| Accepted Posters |
| Category 'R'- Systems Biology and Networks' |
| Poster R01 |
| Quantitative systems-level determinants of drug targets |
| Lixia Yao- Columbia University |
| No additional authors |
| Short Abstract: We identified several quantitative determinants that distinguish drug targets from other genes and proteins at a highly significant level, and built a machine-learning model to demonstrate the usefulness of these quantitative descriptors for predicting drug targets. We hope that our methods can be immediately useful to experimentalists in drug discovery. |
| Long Abstract: Click Here |
| Poster R02 |
| Strategic method for finding signaling pathways that control drug-related gene networks |
| Yoshinori Tamada- Human Genome Center, Institute of Medical Science, University of Tokyo |
| Seiya Imoto (University of Tokyo, Human Genome Center, Institute of Medical Science); Hiromitsu Araki (GNI Ltd., Collaboration Research Department); Masao Nagasaki (University of Tokyo, Human Genome Center, Institute of Medical Science); Rui Yamaguchi (University of Tokyo, Human Genome Center, Institute of Medical Science); Yuki Tomiyasu (GNI Ltd., Collaboration Research Department); Kaori Yasuda (GNI Ltd., Collaboration Research Department); Cristin Print (University of Auckland, Department of Molecular Medicine & Pathology); Stephen Charnock-Jones (University of Cambridge, Department of Obstetrics and Gynaecology); Kousuke Tashiro (Kyushu University, Faculty of Agriculture); Satoru Kuhara (Kyushu University, Faculty of Agriculture); Satoru Miyano (University of Tokyo, Human Genome Center, Institute of Medical Science); |
| Short Abstract: Recently, much attention has been focused on predicting mode-of-action of a drug by gene network analyses. Signaling pathways, however, are not well-studied in this direction due to the lack of comprehensive protein profiling technology. Here we present a computational method for finding signaling pathways affecting and controlling drug-respond gene networks. |
| Long Abstract: Click Here |
| Poster R03 |
| Stochastic models for inferring genetic regulation from microarray gene expression data |
| Tianhai Tian- University of Glasgow |
| No additional authors |
| Short Abstract: This work is aimed at investigating the quantitative relationship between the noise in gene expression data and noise strength of stochastic models. Numerical results indicate that parameters related to the noise strength in stochastic models should be determined by the variance of microarray expression data rather than as adjustable parameters. |
| Long Abstract: Click Here |
| Poster R04 |
| Casting a net for kinases - Systematic Discovery of Cellular Phosphorylation Networks |
| Rune Linding- Institute of Cancer Research |
| Lars Juhl Jensen (EMBL, Biocomputing); Gerald Ostheimer (MIT, CCR); Mike Yaffe (MIT, CCR); Tony Pawson (SLRI, Pawson Lab); Peer Bork (EMBL, Biocomputing); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R05 |
| Using conserved protein interactions across eukaryotes to construct a predicted plant interactome. |
| Matt Geisler- Southern Illinois University Carbondale |
| Jane Geisler-Lee (Southern Illinois University Carbondale, Plant Biology); Nicholas O'Toole (University of Western Australia, Australian Research Council Centre of Excellence in Plant Energy Biology); Ron Ammar (University of Toronto, Cell and Systems Biology); Nicholas Provart (University of Toronto, Cell and Systems Biology); A. Harvey Millar (University of Western Australia, Australian Research Council Centre of Excellence in Plant Energy Biology); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R06 |
| Clustering by common friends finds locally significant proteins mediating modules |
| Bill Andreopoulos- Bioteczentrum TU Dresden |
| Aijun An (York University Toronto, CSE); Xiaogang Wang (York University Toronto, Maths Stats); Michalis Faloutsos (UC Riverside, CS); Michael Schroeder (TU Dresden, Biotec & CS); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R07 |
| The utilization of redundancy by genetic networks. |
| Ran Kafri- Harvard Medical School |
| Orna Dahan (Weizmann Institute of Science, Molecular Genetics); Yitzhak Pilpel (Weizmann Institute if Science, Molecular Genetics); Jonathan Levy (University of Western Ontario, non); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R08 |
| Unequal evolutionary conservation of human protein interactions in interologous networks |
| Kevin Brown- University of Toronto |
| Igor Jurisica (University of Toronto, Department of Medical Biophysics, Department of Computer Science); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R09 |
| Cerebral 2.0: A Cytoscape plugin for the network-based visualization of datasets from multiple experimental conditions |
| Jennifer Gardy- University of British Columbia |
| Aaron Barsky (University of British Columbia, Computer Science); Robert Kincaid (Agilent , Agilent Laboratories); Tamara Munzner (University of British Columbia, Computer Science); Robert Hancock (University of British Columbia, Centre for Microbial Diseases & Immunity Research); |
| Short Abstract: Cerebral allows users to visualize and overlay quantitative data on a localization-based biomolecular interaction network. “Small multiple” and “difference” views colour the network in ways that facilitate the observation of trends and outliers, while a “parallel coordinates” view offers profile-based visualization and interactive clustering. |
| Long Abstract: Click Here |
| Poster R10 |
| Functional maps of protein complexes from quantitative genetic interaction data |
| Sourav Bandyopadhyay- University of California - San Diego |
| Ryan Kelley (UCSD, Bioengineering); Trey Ideker (UCSD, Bioengineering); Nevan Krogan (UCSF, Cellular and Molecular Pharmacology); Andreas Beyer (TU-Dresden, BIOTEC); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R11 |
| Linear motif atlas for phosphorylation-dependent signaling |
| Martin Miller- The Technical University Of Denmark |
| Lars Jensen (EMBL, Heidelberg); Marina Olhovsky (Samuel Lunenfeld Research Institute, Mount Sinai Hospital); Tony Pawson (Samuel Lunenfeld Research Institute, Mount Sinai Hospital); Michael Yaffe (Center for Cancer Research, MIT); Søren Brunak (Center for Biological Sequence Analysis, Technical University of Denmark); Rune Linding (Samuel Lunenfeld Research Institute, Mount Sinai Hospital); Peer Bork (EMBL, Heidelberg); |
| Short Abstract: To support the proteome-wide characterization of interactions modulated by post-translational modifications, we have developed the NetPhorest resource, an atlas of linear motifs that contains classifiers for 179 kinases and 97 phosphotyrosine-binding modules. These reveal that tyrosine kinases mutated in cancer have lower recognition specificity than their non-oncogenic siblings. |
| Long Abstract: Click Here |
| Poster R12 |
| Combining comparative genomics with data integration for the prediction of potential pathogenicity genes |
| Jan Taubert- Rothamsted Research |
| Catherine Canevet (Rothamsted Research, Biomathematics And Bioinformatics); Rainer Winnenburg (Dresden University of Technology, Biotechnology Center); Kim Hammond-Kosack (Rothamsted Research, Plant Pathology & Microbiology); Christopher Rawlings (Rothamsted Research, Biomathematics And Bioinformatics); |
| Short Abstract: Adding annotations and accurate functional predictions to coding sequences identified in high-throughput genome data remains a challenge for bioinformatics. We combine comparative genome analysis with data integration methods in ONDEX to demonstrate how information on experimentally verified pathogenicity identified from the scientific literature can be used to predict pathogenicity genes. |
| Long Abstract: Click Here |
| Poster R13 |
| From data to knowledge – the ONDEX System for integrating Life Sciences data sources |
| Catherine Canevet- Rothamsted Research |
| Christopher Rawlings (Rothamsted Research, Biomathematics And Bioinformatics); Angela Karp (Rothamsted Research, Plant and Invertebrate Ecology); Carole Goble (University of Manchester, School of Computer Science); Robert Stevens (University of Manchester, School of Computer Science); Sophia Ananiadou (University of Manchester, School of Computer Science); Douglas Kell (University of Manchester, School of Chemistry); Anil Wipat (University of Newcastle, School of Computing Science); Darren Wilkinson (University of Newcastle, School of Mathematics and Statistics); Phillip Lord (University of Newcastle, School of Computing Science); David Lydall (University of Newcastle, Faculty of Medical Sciences); |
| Short Abstract: We describe the development of an open-source extensible semantic data integration framework for supporting systems biology research based on the ONDEX system (http://ondex.sf.net). The poster presents the ONDEX workflow, from data input to data integration and analysis, and gives an overview of future developments. |
| Long Abstract: Click Here |
| Poster R14 |
| Linking gene expression and functional network data in human heart failure |
| Anyela Camargo- University of Ulster at Jordanstown |
| No additional authors |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R15 |
| A systems biology approach for steady state analysis of signaling pathways |
| Sorin Draghici- Wayne State University |
| Purvesh Khatri (Wayne State University, Computer Science); Adi Laurentiu Tarca (Wayne State University, Perinatology Research Branch); Kashyap Amin (Wayne State University, Computer Science); Arina Done (Wayne State University, Computer Science); Calin Voichita (Wayne State University, Computer Science); Constantin Georgescu (Wayne State University, Computer Science); Roberto Romero (Wayne State University, Perinatology Research Branch); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R16 |
| Mining co-operative regulation networks from expression data |
| Elati Mohamed- LIPN / Institut Curie |
| Céline Rouveirol (LIPN, Institut Galilée); François Radvanyi (Institut Curie, UMR 144); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R17 |
| Discovering Geometric Structure in Protein-Protein Interaction Networks |
| Natasa Przulj- UC Irvine |
| Marija Rasajski (UC Irvine, Irvine, CA, Department of Computer Science); Desmond Higham (University of Strathclyde, Glasgow G1 1XH, Department of Mathematics); Tijana Milenkovic (University of California, Irvine, Computer Science); Jason Lai (University of California, Irvine, Computer Science); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster R18 |
| Storing, querying and visualizing biological data using BioWeb, BioBrain and Cytoscape |
| Julie Leonard- BD Technologies |
| Tom Dyar (BD Technologies, C&TT); Dylan Wilson (BD Technologies, C&TT); Perry Haaland (BD Technologies, C&TT); |
| Short Abstract: We describe a knowledge management system called BioWeb. BioWeb uses Cytoscape as its front-end GUI and BioBrain, a proprietary AIML bot, to interpret natural language questions and perform queries of the underlying knowledge base. The organization of BioWeb and the corresponding tools allow biologists to easily obtain answers to complex questions. |
| Long Abstract: Click Here |
| Poster R19 |
| Expression Data Integrated with Domain Interaction Networks |
| Dorothea Emig- Max Planck Institute for Informatics |
| Melissa S. Cline (University of California, Santa Cruz, CA 95064, Department of Molecular Cell and Developmental Biology); Anne Kunert (Dortmund University of Technology, Department of Computer Science); Karsten Klein (Dortmund University of Technology, Department of Computer Science); Petra Mutzel (Dortmund University of Technology, Department of Computer Science); Thomas Lengauer (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Mario Albrecht (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); |
| Short Abstract: We developed the Cytoscape plugin DomainGraph for constructing and analyzing protein and domain interaction networks. DomainGraph additionally allows the integration of exon expression data and highlights the effects of alternative splicing in the networks. A new layout algorithm takes integrated biological information into account and improves the visual presentation. |
| Long Abstract: Click Here |
| Poster R20 |
| DASMI: Dynamic online integration and annotation of molecular interaction data |
| Hagen Blankenburg- Max Planck Institute for Informatics |
| Robert Finn (Wellcome Trust Sanger Institute, Informatics Division); Andreas Prlić (Wellcome Trust Sanger Institute, Informatics Division); Andrew Jenkinson (European Bioinformatics Institute, PANDA Group); Fidel RamĂrez (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Dorothea Emig (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Sven-Eric Schelhorn (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Andreas Schlicker (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Joachim BĂĽch (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Thomas Lengauer (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); Mario Albrecht (Max Planck Institute for Informatics, Department of Computational Biology and Applied Algorithmics); |
| Short Abstract: We present DASMI, an extensible framework for the dynamic exchange and annotation of different types of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing interaction data, and clients for data integration and visualization. |
| Long Abstract: Click Here |
| Poster R21 |
| A Computational Approach of Phenotype Microarray Data Finding Functional Units in Metabolic Networks |
| Yukako Tohsto- Ritsumeikan University |
| Hirotada Mori (Nara Institute of Science and Technology, Research and Education Center for Genetic Information); |
| Short Abstract: 45 deletion mutants under 288 conditions (carbon and nitrogen sources) of Phenotype Microarray were analyzed to clear the network in the central metabolisms in E. coli. For this analysis, data processing and analysis methods were developed. Results clearly showed functional connection between genes and respiratory activities. |
| Long Abstract: Click Here |
| Poster R22 |
| A continuous dynamical system of the signaling network that controls the differentiation of T-helper lymphocytes |
| Luis Mendoza- Universidad Nacional AutĂłnoma de MĂ©xico |
| Fátima Pardo (Universidad Nacional Autónoma de México, Instituto de Investigaciones Biomédicas); |
| Short Abstract: We present a model of the signaling network controlling the differentiation of T-helper cells, capable of describing the stable activation patterns observed in Th0, Th1, Th2, Th17 and Treg cells. We chose to model the network using the standardized qualitative dynamical modeling strategy, and made a preliminary sensitivity analysis. |
| Long Abstract: Click Here |
| Poster R23 |
| Information Flow in Interaction Networks |
| Aleksandar Stojmirovic- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health |
| No additional authors |
| Short Abstract: We present a mathematically simple formalism for modeling context-specific information propagation in interaction networks. Our approach is based on dissipating random walks, with the context provided by the selection of sources and destinations of information. The utility is illustrated using examples from yeast protein-protein interaction networks. |
| Long Abstract: Click Here |
| Poster R24 |
| Evolutionary changes of chloroplast metabolic structure enable stable photosynthetic rates under environments which can cause high rates of mutation |
| Zhuo Wang- Shanghai Jiao Tong University |
| Qi Chen (Shanghai Jiao Tong University, Bioinformatics and Biostatistics); Xinguang Zhu (University of Illinois at Urbana-Champaign, Plant Biology); Dongqing Wei (Shanghai Jiao Tong University, Bioinformatics and Biostatistics); Yixue Li (Shanghai Center for Bioinformation Technology, systems biology); Lei Liu (Shanghai Center for Bioinformation Technology, systems biology); |
| Short Abstract: Our previous study suggested that chloroplast metabolic network, compared to cyanobacteria, became denser around Calvin cycle though the overall network became looser. Using Flux Balance Analysis, we demonstrate metabolic structural changes increase the capacity of chloroplast to keep stable photosynthetic rates under environments which can cause high rates of mutation. |
| Long Abstract: Click Here |
| Poster R25 |
| Inference of logic networks from insertion and expression data |
| Jeroen de Ridder- Delft University of Technology |
| Marcel Reinders (Delft University of Technology, Information and Communication Theory Group); Lodewyk Wessels (The Netherlands Cancer Institute, Division of molecular Biology); |
| Short Abstract: We infer associations between virally targeted genes and gene expression profiles in tumors induced by insertional mutagenesis. In the inference procedure, we incorporate parallel possibilities and cooperations between virally inserted loci by means of simple boolean logic. This results in small logic networks of inserted genes that predict gene expression. |
| Long Abstract: Click Here |
| Poster R26 |
| Integration of metabolic flux modeling and gene expression data for the study of human embryonic and mature kidney cells |
| Augustin Luna- Boston University |
| William Riehl (Boston University, Bioinformatics); Daniel Segrè (Boston University, Biology, Biomedical Engineering, and Bioinformatics); |
| Short Abstract: In silico simulations were performed on the reconstructed human metabolic network where reactions were constrained using microarray data specific to embryonic and mature kidney cells. These networks were analyzed using flux balance analysis to study growth in embryonic kidney stem cells and deficiencies of glucose-6-phosphatase in mature kidney cells. |
| Long Abstract: Click Here |
| Poster R27 |
| Computational analysis of Nodal signaling in left-right asymmetry determination in zebrafish development |
| Bo Xu- Princeton University |
| Rebecca Burdine (Princeton University, Molecular Biology); |
| Short Abstract: Nodal signaling is conserved in left-right asymmetry determination in vertebrates. In zebrafish, oep and lefty acts as co-receptor and inhibitor respectively in Nodal pathway. We formulated a mathematical model based on existing data to describe how these components interact with each other to generate the asymmetric patterning during embryonic development. |
| Long Abstract: Click Here |
| Poster R28 |
| A Bayesian Framework for Inferrinf Transcription Factor |
| Thomas Asbury- Medical University of South Carolina |
| No additional authors |
| Short Abstract: Gene expression networks are strongly influenced by transcription factors (TFs). By determining the state of TFs for a given condition, the nature of cell response is described in a clear and meaningful manner. We present a Bayesian model for inferring the binary state of TFs from gene expression data. |
| Long Abstract: Click Here |
| Poster R29 |
| A comprehensive biological knowledge base and the network platform for modeling biological associations |
| Guozhen Liu- SuperArray Bioscience Corp. |
| Elvena Fong (Superarray Bioscience Corporation, R & D); Xiao Zeng (Superarray Bioscience Corporation, R & D); |
| Short Abstract: A simple web based graphic system (nodes and edges) is developed to model biological associations. In the backend a comprehensive biological knowledge base where text-mining results, high through-put experiment results and proprietary data are warehoused and integrated. Our system makes complicated biological discoveries easy to search and fun to navigate. |
| Long Abstract: Click Here |
| Poster R30 |
| Metabolic pathway alignment between species using a comprehensive and flexible similarity measure |
| Yunlei Li- Delft University of Technology |
| Dick de Ridder (Delft University of Technology, Information & Communication Theory Group); Marco de Groot (Delft University of Technology, Information & Communication Theory Group); Marcel Reinders (Delft University of Technology, Information & Communication Theory Group); |
| Short Abstract: We present a framework for matching metabolic pathways, and propose a novel scoring function which measures pathway similarity in a comprehensive and flexible manner. Our method systematically investigates full metabolic networks of two species simultaneously, with the goal of identifying highly similar pathways, which may contain non-identical reactions. |
| Long Abstract: Click Here |
| Poster R31 |
| Markov Chain Monte Carlo (MCMC) optimization to learn coupled Gene Regulatory Networks: the Inferelator 2.0 |
| Aviv Madar- New York University |
| Richard Bonneau (New York University, Biology and Computer Science); |
| Short Abstract: We have recently described a network inference algorithm, Inferelator, which infers dynamical models for genes from microarray data. Here we extend Inferelator, using MCMC, to learn dynamical models for each gene, in a coupled system. |
| Long Abstract: Click Here |
| Poster R32 |
| Combining â€omics data for a systems level picture of organelle metabolism in plants |
| Nicholas O'Toole- The University of Western Australia |
| No additional authors |
| Short Abstract: Metabolic networks have been constructed for the organelles associated with energy metabolism in plants – the mitochondrion, chloroplast and peroxisome – using experimental proteomics data in combination with known and manually-curated metabolic pathway data. Network topology parameters suggested refinements to the network models and further proteomic and metabolomic experimental investigations. |
| Long Abstract: Click Here |
| Poster R33 |
| Uncovering Biological Network Function via Graphlet Degree Signatures |
| Tijana Milenkovic- University of California, Irvine |
| Natasa Przulj (University of California, Irvine, Computer Science); |
| Short Abstract: We use "graphlet degree signatures" of proteins in protein-protein interaction (PPI) networks to design a method that demonstrates that biological function and network structure are closely related. We successfully uncover groups of "topologically similar" proteins that share common biological properties. We apply our method to infer function of unclassified proteins. |
| Long Abstract: Click Here |
| Poster R34 |
| Gene expression data helps in predicting metabolic flow-routes. |
| Rogier Van Berlo- Delft University Of Technology |
| Dick de Ridder (Delft University Of Technology, Department of Mediamatics); Marcel Reinders (Delft University Of Technology, Department of Mediamatics); |
| Short Abstract: We demonstrate that flux-balance analysis models can be improved by incorporating ranking constraints based on gene expression data. Namely, better flux-distribution predictions are obtained if we use the available gene expression data instead of performing flux minimization. This suggests that several metabolic processes are not operating in the most efficient way. |
| Long Abstract: Click Here |
| Poster R35 |
| The Influence of Spatial and Temporal Choices on the Dynamics of Generalized Boolean Networks |
| Christian Darabos- University of Lausanne |
| Marco Tomassini (University of Lausanne, Information Systems Institute (ISI)); Mario Giacobini (University of Torino, Computational Biology Unit, Molecular Biotechnology Center); |
| Short Abstract: Random Boolean Networks, simplified models of Genetic Regulatory Networks, rest on assumptions that do not agree with present data about GRNs, which are neither random in structure nor synchronous. The model we propose and experimentally study combines the original simplicity and current knowledge about spatial structure and timing of events. |
| Long Abstract: Click Here |
| Poster R36 |
| Pathway Prediction with eQTL and Gene Interaction Networks |
| Jacob Michaelson- Biotechnology Center, TU Dresden |
| No additional authors |
| Short Abstract: A network analysis method is presented and applied to expression quantitative trait loci (eQTL) data. The method is designed to overcome weaknesses of eQTL analysis, including noise, false positives, and a lack of molecular context. Results show improvement in recapitulating pathway members, compared to conventional eQTL approaches. |
| Long Abstract: Click Here |
| Poster R37 |
| MI3: Learning Gene Regulatory Models Using Continuous Three-Way Mutual Information |
| Weijun Luo- University of Michigan |
| No additional authors |
| Short Abstract: We developed a new network inference strategy, MI3 that addresses three major issues simultaneously: (1) to handle continuous variables, (2) to detect high order relationships, (3) to differentiate causal vs. confounding relationships. MI3 consistently and significantly outperformed frequently used control methods and faithfully capture mechanistic relationships from gene expression data. |
| Long Abstract: Click Here |
| Poster R38 |
| A comparison of metabolic pathway databases |
| Miranda Stobbe- AMC Amsterdam |
| A.H.C. van Kampen (AMC Amsterdam, Clinical Epidemiology, Biostatistics and Bioinformatics); P.D. Moerland (AMC Amsterdam, Clinical Epidemiology, Biostatistics and Bioinformatics); |
| Short Abstract: We present results of a within-species comparison of four metabolic pathway databases for man and yeast. The comparison highlights many differences between the databases for EC numbers, genes, reactions and network connectivity. Results can be used for an informed decision on which pathway database to use in a specific context. |
| Long Abstract: Click Here |
| Poster R39 |
| Reverse-engineering transcriptional modules from gene expression data |
| Riet De Smet- KULeuven |
| Anagha Joshi (VIB / Ghent University, Plant Systems Biology); Kathleen Marchal (KULeuven, Centre of Microbial and Plant Genetics (CMPG)); Yves Van de Peer (VIB / Ghent University, Plant Systems Biology); Tom Michoel (VIB / Ghent University, Plant Systems Biology); |
| Short Abstract: We present a framework to learn gene regulatory networks from expression data using a probabilistic graphical model. A stochastic algorithm was used to sample large ensembles from such module networks. Validation on an expression compendium for Escherichia coli shows that correct predictions are also the most significant ones. |
| Long Abstract: Click Here |
| Poster R40 |
| Computational identification of the normal and perturbed genetic networks involved in myeloid differentiation and acute promyelocytic leukemia |
| Li-Wei Chang- Washington University |
| Jacqueline Payton (Washington University, Pathology and Immunology); Wenlin Yuan (Washington University, Medicine); Timothy Ley (Washington University, Medicine); Rakesh Nagarajan (Washington University, Pathology and Immunology); Gary Stormo (Washington University, Genetics); |
| Short Abstract: We propose a novel, integrated approach for mammalian genetic network construction by combining the analysis of gene expression profiling data and the identification of transcription factor binding sites. We utilize our approach to construct the genetic circuitries operating in normal myeloid differentiation vs. acute promyelocytic leukemia. |
| Long Abstract: Click Here |
| Poster R41 |
| Efficient knowledge-based method for reconstruction of genetic metabolic networks |
| Aurora Labastida- Institute of cell physiology, Universidad nacional autonoma de Mexico |
| Gabriel Del Rio (Institute of cell physiology, Universidad nacional autonoma de Mexico, Biochemistry); |
| Short Abstract: We describe an approach to reconstruct genetic metabolic networks from chemical data. Combining 18 genetic networks and 16 centrality measurements, a network-centrality pair was identified that leads to the best prediction of lethal phenotypes. Our approach may be used to test for unknown genetic relationships and essential genes. |
| Long Abstract: Click Here |
| Poster R42 |
| Multi-species Integrative Biclustering with cMonkey |
| Thadeous Kacmarczyk- New York University |
| Peter Waltman (New York University, Computational Biology); |
| Short Abstract: Biological systems can be decomposed into modules of functionally related genes from which regulatory networks can be inferred. We present a comparative integrative biclustering method that identifies the genes and motifs associated with these functional modules. We introduce a resource for visualizing and exploring the results of this analysis. |
| Long Abstract: Click Here |
| Poster R43 |
| Generation and Refinement of Metabolic Reaction Networks in the SEED |
| Paul Frybarger- Hope College |
| Matt DeJongh (Hope College, Computer Science); |
| Short Abstract: We have developed a method for generating organism-specific metabolic reaction networks based on annotated genomes, and tools for manually refining them. These tools enable an iterative process of annotation, reaction network assembly, assertion of reactions to fill gaps, and testing for complete reaction paths from substrates to biomass components. |
| Long Abstract: Click Here |
| Poster R44 |
| MetNetGE: Visualizing Metabolic Networks using Google Earth |
| Julie Dickerson- Iowa State University |
| Ming Jia (Iowa State University, Computer Engineering); Eve Wurtele (Iowa State University, Genetics); |
| Short Abstract: MetNetGE, uses Google Earth (GE) to visualize metabolic pathways and omics data. The visual metaphor is a road network and its traffic status as an analog to biological networks. A web-based GUI generates files for GE. Animation of 3D shapes and arrows shows where key spots occur in the network. |
| Long Abstract: Click Here |
| Poster R45 |
| Global discovery and functional annotation of Arabidopsis cis-regulatory elements and promoter modules |
| Matt Geisler- Southern Illinois University Carbondale |
| Patrick Brown (SIUC, Computer Science); Elisabeth Fitzek (SIUC, Plant Biology); Changming Lee (SIUC, Computer Science); Mengxia Zhu (SIUC, Computer Science); |
| Short Abstract: We developed a new algorithm to test the biological significance of cis regulatory elements across a whole genome. We correlate the presence, orientation, distance from the TSS, and location to the regulatory fingerprint of gene expression. This forms the basis for improved promoter annotation and eliminates false positives. |
| Long Abstract: Click Here |
| Poster R46 |
| Elucidation of Functional Relationships via Information-Theoretic Clustering of Gene Expression Data and Consensus Motif Extraction |
| Jason Fye- Wake Forest University |
| William Turkett (Wake Forest University, Computer Science); Jacquelyn Fetrow (Wake Forest University, Computer Science & Physics); |
| Short Abstract: A novel method for clustering microarray data using mutual information is presented. Analysis of dendritic cell maturation data demonstrates the method’s ability to extract biologically-significant clusters inferring functional relationships through negatively-correlated expression patterns. An analysis tool for extracting and scoring transcriptional regulatory motifs using probabilistic suffix trees is also presented. |
| Long Abstract: Click Here |
| Poster R47 |
| Constructing gene relevance networks with local threshold |
| Bo Li- Clemson University |
| Feng Luo (Clemson University, Computer Science); Alex Feltus (Clemson University, Genetics and Biochemistry); |
| Short Abstract: We developed a new algorithm, which employed the significant level, rather than actual mutual information, as threshold to construct gene relevance networks. The results show that the local threshold method leads to gene relevance networks with more genes and links, while maintains the same true links level. |
| Long Abstract: Click Here |
| Poster R48 |
| Application of Bioinformatics Approaches to Understand Biosynthesis and Regulation of Suberin, a Biopolymer |
| Jane Geisler-Lee- Southern Illinois University Carbondale |
| Mengxia Zhu (SIUC, Computer Science); Matt Geisler (SIUC, Plant Biology); Kimberly Fair (SIUC, Plant Biology); |
| Short Abstract: We mine transcriptomic, interactomic and promoter sequence data to discover new genes in a biochemical pathway for the synthesis of suberin in Arabidopsis. We have identified 429 tentative genes passing 2 of 3, and 6 novel genes which pass all 3 criteria. Preliminary experimental analysis shows impact on root development. |
| Long Abstract: Click Here |
| Poster R49 |
| Hub-based Gene Expression Analysis of Melphalan Resistance in Multiple Myeloma |
| Steven Eschrich- H. Lee Moffitt Cancer Center & Research Institute |
| Margalit Goldgof (H. Lee Moffitt Cancer Center & Research Institute, Biomedical Informatics); Lori Hazlehurst (H. Lee Moffitt Cancer Center & Research Institute, Experimental Therapeutics); William Dalton (H. Lee Moffitt Cancer Center & Research Institute, Experimental Therapeutics); |
| Short Abstract: One systems biology approach to translating in vitro molecular signatures to clinical samples involves gene network hubs. Four hubs were identified using expression microarrays in a cell line study of melphalan drug resistance in multiple myeloma. One hub was significantly correlated to survival in a large dataset of melphalan-treated patients. |
| Long Abstract: Click Here |
| Poster R50 |
| Bayesian Reconstruction of ROS Detoxification Pathway in E. coli |
| Andrew Hodges- University of Michigan |
| Zuoshuang Xiang (University of Michigan, ULAM); Peter Woolf (University of Michigan, Chemical Engineering, Biomedical Engineering, CCMB); Yongqun He (University of Michigan, ULAM, Microbiology & Immunology, CCMB); |
| Short Abstract: The detoxification of reactive oxygen species in E. coli was modeled at the gene expression regulatory level using Bayesian networks (BN) and microarray data in the MARIMBA web portal (http://marimba.hegroup.org). Putative regulators of the detoxification pathway were identified via a new BN expansion algorithm. |
| Long Abstract: Click Here |
| Poster R51 |
| Better prediction of physical protein-protein interactions from microarray data |
| Ta-tsen Soong- Columbia University |
| Kazimierz O. Wrzeszczynski (Columbia University, Integrated Program in Cellular, Molecular, Structural and Genetic Studies); Burkhard Rost (Columbia University, Department of Biochemistry and Molecular Biophysics); |
| Short Abstract: Microarray data are usually used in discovery of functionally associated proteins and do not predict physical protein-protein interactions well. Here, we present a novel framework that improves interaction prediction from microarray data. Our method significantly outperforms the conventional correlation method and reveals several interesting predictions worthy of experimental validation. |
| Long Abstract: Click Here |
| Poster R52 |
| Biomarker discovery for hypertension using a NMR-based metabolomics approach |
| Tim De Meyer- Ghent University |
| Davy Sinnaeve (Ghent University, Department of Organic Chemistry, NMR and Structure Analysis); Bjorn Van Gasse (Ghent University, Department of Organic Chemistry, NMR and Structure Analysis); Elena Tsiporkova (VRT Medialab, VRT R&D at the Institute for BroadBand Technologies); Ernst Rietzschel (Ghent University, Department of Cardiovascular Diseases); Marc De Buyzere (Ghent University, Department of Cardiovascular Diseases); Thierry Gillebert (Ghent University, Department of Cardiovascular Diseases); Sofie Bekaert (Ghent University, Department of Molecular Biotechnology); Wim Van Criekinge (Ghent University, Department of Molecular Biotechnology); |
| Short Abstract: Hypertension is a major cardiovascular risk factor. We attempted to identify associated biomarkers using NMR based metabolic profiling of serum from hypertensive subjects and controls. After application of AI-binning, O-PLS and PLS-DA on the spectral data, our results suggest the involvement of choline and alpha-1 acid glycoprotein biochemistry in hypertension. |
| Long Abstract: Click Here |
| Poster R53 |
| Functional Pathway and Network Analysis of Cancer Omics Data |
| Zhang-Zhi Hu- Georgetown University Medical Center |
| Hongzhan Huang (Georgetown University Medical Center, Department of Biochemistry and Molecular & Cellular Biology); Benjamin Kagan (Georgetown University Medical Center, Department of Oncology); Anna Riegel (Georgetown University Medical Center, Department of Oncology); Anton Wellstein (Georgetown University Medical Center, Department of Oncology); Mira Jung (Georgetown University Medical Center, Department of Radiation Medicine); Anatoly Dritschilo (Georgetown University Medical Center, Department of Radiation Medicine); Cathy Wu (Georgetown University Medical Center, Department of Biochemistry and Molecular & Cellular Biology); |
| Short Abstract: Analysis and interpretation of large-scale omics data remain challenging and require effective use of public knowledge resources and advanced bioinformatics approaches. We present two case studies, using our iProXpress omics data analysis system coupled with pathway and network visualization tools to elucidate molecular mechanisms underlying drug- or radiation-resistance in cancers. |
| Long Abstract: Click Here |
| Poster R54 |
| A systems biology approach for analyzing RNAi data using functional networks |
| Angela Simeone- Biotechnology Center (BIOTEC), Technische Universität Dresden |
| Andreas Beyer (Biotechnology Center (BIOTEC), Technische Universität Dresden, Cellular Networks); |
| Short Abstract: In order to address the problems and the limits of RNAi screens and to correctly understand the role of each gene/protein in the specific cellular process we integrated the phenotypic information with functional networks. We show that this approach successfully detects false positive and false negatives in RNAi screens. |
| Long Abstract: Click Here |
| Poster R55 |
| INFERING CENTERS OF MOLECULAR REGULATION IN ALCOHOLISM |
| George Acquaah-Mensah- Massachusetts College of Pharmacy and Health Sciences |
| Caitlin Lally (Worcester Polytechnic Institute, Biochemistry); |
| Short Abstract: Bayesian Networks and ARACNE facilitated the inference of inter-connections between the regulators of protein metabolism, stress-responsive, and signal transduction pathways in alcoholism aspects. In ethanol preference, Carm1, Crebbp, Creb1, Stat3, Nfkbib and Atf2 play key roles. Similarly, in fetal alcohol exposure, Stat5 genes, Pmf1, and stress-responsive transcription factors are important. |
| Long Abstract: Click Here |
| Poster R56 |
| MouseCyc: a new database of curated biochemical pathways for the laboratory mouse |
| Mary Dolan- The Jackson Laboratory |
| Emily Patek (The Jackson Laboratory, Mouse Genome Informatics); Alexei Evsikov (The Jackson Laboratory, Mouse Genome Informatics); Michael Genrich (The Jackson Laboratory, Systems Administration); Carol J. Bult (The Jackson Laboratory, Mouse Genome Informatics); |
| Short Abstract: The MouseCyc database is a curated biochemical pathways database for the laboratory mouse. This resource provides pathway data that will be useful in other mammalian systems and represents a significant advance for biomedical researchers wanting to access mouse genetic and genomic data in the context of physiological and cellular processes. |
| Long Abstract: Click Here |
| Poster R57 |
| Genome-wide transcriptional coherence network and its applications |
| Jin Gyoon Park- Samuel Lunenfeld Research Institute |
| Jing Jin (Samuel Lunenfeld Research Institute, University Toronto); Chen Chen (Samuel Lunenfeld Research Institute, University Toronto); Tony Pawson (Samuel Lunenfeld Research Institute, University Toronto); |
| Short Abstract: We present a multi-dimensional transcriptional coherence network using public microarray sets, which were applied for domain-based systematic analysis and inference of gene functions. We also identified novel genes expressed specifically in many physiological contexts. The data will be a valuable resource for genome-wide expression analysis and pathway discovery. |
| Long Abstract: Click Here |
| Poster R58 |
| Optimized Null Model for Protein Structure Networks |
| Natasa Przulj- UC Irvine |
| Tijana Milenkovic (University of California, Irvine, Computer Science); Ioannis Filippis (Max Planck Institute for Molecular Genetics, Berlin, Bioinformatics/Structural Proteomics); Michael Lappe (Max Planck Institute for Molecular Genetics, Berlin, Bioinformatics/Structural Proteomics); |
| Short Abstract: We address a challenge of finding a well-fitting null model for protein structure networks, or residue interaction graphs (RIGs). The superiority of the fit of geometric random graphs over four other random graph models may have important implications for the discovery of significant structural motifs and for protein structure comparison. |
| Long Abstract: Click Here |
| Poster R59 |
| Expanding protein-protein interaction networks by integrating proteomics data with multiple lines of biological evidence |
| James Vlasblom- Hospital for Sick Children/University of Toronto |
| Shuye Pu (Hospital for Sick Children, Molecular Structure and Function); Shoshana Wodak (Hospital for Sick Children, Molecular Structure and Function); |
| Short Abstract: Tandem affinity purification/mass spectrometry is a high-throughput experimental method for identifying protein-protein interactions with good accuracy and coverage, contingent on appropriate statistical analysis. Current analysis methods discard most observed associations to maintain high prediction confidence. Here we investigate incorporating additional evidence using supervised classification to improve interactome coverage. |
| Long Abstract: Click Here |
| Poster R60 |
| Gene Function Prediction in Yeast, Mouse, and Human |
| Sara Mostafavi- University of Toronto |
| Quaid Morris (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); |
| Short Abstract: We present a fast and accurate algorithm for predicting gene function from multiple functional-association networks. We use linear regression to combine networks and a label propagation algorithm for predicting gene function from the combined network. We show that our algorithm results in high prediction accuracies in yeast, mouse, and human. |
| Long Abstract: Click Here |
| Poster R61 |
| Reverse Engineering of the Transcriptional Subnetwork in Yeast Cell Cycle Pathway Using Dynamic Bayesian Networks and Covariance Matrix Adaptation Evolution Strategy with Explicit Memory |
| Maryam Salehi- Queen's University- Graduate Student |
| Parvin Mousavi (Assistant Professor- Queen's University, School of Computing); |
| Short Abstract: In this study, we employ Dynamic Bayesian Networks with an evolutionary structure-learning approach to reverse engineer the transcriptional network of 14 genes involved in the cell cycle of yeast Saccharomyces Cerevisiae. The results, compared to KEGG pathway, indicate the efficiency of our method in reconstructing the network (80% Sensitivity). |
| Long Abstract: Click Here |
| Poster R62 |
| INDUCNG PLANT STRESS TOLERANCE THROUGH SYSTEMS BIOLOGY AND COMPARITIVE GENOMICS |
| Namit Bharija- VIT University |
| MONA SRIVASTAVA (VIT University, Bioinf); |
| Short Abstract: plant stress tolerance is one the major concerns for plant biologists today due to global warming.This study aims at increases antioxidant production with the photosynthetic pathway to achieve plant stress tolerance.E-cell enables to study the relation between these pathways in-silico through systems biology. |
| Long Abstract: Click Here |
| Poster R63 |
| Modeling Reaction Kinetics of MAP Kinase Signaling in Crowded Intracellular Environments Using a Parallelized Stochastic Simulation |
| Joshua Swearingen- Medical University of South Carolina |
| John Schwacke (MUSC, Bioinformatics); |
| Short Abstract: We use a clustered stochastic simulation to investigate MAP Kinase signalling at the level of the individual molecule, and observe how crowding in the environment has an effect on reaction rates. |
| Long Abstract: Click Here |
| Poster R64 |
| Evolution of Metabolism in a graph-based Toy-Universe |
| Alexander Ullrich- Universität Wien |
| Christoph Flamm (Universität Wien, Theoretical Biochemistry Group, Institut für Theoretische Chemie); |
| Short Abstract: We present an entirely graph-based model for simulating the evolution of metabolic reaction networks. Integrating a sophisticated artificial chemistry, a novel genotype-phenotype mapping and a metabolic flux analysis with extended functionality. Allowing unbiased research on the emergence of network properties, evolution of biological entities and evolutionary optimization in populations. |
| Long Abstract: Click Here |
| Poster R65 |
| BioPAX - Biological Pathway Data Exchange Format |
| Gary Bader- University of Toronto |
| Emek Demir (Memorial Sloan-Kettering Cancer Centre, Computational Biology); BioPAX Workgroup (BioPAX, www.biopax.org); |
| Short Abstract: BioPAX (biopax.org) is a data exchange format for biological pathways developed and used by pathway databases, including BioCyc, Reactome, INOH, NCI/Nature PID, TRANSFAC, RegulonDB, Cancer Cell Map. Recently released BioPAX Level 3 supports metabolic and signal transduction pathways, gene regulatory networks, and molecular and genetic interactions. |
| Long Abstract: Click Here |
| Poster R66 |
| Pathway Commons – Single point of access to public biological pathway information |
| Gary Bader- University of Toronto |
| Ethan Cerami (Memorial Sloan-Kettering Cancer Center, Computational Biology Department); Emek Demir (Memorial Sloan-Kettering Cancer Center, Computational Biology Department); Benjamin Gross (Memorial Sloan-Kettering Cancer Center, Computational Biology Department); Chris Sander (Memorial Sloan-Kettering Cancer Center, Computational Biology Department); |
| Short Abstract: Pathway Commons (http://www.pathwaycommons.org) is a single point of access to public biological pathway information. It currently contains Reactome, HumanCyc, NCI/Nature PID, Cancer Cell Map, MINT, IntAct and HPRD pathway and interaction databases. Pathways can be searched, browsed, visualized, downloaded in BioPAX format and accessed via a web service. |
| Long Abstract: Click Here |
| Poster R67 |
| Mathematical Modeling of the Transcriptional Regulatory Network Controlling the Cold Shock Response in Saccharomyces cerevisiae |
| Kam Dahlquist- Loyola Marymount University |
| Stephanie Kuelbs (Loyola Marymount University, Mathematics); Kevin Entzminger (Loyola Marymount University, Chemistry & Biochemistry); Kenny Rodriguez (Loyola Marymount University, Biology); Ben Fitzpatrick (Loyola Marymount University, Mathematics); |
| Short Abstract: Using differential equations, we modeled the dynamics of a transcriptional regulatory network of twenty-one transcription factors controlling the cold shock response in yeast. Weight parameters were optimized to DNA microarray data. Predictions from simulated data for the intact network and networks with each gene deleted were tested by laboratory experiments. |
| Long Abstract: Click Here |
| Poster R68 |
| Transcriptome Analysis and in silico Modeling Lead to Mechanistic Insights on FTI/Taxol Synergy |
| Zeynep Gumus- HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine |
| Ada Gjrezi (Department of Medicine, Weill Medical College, Cornell University); Heming Xing (Gene Network Sciences, Boston); Zach Pitluk (Gene Network Sciences, Boston); Ilse Van den Wyngaert (Johnson and Johnson Pharmaceutical Research and Development, Functional Genomics); William Talloen (Johnson and Johnson Pharmaceutical Research and Development, Functional Genomics); Hinrich Goehlman (Johnson and Johnson Pharmaceutical Research and Development, Functional Genomics); Iya Khalil (Gene Network Sciences, Boston); Harel Weinstein (HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Medical College of Cornell University); Paraskevi Giannakakou (Department of Medicine, Division of Hematology and Medical Oncology, Weill Medical College of Cornell University); |
| Short Abstract: Farnesyltransferase inhibitors (FTIs) combined with taxanes are promising anticancer agents. Transcriptome changes due to FTI Lonafarnib and Taxol alone, or in combination, were functionally grouped and translated into molecular interaction maps and pathways. Experiments on drug effects on tubulin acetylation were analyzed with a reverse engineering and forward simulation technology. |
| Long Abstract: Click Here |
| Poster R69 |
| Paxtools: A software library for accessing and analyzing biological pathway information |
| Emek Demir- MSKCC |
| Ken Fukuda (National Institute of Advanced Industrial Science and Technology (AIST), Computational Biology Research Center (CBRC)); |
| Short Abstract: Paxtools is a software library for manipulating pathways represented in the BioPAX format. Paxtools provides an object representation of BioPAX and methods for input/output, validation, integration and persistence. Paxtools significantly eases developing BioPAX software such as editors, exporters, importers, visualization tools and analysis algorithms. |
| Long Abstract: Click Here |
| Poster R70 |
| Integration of diverse functional genomics data for computational characterization of intra-protein-complex hierarchy in S. cerevisiae |
| Vuk Pavlovic- University of Toronto |
| No additional authors |
| Short Abstract: Recent protein-complex discovery efforts have elucidated a vast and intricate yeast complexome. However, the roles and relationships of complex constituents remain unknown due to the lack of pertinent high-throughput experimental methods. We present a method that integrates diverse functional genomics data to predict sub-complex characterizations using a novel hierarchical-modeling approach. |
| Long Abstract: Click Here |
| Poster R71 |
| Deciphering cellular pathways and network architecture of yeast using quantitative SGA interaction data |
| Judice Koh- University of Toronto |
| Chad L. Myers (University of Minnesota, Department of Computer Science); Joe Whitney (University of Toronto, Department of Computer Science); Anastasia Baryshnikova (University of Toronto, Department of Molecular Genetics); Michael Costanzo (University of Toronto, Banting and Best Department of Medical Research); Michael Brudno (University of Toronto, Department of Computer Science); Charles Boone (University of Toronto, Banting and Best Department of Medical Research); |
| Short Abstract: We describe a study of yeast genetic interaction network architecture based on a novel dataset of quantitative synthetic genetic array (SGA) interactions. It unravels a high-level global architecture in yeast genetic network, leading to the development of a new algorithm, GINECA for inferring functional gene modules and their genetic associations. |
| Long Abstract: Click Here |
| Poster R72 |
| Coordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiae |
| Patrick Bradley- Princeton University |
| Olga Troyanskaya (Princeton University, Computer Science); Joshua Rabinowitz (Chemistry, Princeton University); Matthew Brauer (Molecular Biology, Princeton); |
| Short Abstract: Previous research is divided as to whether functionally-related metabolites and transcripts show coherent patterns of concentration changes at a global level. We demonstrate coordination of the transcriptional and metabolomic responses to two environmental perturbations in yeast, and develop a Bayesian algorithm that effectively recovers gene-metabolite interactions from this data. |
| Long Abstract: Click Here |
| Poster R73 |
| Integrated Experimental and Computational Approaches to Efficient Discovery of Higher-Order Drug Combinations |
| Adrian Heilbut- Boston University and CombinatoRx Inc. |
| Andrew Krueger (Boston University, Biomedical Engineering); Grant Zimmermann (CombinatoRx Inc., Discovery); Joseph Lehár (CombinatoRx Inc. and Boston University, Computational Biology); |
| Short Abstract: CombinatoRx has developed a platform for combination high throughput screening to discover therapeutically relevant drug synergies. Here, we describe systematic mechanistic screens in cancer, and computational approaches to optimize higher-order screen designs. Integration of simulation, statistical models, and screening will guide discovery of effective, selective higher-order drug combinations. |
| Long Abstract: Click Here |
| Poster R74 |
| Deriving quantitative epistasis measures from double mutant colony growth |
| Chad Myers- University of Minnesota |
| Anastasia Baryshnikova (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); Michael Costanzo (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); Judice Koh (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); David Hess (Princeton University, Lewis-Sigler Institute for Integrative Genomics); Huiming Ding (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); Gary Bader (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); Charles Boone (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); Olga Troyanskaya (Princeton University, Lewis-Sigler Institute for Integrative Genomics); |
| Short Abstract: We have developed an approach for deriving precise quantitative epistasis measurements from double mutant colony growth data. We have applied this approach to high-throughput Synthetic Genetic Array (SGA) screens and find that we are able to measure tens of thousands of previously unreported, highly reproducible, positive and negative genetic interactions. |
| Long Abstract: Click Here |
| Poster R75 |
| Merging protein interaction networks with protein motif analyses for biological discovery |
| Xiaojun Guan- Renaissance Computing Institute |
| Jeffrey Tilson (Renaissance Computing Institute, RENCI); Dennis Simpson (University of North Carolina at Chapel Hill, Pathology); Gloria Rendon (University of Illinois, Urbana, Molecular and Integrative Physiology); Eric Jakobsson (University of Illinois, Urbana, Molecular and Integrative Physiology); Jefferson Heard (Renaissance Computing Institute, RENCI); Dave Bowman (Renaissance Computing Institute, RENCI); William Kaufmann (University of North Carolina at Chapel Hill, Pathology); |
| Short Abstract: We show the results of our effort to describe interactions among the network of proteins that participate in the intra-S checkpoint response to DNA damage, and the motif structure of the intra-S checkpoint proteome. We also demonstrate a new tool to visualize a hierarchical cluster tree of proteins |
| Long Abstract: Click Here |
| Poster R76 |
| Systematic Discovery of In Vivo Phosphorylation Networks |
| Rune Linding- MIT & Mt.Sinai Hospital |
| No additional authors |
| Short Abstract: Linking kinases to their cellular substrates remains a grand challenge of systems and signaling studies. Here, we present the computational integrative algorithm NetworKIN, which uses contextual modelling to improve the accuracy with which phosphorylation networks can be constructed more than 2.5 fold. |
| Long Abstract: Click Here |
| Poster R77 |
| Knowledge-driven data integration for the prediction of protein-protein interaction networks |
| Huiru Zheng- University of Ulster |
| Fiona Browne (University of Ulster, School of Comouting and Mathematics); Haiying Wang (University of Ulster, School of Comouting and Mathematics); Francisco Azuaje (Research Centre for Public Health , Laboratory of Cardiovascular Research); |
| Short Abstract: This paper proposes a knowledge-driven computational framework to support systems-level data integration for the prediction of protein-protein interaction (PPI) networks. Based on the incorporation of prior knowledge of the relationship between different “omic” datasets, different likelihood-ratio-based Bayesian models have been developed to combine diverse biological data to improve PPI predictions. |
| Long Abstract: Click Here |
| Poster R78 |
| Combinatorial regulation across species: co-regulatory associations, hierarchy of regulation and evolutionary dynamics |
| Nitin Bhardwaj- Yale University |
| No additional authors |
| Short Abstract: Co-regulation networks between transcription factors regulating the same gene are built and analyzed for five diverse species revealing many ubiquitous interesting properties: the presence of two kinds of regulatory hubs (integrative and autonomous), coregulation patters between TFs from different layers in the hierarchy and a very high evolutionary divergence rate. |
| Long Abstract: Click Here |
| Poster R79 |
| Stochasticity in the EGFR-stimulated activation of Ras |
| Gordon Webster- Plectix BioSystems |
| Russ Harmer (CNRS, Paris-Diderot); Joshua Havumaki (Plectix BioSystems, Biology); Isha Antani (Plectix BioSystems, Biology); |
| Short Abstract: EGFR regulates Ras in contradictory ways, promoting its activation via Sos and inhibiting it via RasGap. Simulating the Ras-activation of the downstream MAP kinase pathways using the Kappa language, we have been able to reproduce the stochastic behavior of this system that has been observed experimentally in certain cell lines. |
| Long Abstract: Click Here |
| Poster R80 |
| Investigating metabolite turnover rates using constraint-based models of metabolism |
| Laurence Yang- University of Toronto |
| Radhakrishnan Mahadevan (University of Toronto, Chemical Engineering and Applied Chemistry); William R. Cluett (University of Toronto, Chemical Engineering and Applied Chemistry); |
| Short Abstract: Global metabolite turnover rate profiles can offer insight and improve strain design. Using a thermodynamically constrained metabolic model of Escherichia coli with additional constraints from multi-omics measurements in the literature, we found that metabolite turnover rates were bimodally distributed and found a correlation between turnover rates and metabolite connectivity. |
| Long Abstract: Click Here |
| Poster R81 |
| Biological workflows enabled by the Bioinformatics Resource Manager |
| Mudita Singhal- Pacific Northwest National Laboratories |
| Anuj Shah (PNNL, SDM); Justin Almquist (PNNL, Knowledge Systems); Kelly Domico (PNNL, NSD); Chandrika SivaramaKrishnan (PNNL, Applied Computer Science); Tara Gibson (PNNL, SDM); Ian Gorton (PNNL, ACS); Katrina Waters (PNNL, CBB); |
| Short Abstract: The Bioinformatics Resource Manager (BRM) is a software platform for systems biology that provides users with data management, integration and analysis capabilities through seamless integration of publicly available tools and annotation databases. BRM also facilitates data analysis workflows through our Middleware for Data Intensive Computing (MeDICi) architecture. |
| Long Abstract: Click Here |
| Poster R82 |
| A Bayesian Network Approach for Identifying Trait-Dependent, Cross Tissue Expression Co-regulation |
| Zhidong Tu- Rosetta/Merck |
| Chunsheng Zhang (Rosetta Inpharmatics, Custom Analysis); Hongyue Dai (Rosetta Inpharmatics, Custom Analysis); Jun Zhu (Rosetta Inpharmatics, Genetics); Eric Schadt (Rosetta Inpharmatics, Genetics); |
| Short Abstract: Complex living systems are comprised of multiple tissue types that interact in complex ways. Using a mouse cross, we study the causative relationship among genetic loci, gene expression traits spanning multiple tissues, and phenotypic traits. We generate a first look of trait-dependent, cross tissue gene co-regulation at the systems level. |
| Long Abstract: Click Here |
| Poster R83 |
| R2 and Cytoscape: Graph based integration and analysis of molecular biological data |
| Piet Molenaar- Academic Medical Center - University of Amsterdam |
| Jan Koster (Bioinformatician, Human Genetics); Rogier Versteeg (Professor, Human Genetics); |
| Short Abstract: We developed a plugin for the graph visualization tool Cytoscape that maintains the current state of knowledge in our group and enables analysis in a graph based way. Evidence from wet-lab experiments can be analyzed in combination with array data via our in-house developed web based tool R2 |
| Long Abstract: Click Here |
| Poster R84 |
| A new scoring strategy for pathway enrichment analysis |
| Hung Jui-Hung- Boston university |
| Jui-Hung Hung (Boston University, Bioinformatics program); Zhenjun Hu (Boston University, Bioinformatics program); Zhiping Weng (Boston University, Bioinformatics program); Charles DeLisi (Boston University, Bioinformatics program); |
| Short Abstract: Pathway, which can be considered as a set of connected genes, proteins, compounds, and reactions, which were previously regarded as just collections of genes. Our new scoring strategy acknowledges the topological traits of pathways and gives results more consistant to biological facts. |
| Long Abstract: Click Here |
| Poster R85 |
| MetNet 3: Searching and visualizing plant metabolic pathways |
| Yves Sucaet- Iowa State University |
| Eve Syrkin Wurtele (Iowa State University, GDCB); |
| Short Abstract: MetNet 3 is a novel online platform to retrieve information on plant metabolic and regulatory networks from MetNetDB. Pathways and subnetworks visualized with MetNet3 represent user-selected data types, including information flow from genes to metabolites, interactions, and feedback loops that induce (post-)transcriptional perturbations. The site is available through http://www.metnetdb.org/metnet3 |
| Long Abstract: Click Here |
| Poster R87 |
| Identification of MicroRNA Regulatory Modules in Arabidopsis Developmental Processes Using a Probabilistic Graphical Model |
| Je-Gun Joung- Boyce Thompson Institute, Cornell University |
| Zhangjun Fei (Boyce Thompson Institute, Cornell University, and USDA Robert W. Holley Center for Agriculture and Health, Bioinformatics Lab.); Je-Gun Joung (Boyce Thompson Institute, Cornell University, Bioinformatics Lab.); |
| Short Abstract: MicroRNAs play critical roles in various cellular processes. Here we propose a method based on the probabilistic graphical model to identify miRNA modules with the information of miRNA binding targets and their gene expression profiles. The proposed method found clusters of miRNAs highly related to different Arabidopsis developmental processes. |
| Long Abstract: Click Here |
| Poster R88 |
| Exploratory Analysis of Community Structure in Biological Interaction Networks |
| Gang Su- University of Michigan |
| David States (University of Michigan Ann Arbor, Bioinformatics); |
| Short Abstract: We have applied community structure detection algorithms on MiMI protein-protein interaction network data to explore clusters of highly interacting biological molecules. Results shown the produced clusters have high correspondence to annotated pathway data from KEGG; the clustering information can be used to improve our knowledge on pathways and protein complexes. |
| Long Abstract: Click Here |
| Poster R89 |
| Qualitative Reasoning about Biological Networks |
| Michael Liebman- Windber Research Institute |
| No additional authors |
| Short Abstract: Biological networks are the functional representation of the relationship among multi-scale data that ties composition, structure and function together in both normal and abnormal (disease) biology. We have developed a qualitative reasoning approach to their representation and modeling that enables a modeling-experiment paradigm to be implemented in disease analysis. |
| Long Abstract: Click Here |
| Poster R90 |
| Estimating protein copy number based on fluctuations in intensity |
| Lee Zamparo- Concordia University |
| Theodore Perkins (McGill University, Computer Science); Greg Butler (Concordia University, Computer Science); |
| Short Abstract: We develop two estimators of protein copy number based on fluctuations in fluorescent intensity, and apply them to in situ hybridization images of drosophila embryos. Our results suggest that protein copy numbers for segmentation network genes typically reach at least several hundreds per nucleus during development. |
| Long Abstract: Click Here |
| Poster R91 |
| Parallel Use Of Genome Scale Experimentation and Modeling To Refine Knowledge Of Yeast Metabolism |
| Evan Snitkin- Boston University |
| Aimee Dudley (Institute For Systems Biology, Seattle, WA, 98103, -); Daniel Segre (Boston University, Boston, MA, 02215, Bioinformatics Program); George Church (Harvard Medical School, Boston, MA, 02215, Department of Genetics); Daniel Janse (McKinsey & Company, London, UK SW1Y 4UH, -); Kaisheen Wong (Harvard College, Cambridge, MA, 02138, -); |
| Short Abstract: An open question is how genome-scale experimentation and modeling can be used side-by-side to drive biological discovery. Here, we explored the parallel use of experimentally determined single gene deletion mutants growth rates, and corresponding flux balance model predictions, in experimental quality control and biological hypothesis testing. |
| Long Abstract: Click Here |
| Poster R92 |
| Tools for Pathway Creation, Visualization, and Collaboration |
| Alexander Pico- The Gladstone Institutes, UCSF |
| Martijn van Iersel (University of Maastricht, BiGCaT Bioinformatics); Thomas Kelder (University of Maastricht, BiGCaT Bioinformatics); Chris Evelo (University of Maastricht, BiGCaT Bioinformatics); Kristina Hanspers (The Gladstone Institutes, UCSF, GICD); Bruce Conklin (The Gladstone Institutes, UCSF, GICD); |
| Short Abstract: We want to develop a user-friendly and flexible pathway visualization framework. This will take the form of a pathway editing program that extends the GenMAPP concept named PathVisio and a website for online collaboration and curation named WikiPathways. |
| Long Abstract: Click Here |
| Accepted Posters |
| View Posters By Category |
- A) Arrays
- B) Bioinformatics of Health and Disease
- C) Chemical and Pharmaceutical Informatics
- D) Comparative Genomics
- E) Databases
- F) Evolution
- G) Functional Genomics
- H) Gene Prediction
- I) Genome Annotation
- J) Genomics
- K) Interactions
- L) Machine Learning
- M) Population Genetics and Variation
- N) Proteomics
- O) Regulation
- P) Sequence Analysis
- Q) Structure and Function Prediction
- R) Systems Biology and Networks
- S) text mining
- T) Other (includes posters with fewer than 10 submissions)
| Search Posters: |