mRNA or protein product of the Arabidopsis HARDY (hrd) DNA binding transcription factor (555 bp present on chromosome 2). Chromosome 6 showed a sequence match of 61.5 percent positive between 61 and 255 mRNA residues of the query region. Further confirmation was obtained by TBLASTN which showed that chromosome 6 of the sorghum genome has a region between 54948120 and 54948668 which has 80 amino acid similarities out of the 185 residues. A homology model was constructed and verified using Anolea, Gromos and Verify3D. Scanning the motif for possible activation sites revealed that there was a protein kinase C phosphorylation site between 15th and 20th residue. The study indicates the possibility of the presence of a DNA binding transcription factor in chromosome 6 of Sorghum bicolor with 60 percent similarity to that of Arabidopsis hrd DNA binding transcription factor. http://precedings.nature.com/documents/3432/version/2 Mon, 20 Jul 2009 09:09:06 UTC In silico analysis for the presence of HARDY an Arabidopsis drought tolerance DNA binding transcription factor product in chromosome 6 of Sorghum bicolor genome hdl:10101/npre.2009.3432.2 2009-07-20 Arun K. Shanker Nature Precedings 2009-07-20T09:09:06Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3156.2 Harnessing cognitive abilities of many individuals, a language evolves upon their mutual interactions establishing a persistent social environment to which language is closely attuned. Human history is encoded in the rich sets of linguistic data by means of symmetry patterns that are not always feasibly represented by trees. Here we use the methods developed in the study of complex networks to decipher accurately symmetry records on the language phylogeny of the Indo-European and the Austronesian language families, considering, in both cases, the samples of fifty different languages. In particular, we support the Anatolian theory of Indo-European origin and the ‘express train’ model of Austronesian expansion from South-East Asia, with an essential role for the Batanes islands located between the Philippines and Taiwan. http://dx.doi.org/10.1038/npre.2009.3156.2 Wed, 27 May 2009 22:44:06 UTC Networking Phylogeny for Indo-European and Austronesian Languages doi:10.1038/npre.2009.3156.2 2009-05-27 Dimitri Volchenkov Nature Precedings 2009-05-27T22:44:06Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3249.1 As the first phase of a knowledge engineering study of biocuration workflows, we performed a preliminary task-modeling exercise on seven separate bioinformatics systems. This involved constructing UML activity diagrams from detailed interviews with curators in order to understand the organization of the process the biocurators used to populate their system. The objective of this work was to identify common patterns within the workflows where we might apply text mining methods to accelerate curation. We compiled a number of workflows in a common format but were largely unable to consolidate these structures into a formal structure that facilitated comparison across workflows. At present, more work is needed to perform this task. http://dx.doi.org/10.1038/npre.2009.3249.1 Thu, 14 May 2009 21:28:32 UTC Studying Biocuration Workflows doi:10.1038/npre.2009.3249.1 2009-05-14 Gully A. P. C. Burns Nature Precedings 2009-05-14T21:28:32Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3156/version/1 Harnessing cognitive abilities of many individuals, a language evolves upon their mutual interactions establishing a persistent social environment to which language is closely attuned. Human history is encoded in the rich sets of linguistic data by means of symmetry patterns that are not always feasibly represented by trees. Here we use the methods developed in the study of complex networks to decipher accurately symmetry records on the language phylogeny of the Indo-European and the Austronesian language families, considering, in both cases, the samples of fifty different languages. In particular, we support the Anatolian theory of Indo-European origin and the ‘express train’ model of Austronesian expansion from South-East Asia, with an essential role for the Batanes islands located between the Philippines and Taiwan. http://precedings.nature.com/documents/3156/version/1 Fri, 24 Apr 2009 00:15:36 UTC Networking Phylogeny for Indo-European and Austronesian Languages hdl:10101/npre.2009.3156.1 2009-04-24 Dimitri Volchenkov Nature Precedings 2009-04-24T00:15:36Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/