| Accepted Posters |
| Category 'X'- Systems Biology and Networks' |
| Poster X001 |
| Describing Simulation Experiments using the SED-ML |
| Dagmar Köhn- Universität Rostock |
| Richard Adams (The University of Edinburgh , Centre for Systems Biology); Igor Goryanin (The University of Edinburgh , Informatics Life-Sciences Institute); Frank Bergmann (Keck Graduate Institute, Applied Sciences); Fedor Kolpakov (Design Technological Institute of Digital Techniques, Institute of Systems Biology ); Michael Hucka ( Phone (626) 395-8128 Office Beckman Institute 272 California Institute of Technology, Control and Dynamical Systems); Ion Moraru (University of Connecticut, Center for Cell Analysis and Modeling); Nicolas Le Novère (European Bioinformatics Institute, Computational Neurobiology); Sven Sahle (Universität Heidelberg, Bioquant); Henning Schmidt (-, -); |
| Short Abstract: The proper use of computational models of biochemical processes requires the details of the simulation processes to run. We present the XML-based formatSED-ML for the encoding of simulation experiments. It holds information about the simulation, the models and model perturbations, the experiment setups, and the output. |
| Long Abstract: Click Here |
| Poster X002 |
| Stochastic modelling in biology |
| Yang Luo- Cambridge University |
| No additional authors |
| Short Abstract: Stochastic heterogeneity in a cell culture derived from the same environmental exposure and identical histories is a key to understanding the phenotypic variability in these cell populations. To assess the effect of stochastic fluctuations, we perform stochastic simulations after adding stochastic effect on to deterministic models. |
| Long Abstract: Click Here |
| Poster X003 |
| Computational evidence for the relationship between protein phosphorylation and protein complex formation |
| Nozomu Yachie- Keio University |
| Rintaro Saito (Keio University, Institute for Advanced Biosciences); Naoyuki Sugiyama (Keio University, Institute for Advanced Biosciences); Masaru Tomita (Keio University, Institute for Advanced Biosciences); Yasushi Ishihama (Keio University, Institute for Advanced Biosciences); |
| Short Abstract: Ｗe identified 3,774 novel phosphosites and 898 phosphoproteins in yeast cells by liquid chromatography-tandem mass spectrometry. We then integrated yeast phosphoproteome and protein interactome data to computationally demonstrate general relationship between protein phosphorylation and protein complex formation. |
| Long Abstract: Click Here |
| Poster X004 |
| Identifying and Investigating Protein Clusters in the HIV-Human Protein Interaction Network |
| Jamie MacPherson- University of Manchester |
| John Pinney (Imperial College London, Division of Molecular Biosciences); David Robertson (University of Manchester, Faculty of Life Sciences); |
| Short Abstract: Using biclustering analysis, we investigate groups of human proteins that respond in similar ways during the course of HIV-1 infection. We identify 261 significant clusters and find that many of these consist of biologically related proteins. Our results highlight ways through which HIV-1 perturbs the host cell. |
| Long Abstract: Click Here |
| Poster X005 |
| Inferring the metabolic network of Plasmodium falciparum in the host |
| Segun Fatumo- Covenant University |
| Ezekiel Adebiyi (Covenant University, Nigeria, Department of Computer and Information Sciences); Rainer König (German Cancer Research Center, Heidelberg, Theoretical Bioinformatics); |
| Short Abstract: A critical comparison disclosed that the automatic reconstruction of pathways generates manifold paths that need an expert manual verification. In this work, we support the hypothesis that the gaps in PlasmoCyc could be filled by an elaborated comparison to the human metabolic network as the parasite may take advantage of human enzymes. |
| Long Abstract: Click Here |
| Poster X006 |
| On multiple regulatory mechanisms in the tryptophan operon system in Escherichia coli: in silico study of perturbation dynamics |
| Lan Nguyen- Lincoln University |
| No additional authors |
| Short Abstract: Using mathematical models and intensive computational simulations, we show that the multiple control mechanisms that govern the tryptophan operon system in Escherichia coli, are not redundant but possess distinctive functional characteristics. Enzyme inhibition directly controls the disturbance level from perturbations. Attenuation speeds up system recovery whereas Repression lengthens recovery time. |
| Long Abstract: Click Here |
| Poster X007 |
| Reactome: a database of curated pathways |
| David Croft- European Bioinformatics Institute |
| No additional authors |
| Short Abstract: Reactome is a curated database of cellular processes in human biology. It is authored by biological researchers and maintained by the Reactome editorial staff All entries are supported by literature references. Inferred orthologous pathways in 22 other species, including mouse, worm, fly, yeast and E.coli are also available. |
| Long Abstract: Click Here |
| Poster X008 |
| Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data |
| Natasa Przulj- UC Irvine |
| Tijana Milenkovic (University of California, Irvine, Computer Science); Vesna Memisevic (University of California, Irvine, Computer Science); Anand Ganesan (University of California, Irvine, Dermatology and Biological Chemistry); |
| Short Abstract: We use a sensitive graph-theoretic method for comparing local structures of node neighborhoods to demonstrate that in human protein interaction network, topology around cancer genes is different from topology around non-cancer genes. From this observation, we identify novel cancer gene candidates, validating our predictions both biologically and in the literature. |
| Long Abstract: Click Here |
| Poster X009 |
| Functional Module Identification in Protein Interaction Networks |
| Waqar Ali- Oxford University |
| Charlotte Deane (Oxford University, Statistics); |
| Short Abstract: Current functional module detection studies based on network alignment invariably use sequence information. We investigated the use of protein function for alignment and achieved significantly better results. We went on to develop a combined method that improves the coverage of alignment results. |
| Long Abstract: Click Here |
| Poster X010 |
| Integrated analysis of multidimensional RNAi screens |
| Angela Simeone- TU Dresden |
| Claudio Collinet (Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Institute of Molecular Cell Biology and Genetics); Yannis Kalaidzidis (Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Institute of Molecular Cell Biology and Genetics); Marino Zerial (Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Institute of Molecular Cell Biology and Genetics); Andreas Beyer (TU Dresden, BIOTEC); |
| Short Abstract: Despite the technological progress genome-wide RNAi screens still cope with significant problems (off-target effects, inaccurate phenotype detection). We propose two methods that integrate high-dimensional RNAi screens with independent interaction data. These methods improve the hit detection and provide rich insights for the biological interpretation of the screen. |
| Long Abstract: Click Here |
| Poster X011 |
| Condition-dependent complex regulation in Escherichia coli |
| Karen Lemmens- KU Leuven |
| Tijl De Bie (University of Bristol, Department of Engineering Mathematics); Thomas Dhollander (KU Leuven, Department of Electrical engineering); Bart De Moor (KU Leuven, Department of Electrical engineering); Sigrid De Keersmaecker (KU Leuven, Department of Microbial and Molecular Systems (M2S)); Inge Thijs (KU Leuven, Department of Microbial and Molecular Systems (M2S)); Kristof Engelen (KU Leuven, Department of Microbial and Molecular Systems (M2S)); Kathleen Marchal (KU Leuven, Department of Microbial and Molecular Systems (M2S)); Julio Collado-Vides (Universidad Nacional Autónoma de México, Centro de Ciencias Genómicas); |
| Short Abstract: DISTILLER is a data integration framework for the inference of transcriptional modules. Experimental validation of predicted targets showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred network was studied. Surprisingly, complex regulatory programs decreased the degree of modularity |
| Long Abstract: Click Here |
| Poster X012 |
| Identify co-conserved regulatory-metabolic network by multi-level pathway alignment |
| Yunlei Li- Delft University of Technology |
| Dick de Ridder (Delft University of Technology, Mediamatics); Marcel Reinders (Delft University of Technology, Mediamatics); |
| Short Abstract: We systematically compare the metabolic networks and transcriptional regulations of two species in the same time, with the goal of finding conserved units of evolution at both regulatory and metabolic levels. Then we generate hypotheses at one level using the information of another level, and reveal the differences between them. |
| Long Abstract: Click Here |
| Poster X013 |
| Immunology Atlas (I-ATLAS) A pathway based approach to dissect the hyerarchy of signal transduction in the dendritic cell |
| Enrica Calura- University of Firenze |
| Luca Beltrame (University of Firenze, Pharmacology); Raffaele Paola (University of Firenze, Pharmacology); Lisa Rizzetto (University of Firenze, Pharmacology); Razvan Popovic (Wayne State University, Computer Science); Sorin Draghici (Wayne State University, Computer Science); Duccio Cavalieri (University of Firenze, Pharmacology); Damariz Rivero-Guedez (University of Firenze, Pharmacology); |
| Short Abstract: Here we present a study focused on the advantages of the use cell type-specific pathways, employed by re-contructing the pathway signatures of public expression datasets on macrophage and dendritic cell in response to different stimuli, to decipher the biological complexity of the immune response depending on organism and cell-type. |
| Long Abstract: Click Here |
| Poster X014 |
| Searching for functional gene modules with interaction component models |
| Juuso Parkkinen- Helsinki University of Technology |
| Samuel Kaski (Helsinki University of Technology, Department of Information and Computer Science); |
| Short Abstract: We introduce a new probabilistic modeling framework to integrate protein-protein interactions with gene expression measurements. Our combined approach outperforms alternatives in the task of of finding functional gene modules. |
| Long Abstract: Click Here |
| Poster X015 |
| Computational Study on Signaling Pathway Networks of Fission Yeast |
| JIAN-QIN LIU- NICT-KARC |
| HIRAOKA YASUSHI (NICT-KARC/Osaka University, Biological ICT Group (Cell Biology Project)/Laboratory of Cellular Structure and Function); |
| Short Abstract: With a signaling pathway network model designed based on information theoretic measurement and methodology of systems biology, complex dynamics mechanisms of signal transduction in the fission yeast Schizosaccharomyces pombe is quantitatively analyzed, from which the transmission rate of the Byr2-Byr1-Spk1 pathway is determined for verifying the feedback function on meiosis. |
| Long Abstract: Click Here |
| Poster X016 |
| Deciphering chemotaxis pathways |
| Rebecca Hamer- University of Oxford |
| Pao-Yang Chen (University of Oxford, Statistics); Judith Armitage (University of Oxford, Oxford Centre for Integrated Systems Biology); Charlotte Deane (University of Oxford, Statistics); Gesine Reinert (University of Oxford, Statistics); |
| Short Abstract: Chemotaxis is the process whereby motile bacteria sense their environment and move towards more favourable conditions. Escherichia coli utilises a single pathway but other bacteria have more complex systems with little known about the pathways involved. We aim to predict chemotaxis pathways and determine common features across bacterial species. |
| Long Abstract: Click Here |
| Poster X017 |
| SysMO-DB: Sharing and Exchanging Systems Biology Data and Models |
| Katy Wolstencroft- University of Manchester |
| Olga Krebs (EML Research, gGmbH); Wolfgang Mueller (EML Research, gGmbH); Isabel Rojas (EML Research, gGmbH); Stuart Owen (University of Manchester, School of Computer Science); Sergejs Aleksejevs (University of Manchester, School of Computer Science); Carole Goble (University of Manchester, School of Computer Science); Jacky Snoep (University of Manchester, Manchester Centre for Integrative Systems Biology); |
| Short Abstract: SysMO-DB is a web-based data exchange environment for scientists to share Systems Biology data and models. It provides an integrated platform for the dissemination of results and methodologies from the SysMO consortium (Systems Biology of Microorganisms), demonstrating an approach for managing the interoperation between models and experimental data. |
| Long Abstract: Click Here |
| Poster X018 |
| EFFICIENT QUERY-DRIVEN AND GLOBAL BICLUSTERING OF GENE EXPRESSION DTA USING PROBABILISTIC RALATIONAL MODELS |
| hui zhao- K.U.Leuven |
| Tim Van den Bulcke (KULeuven, ESAT); Lore Cloots (KULeuven, CMPG); Kristof Engelen (KULeuven, CMPG); Tom Michoel (VIB, PSB); Bart De Moor (KULeuven, ESAT); Kathleen Marchal (KULeuven, CMPG); |
| Short Abstract: ProBic is an efficient biclustering algorithm that simultaneously identifies a set of overlapping biclusters in a gene expression dataset. It can be used in both a query-based and a global mode. Experiments on synthetic data showed that biclusters are successfully identified under various settings, both in query-driven and global setting. |
| Long Abstract: Click Here |
| Poster X019 |
| Functional Class Scoring for Metabolomics |
| Henning Redestig- Riken Plant Science Center |
| Miyako Kusano (Riken Plant Science Center, Metabolomics Research Group); Fumio Matsuda (Riken Plant Science Center, Metabolomics Research Group); Akira Oikawa (Riken Plant Science Center, Metabolomics Research Group); Hiroshi Ezura (Tsukuba University, Gene Research Center); Masanori Arita (Plant Science Center, Metabolomics Research Group); Kazuki Saito (Plant Science Center, Metabolomics Research Group); |
| Short Abstract: The use of gene class testing has become routine analysis fortranscriptomics data. Improvements in profiling techniques has made itpossible to analyze the metabolome in a similar manner. Here wedescribe an implementation and application of metabolite class testingto data from a substantial equivalence study of transformed tomato. |
| Long Abstract: Click Here |
| Poster X020 |
| Identification and quantification of Granger causality between gene sets. |
| Andre Fujita- University of Tokyo / Institute of Medical Science |
| Joao Sato (Universidade Federal do ABC, Mathematics, Computation and Cognition Center); Kaname Kojima (University of Tokyo, Human Genome Center); Luciana Gomes (University of Sao Paulo, Biochemistry); Mari Sogayar (University of Sao Paulo, Biochemistry); Satoru Miyano (University of Tokyo, Human Genome Center); |
| Short Abstract: Granger has introduced the concept of Granger causality between two variables. We generalize this concept in order to identify Granger causalities between sets of gene expressions. An identification method with a bootstrap test is proposed. This method is applied in simulated and also in actual biological gene expression data. |
| Long Abstract: Click Here |
| Poster X021 |
| Comparative Analysis of Co-expressed Protein Interaction Networks Reveals Heart Failure Related Modules |
| Chen-Ching Lin- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University |
| No additional authors |
| Short Abstract: Heart failure stems from complicate causes and is one of the main causes of death in the world. We presented a novel computational network-based approach for integration of transcriptomic and proteomic data to identify heart failure related modules which may be potential disease markers and provide new directions for heart failure therapy. |
| Long Abstract: Click Here |
| Poster X022 |
| HOW PERFECT CAN PROTEIN INTERACTOMES BE? LESSONS FROM PHOSPHOPROTEOMES |
| Emmanuel Levy- Universite de Montreal |
| Christian Landry (Universite de Montreal, Biochemistry Department); Stephen Michnick (Universite de Montreal, Biochemistry Department); |
| Short Abstract: Phosphorylation sites (phosphosites) characterized by phosphoproteomics methods are not globally conserved compared to random serines and threonines, and are as likely to be polymorphic. However, among dataset of manually curated phosphosites, those of known function are significantly more conserved, suggesting that the rapid evolution of phosphoproteomes results from a fraction non-functional phosphosites. |
| Long Abstract: Click Here |
| Poster X023 |
| Automatic layout tool for large-scale metabolic pathway models based on KEGG Atlas and SBML/SBGN |
| Nobuhiro Kido- Institute for Advanced Biosciences, Keio University |
| Nobuaki Kono (Institute for Advanced Biosciences, Keio University, Bioinformatics); Kazuharu Arakawa (Institute for Advanced Biosciences, Keio University, Bioinformatics); Masaru Tomita (Institute for Advanced Biosciences, Keio University, Bioinformatics); |
| Short Abstract: We present a software tool that lays out large-scale metabolic pathway models in SBML format according to the coordinates of KEGG Atlas using SBGN, so that users can intuitively edit the model on CellDesigner. This tool is available at http://g-language.org/metal/. |
| Long Abstract: Click Here |
| Poster X024 |
| Validation of ChIP-chip targets by query-driven biclustering |
| Riet De Smet- KULeuven |
| Thomas Dhollander (KULeuven, ESAT); Inge Thijs (KULeuven, CMPG); Kristof Engelen (KULeuven, CMPG); Kathleen Marchal (KULeuven, CMPG); |
| Short Abstract: Due to problems with non-functional binding and experiment noise ChIP-chip experiments often yield a lot of false positive results. Here we illustrate how an improved query-driven biclustering method together with a microarray compendium can be used to verify and extend the knowledge on the regulatory network obtained by ChIP-chip. |
| Long Abstract: Click Here |
| Poster X025 |
| Executable Modeling of the EGFR Signalling Pathway. |
| Dennis Wang- University of Cambridge |
| Jasmin Fisher (Microsoft Research, Computational Biology); Luca Cardelli (Microsoft Research, Programming Principles and Tools); Nir Piterman (Imperial College London, Computing); Andrew Phillips (Microsoft Research, Computational Biology); |
| Short Abstract: Using stochastic pi-calculus we developed a model for describing the dynamics of the EGFR signaling pathway. Perturbation of the model allows us to characterize key control mechanisms in the signaling pathway. Partitioning the model into signaling modules summarizes the control mechanisms and facilitates the abstraction of the model. |
| Long Abstract: Click Here |
| Poster X026 |
| Probabilistic Network Clustering in Euclidean Space |
| Robert Gevers- Purdue University |
| Olga Vitek (Purdue University, Statistics); |
| Short Abstract: We propose an approach to clustering protein interaction networks that is based on embedding the network into Eucledian space. Advantages include a natural mechanism for incorporating additional, context-specific experimental measurements associated with the proteins, inference on the number of clusters, and probabilistic representations of cluster membership. |
| Long Abstract: Click Here |
| Poster X027 |
| Exploring type-2 Diabetes Protein Interactions Network by modeling their complex structures |
| Aggeliki Kosmopoulou- UPF |
| No additional authors |
| Short Abstract: The exact causes of type-2 diabetes mellitus (T2DM), a metabolic disorder that is primarily characterized by insulin resistance, relative insulin deficiency and hyperglycemia, are not completely understood. Here, we apply a method for delineating the interacting motifs of hub proteins to discover their roles in type-2 Diabetes Protein Interaction Network. |
| Long Abstract: Click Here |
| Poster X028 |
| IDENTIFICATION OF PROTEIN-PROTEIN INTERACTION DOMAINS |
| SALIHA OZBABACAN- KOC UNIVERSITY |
| ATILLA GURSOY (KOC UNIVERSITY, CENTER FOR COMPUTATIONAL BIOLOGY AND BIOINFORMATICS AND COLLEGE OF ENGINEERING); OZLEM KESKIN (KOC UNIVERSITY, CENTER FOR COMPUTATIONAL BIOLOGY AND BIOINFORMATICS AND COLLEGE OF ENGINEERING); |
| Short Abstract: This project focuses on interaction domains, which play a crucial role in the regulation of different cellular processes, through two aspects: by classifying interaction domains in order to differentiate them from other domains via machine learning tool and by structurally comparing them with the nonredundant template interfaces obtained from PDB. |
| Long Abstract: Click Here |
| Poster X029 |
| Atacking Interface & Interaction Networks |
| Billur Engin- Koc University |
| Ekin Tuzun (Koc University, Center for Computational Biology and Bioinformatics and College of Engineering); Atilla Gursoy (Koc University, Center for Computational Biology and Bioinformatics and College of Engineering); Ozlem Keskin (Koc University, Center for Computational Biology and Bioinformatics and College of Engineering); |
| Short Abstract: Here, networks resulting from integration of binding site information of the proteins into PPI networks (Interface & Interaction Networks) are presented. Various attack strategies with several network parameters are performed on these networks and we analyzed the robustness of these networks. |
| Long Abstract: Click Here |
| Poster X030 |
| Human Protein-Protein Interaction Network: a Structural Perspective |
| Gozde Kar- Koc University |
| Attila Gursoy (Koc University, Center for Computational Biology and Bioinformatics); Ozlem Keskin (Koc University, Center for Computational Biology and Bioinformatics); |
| Short Abstract: We provide a detailed analysis of human protein-protein interaction network which contains cancer-related interactions and characterize the interactions using three-dimensional protein structures. The results reveal distinctive characteristics of cancer-related interactions; they have smaller, more planar and more hydrophilic binding sites which may indicate low affinity and high specificity of them. |
| Long Abstract: Click Here |
| Poster X031 |
| Pathway Projector: Web-based Zoomable Pathway Browser using KEGG Atlas and Google Maps API |
| Nobuaki Kono- Institute for Advanced Biosciences, Keio University |
| Kazuharu Arakawa (Institute for Advanced Biosciences, Keio University, Media & Governance); Nobuhiro Kido (Institute for Advanced Biosciences, Keio University, Environment & Info.); Ryu Ogawa (Institute for Advanced Biosciences, Keio University, Media & Governance); Kazuki Oshita (Institute for Advanced Biosciences, Keio University, Environment & Info.); Keita Ikegami (Institute for Advanced Biosciences, Keio University, Environment & Info.); Satoshi Tamaki (Institute for Advanced Biosciences, Keio University, Environment & Info.); Masaru Tomita (Institute for Advanced Biosciences, Keio University, Environment & Info.); |
| Short Abstract: Pathway Projector is a web application for browsing global pathway map based on KEGG Atlas, with zoomable user interface implemented using Google Maps API. This application allows numerous interactive queries, as well as data mapping from -omics experiments. Pathway Projector is available at: http://www.g-language.org/PathwayProjector/. |
| Long Abstract: Click Here |
| Poster X032 |
| Stochastic Simulation of T-Cell Activation |
| Wolfgang Schreiner- Medical University Vienna |
| Bernhard Knapp (Medical University Vienna, Medical statistics and Informatics); rudolf Karch (Medical University Vienna, Medical statistics and Informatics); Michael Cibena (Medical University Vienna, Medical statistics and Informatics); |
| Short Abstract: We present an agent based simulation of epitope detection by MHC-peptide-TCR – complexes. Cooperative phenomena are modelled to investigate the mechanism that gives rise to a realistic discrimination between self and pathogen peptides. |
| Long Abstract: Click Here |
| Poster X033 |
| Analysing perturbational effects at systems level by data-integration. |
| Anagha Joshi- VIB/ Gent university |
| Tom Michoel (VIB/ Gent university, Plants systems biology); Yves van de Peer (VIB/ Gent university, Plants systems biology); Thomas van Parys (VIB/ Gent university, Plants systems biology); |
| Short Abstract: when transcription factor is perturbed, we observed that only 2% ofdifferentially expressed targets were direct transcriptional. We used a compendium of overexpression and deletion experiments in S. cerevisiae and examinedregulatory paths which combine transcriptional, protein-protein and phosphorylation interactions. We found 8 overrepresented paths which explain 10% of differentially expressed targets. |
| Long Abstract: Click Here |
| Poster X034 |
| Exploring Transcription Factor Target Gene Relationships by Condition-dependent Module Networks |
| Hasan Ogul- Helsinki University of Technology |
| Jarkko Salojarvi (Helsinki University of Technology, Department of Information and Computer Science); Pinja Jaspers (University of Helsinki, Department of Biological and Environmental Sciences); Antti Ajanki (Helsinki University of Technology, Department of Information and Computer Science); Mikael Brosché (University of Helsinki, Department of Biological and Environmental Sciences); Jaakko Kangasjärvi (University of Helsinki, Department of Biological and Environmental Sciences); Samuel Kaski (Helsinki University of Technology, Department of Information and Computer Science); |
| Short Abstract: We present a probabilistic model for inferring local regulatory networks and transcription factor target gene relationships based on condition-dependent Bayesian module networks and promoter motif analysis. |
| Long Abstract: Click Here |
| Poster X035 |
| From the zebrafish embryo toxicity test to systems toxicology: The development of a multifactorial knowledge base and bioinformatics tools to optimise data interpretation |
| Marion Reuter- Fraunhofer Gesellschaft |
| Martina Fenske (Fraunhofer Gesellschaft, IME); Torben Söker (Fraunhofer Gesellschaft, IME); Christoph Schäfers (Fraunhofer Gesellschaft, IME); Viktoria Schiller (Fraunhofer Gesellschaft, IME); Arne Wichmann (Fraunhofer Gesellschaft, IME); |
| Short Abstract: UNIFISH uses phenotypic and systemic changes in zebrafish embryos to assess chemical toxicity and to screen bioactive compounds. Experimental data together with public database information are compiled and managed in a multifactorial knowledge base. The knowledge base provides the fundamental basis for the development of customised automated high-throughput testing systems. |
| Long Abstract: Click Here |
| Poster X036 |
| Integrative Analysis of Type 2 Diabetes Protein Interaction Network and OMIM Database Reveals Associations between Complex Disorders |
| Binnaz Coskunkan- Yeditepe University |
| Deniz Rende (Yeditepe University and Bogazici University, Department of Chemical Engineering); Nihat Baysal (Yeditepe University, Department of Chemical Engineering); Betul Kirdar (Bogazici University, Department of Chemical Engineering); |
| Short Abstract: This study aims to investigate the associations between type 2 diabetes and other complex diseases. Disease related protein interaction network was constructed, transformed to a line graph and clustered into functional modules. The integration of pathway, localization and disease terms with functional modules indicates strong coherence of complex diseases. |
| Long Abstract: Click Here |
| Poster X037 |
| Human gene coexpression network built with a robust reverse engineering method: deciphering functional, transcriptional and topological information |
| Carlos Prieto- Cancer Research Center (CIC-IMBCC, CSIC/USAL) |
| Carlos Prieto (Cancer Research Center (CIC-IMBCC, CSIC/USAL), Bioinformatics and Functional Genomics Research Group); Alberto Risueño (Cancer Research Center (CIC-IMBCC, CSIC/USAL), Bioinformatics and Functional Genomics Research Group); Celia Fontanillo (Cancer Research Center (CIC-IMBCC, CSIC/USAL), Bioinformatics and Functional Genomics Research Group); Javier De Las Rivas (Cancer Research Center (CIC-IMBCC, CSIC/USAL), Bioinformatics and Functional Genomics Research Group); |
| Short Abstract: Human genome-wide expression data from normal-healthy tissues is used to built a confident human gene coexpression network with a new robust method. Further functional and topology analysis of the network defines critical nodes and shows functional coherent biological modules that share common transcription factors. |
| Long Abstract: Click Here |
| Poster X038 |
| Finding new drug candidates against Mycobacterium tuberculosis using protein-protein interaction networks |
| Gabor Ivan- PhD Student |
| Dániel Bánky (Eotvos Lorand University, Department of Computer Science); Vince Grolmusz (Eotvos Lorand University, Department of Computer Science); |
| Short Abstract: We created the metabolic (including the MTB-specific mycolic acid pathway) and physical protein-protein interaction (PPI) networks of Mycobacterium tuberculosis (MTB). We then merged the two types of networks to a single one which is currently being used for estimating new protein targets for drug candidates against MTB. |
| Long Abstract: Click Here |
| Poster X039 |
| A method for atherosclerotic plaque growth based on a markup representation of blood flow simulation |
| Stefanos Konstantinos Petsios- Unit of Medical Technology & Intelligent Information Systems University of Ioannina |
| Antonis Sakellarios (Unit of Medical Technology & Intelligent Information Systems University of Ioannina, Computer Science); Dimitrios I. Fotiadis (Unit of Medical Technology & Intelligent Information Systems University of Ioannina, Computer Science); |
| Short Abstract: We propose a formal XML like representation of blood flow simulation (BFS) results. It contains the required data for preprocessing, the produced results and the metadata related with the BFS. We use these results to construct a mathematical model of the early plaque formation in the artery and blood flow. |
| Long Abstract: Click Here |
| Poster X040 |
| Using monochromatic purity to select gene sets |
| Magali Michaut- University of Toronto |
| Gary Bader (University of Toronto, Terrence Donnelly CCBR); |
| Short Abstract: We aim to automatically select gene sets from GO at an appropriate level of the hierarchy to best annotate a given genomic data set. We analyze quantitative positive and negative genetic interactions and select gene sets by monochromatic purity (the ratio of positive to negative interactions). |
| Long Abstract: Click Here |
| Poster X041 |
| Deciphering the regulation of cell cycle progression by cell adhesion using an integrated network of gene interactions |
| Marcio Acencio- Sao Paulo State University - Institute of Biosciences of Botucatu |
| No additional authors |
| Short Abstract: To better understand the regulation of cell cycle progression by celladhesion, we constructed a human integrated network of geneinteractions likely involved with this phenotype and then calculatednetwork centrality measures and performed functional analysis toreveal important pathways and genes involved with the regulation of cell cycle progression bycell adhesion. |
| Long Abstract: Click Here |
| Poster X042 |
| Beyond linearity in the relationship between DNA copy number alterations and mRNA expression in Breast Cancer |
| Hiroko Solvang- Norwegian Radium Hospital University Hospital, University of Oslo |
| Ole Christian Lingjærde (University of Oslo, Department of Informatics); Arnoldo Frigessi (University of Oslo, Institute of Basic Medical Research); Vessela Kristensen (Norwegian Radiumhospital, University of Oslo, Department of Genetics, Medical Faculty); Anne-Lise Børresen-Dale (Norwegian Radium Hospital, University of Oslo, Department of Genetics); |
| Short Abstract: For the investigation related to the variation in gene expression attributable to variation in gene copy number among breast tumors, we propose a statistical analysis including comparison with the linear and nonlinear relationship to identify the regulatory mechanisms of abnormal mRNA expression. |
| Long Abstract: Click Here |
| Poster X043 |
| Dissecting disease progression of Chronic Lymphocytic Leukemia using an integrated quantitative proteomic and genomic analysis |
| Han-Yu Chuang- Univ. Of California San Diego |
| Zhouxin Shen (UCSD, Biology); Laura Ressenti (UCSD, Medicine); Steve Briggs (UCSD, Biology); Thomas Kipps (UCSD, Medicine); Trey Ideker (UCSD, Bioengineering); |
| Short Abstract: We developed a shotgun method to quantify protein expression correlated with progression of chronic lymphocytic leukemia. Our protein markers are more functionally related, consistent with gene expression and robust across patient cohorts. We found that protein differential expression is coherent with their interaction, enabling prediction of expression for unmeasured proteins. |
| Long Abstract: Click Here |
| Poster X044 |
| A WEB-BASED APPLICATION FOR EVALUATING PARAMETER IDENTIFIABILITY OF BIOCHEMICAL MODELS IN POLYNOMIAL FORM |
| Marcos Simoes- INESC-ID Lisbon |
| Susana Vinga (INESC-ID Lisbon, kdbio Group); |
| Short Abstract: State-space models are often used for the quantitative modelling of biochemical systems. Alongside parameter estimation, a necessary (and early) step in the building of such models is the assessment of structural identifiability properties. We present a web-based application that computes these properties for polynomial models in a completely automated manner. |
| Long Abstract: Click Here |
| Poster X045 |
| Core stemness mechanisms revealed through homology |
| Martina Koeva- University of California, Santa Cruz |
| Josh Stuart (University of California, Santa Cruz, Department of Biomolecular Engineering); Camilla Forsberg (University of California, Santa Cruz, Department of Biomolecular Engineering); |
| Short Abstract: Few stemness genes required for core stem cell function across many stem cell types have been identified through transcriptional profiling. We developed a computational approach to test for stemness groups, which show frequent upregulation in many stem cell types and utilize paralogous genes for functionally similar or equivalent purposes. |
| Long Abstract: Click Here |
| Poster X046 |
| Modeling the Dynamics of SIRT1 Regulation |
| Augustin Luna- Boston University/National Cancer Institute |
| Kurt Kohn (National Cancer Institute, Laboratory of Molecular Pharmacology); Geoffrey McFadden (National Institute of Standards and Technology, Mathematical Modeling Group); Mirit Aladjem (National Cancer Institute, Laboratory of Molecular Pharmacology); |
| Short Abstract: We propose a mathematical model describing the dynamics of control loops involved in the regulation of SIRT1, a histone deacetylase implicated in aging. The model is the basis for simulation studies aimed at determining how SIRT1 levels affect elements critical to the DNA damage response induced by ionizing radiation. |
| Long Abstract: Click Here |
| Poster X047 |
| Inferring gene networks from short time-series microarray datasets under different biological conditions |
| Teppei Shimamura- University of Tokyo |
| Teppei Shimamura (University of Tokyo, Human Genome Center, Institute of Medical Science); Seiya Imoto (University of Tokyo, Human Genome Center, Institute of Medical Science); Rui Yamaguchi (University of Tokyo, Human Genome Center, Institute of Medical Science); Masao Nagasaki (University of Tokyo, Human Genome Center, Institute of Medical Science); Yoshinori Tamada (University of Tokyo, Human Genome Center, Institute of Medical Science); Satoru Miyano (University of Tokyo, Human Genome Center, Institute of Medical Science); |
| Short Abstract: We develop a new approach for incorporating multiple time-series datasets measuring the expressions of the same set of genes under different experimental conditions in inferring gene network from short time-series data. We apply this methodology to structure learning of dynamic Bayesian networks from multiple short time-series datasets. |
| Long Abstract: Click Here |
| Poster X048 |
| Neutral Network models for Biochemical Systems |
| Marco Vilela- ITQB/UNL |
| Susana Vinga (INESC, Bioinformatics); Marco Grivet Mattoso Maia (PUC, Centro de Estudo em Telecomunicações); Eberhard Voit (Georgia Institute of Technology and Emory University, Integrative BioSystems Institute and Dept. Biomedical Engineering); Jonas Almeida (University of Texas M.D. Anderson Cancer Center, Dept. Bioinformatics and Computational Biology); |
| Short Abstract: The major difficulty in modeling biological systems from time series is the identification of parameter sets that gives the model a desired behavior. This work shows that different parameter sets with different model’s structure can give rise to similar dynamical behavior, relating it to the system’s robustness and evolvability |
| Long Abstract: Click Here |
| Poster X049 |
| From Use Case to User Base: Developing a 3R Implementation for Community Use. |
| Hannah Tipney- University of Colorado Denver |
| William Baumgartner Jr (University of Colorado Denver, Center for Computational Pharmacology, School of Medicine); Ronald Schuyler (University of Colorado Denver, Center for Computational Pharmacology, School of Medicine); Lawrence Hunter (University of Colorado Denver, Center for Computational Pharmacology, School of Medicine); |
| Short Abstract: Although hugely useful as investigative tools, networks and associated software are often viewed as impenetrable by biologists, the very users they are supposed to support. We present several barriers to network use and illustrate how we addressed them, moving the Hanalyzer from its interesting use case to wider user base. |
| Long Abstract: Click Here |
| Poster X050 |
| An atlas of combinatorial transcriptional regulation in mouse and man |
| Carlo Cannistraci- Polytechnic of Turin |
| Vladimir B. Bajic (University of the Western Cape, South African National Bioinformatics Institute); Kai Tan (University of California San Diego, Department of Bioengineering, Jacobs School of Engineering); Shintaro Katayama (RIKEN Yokohama Institute, RIKEN Omics Science Center); Alistair R. R. Forrest (Griffith University, The Eskitis Institute for Cell and Molecular Therapies); Nicolas Bertin (RIKEN Yokohama Institute , RIKEN Omics Science Center); Piero Carnici (RIKEN Yokohama Institute , RIKEN Omics Science Center); Jesper Tegnér ( Karolinska University Hospital Solna SE, Computational Medicine Group, Atherosclerosis Research Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet); Sarah A. Teichmann (Cambridge CB2 0QH , MRC Laboratory of Molecular Biology); Harukazu Suzuki (RIKEN Yokohama Institute , RIKEN Omics Science Center); Yoshihide Hayashizaki (RIKEN Yokohama Institute , RIKEN Omics Science Center); Trey Ideker (University of California San Diego , Department of Bioengineering, Jacobs School of Engineering); Timothy Ravasi (University of California San Diego , Department of Bioengineering, Jacobs School of Engineering); |
| Short Abstract: We present a systems approach to study mammalian transcription regulatory networks (TRNs). By integration of genome-wide measurements of several cellular components across tissues and time, we identified two distinct classes of TRN hubs displaying specific topological and functional properties. Furthermore we mapped tissues&time-specific combinatorial regulatory sub-networks. |
| Long Abstract: Click Here |
| Poster X051 |
| Introduction and application of CellExpress, a new database for studying human tissue-specific gene expression |
| Larisa Kiseleva- AIST |
| Raymond Wan (AIST, CBRC); Paul Horton (AIST, CBRC); |
| Short Abstract: We introduce the CellExpress database, the storing gene expression data from (currently) 10,000 samples classified in a tissue specific manner. The data gathered has been used for visualizing human cell/tissue type relationships and identifying tissue-specific transcription factors. |
| Long Abstract: Click Here |
| Poster X052 |
| Asymmetric relationships between proteins shape genome evolution |
| Bas Dutilh- Radboud University Nijmegen Medical Centre |
| Richard A. Notebaart (Radboud University Nijmegen Medical Centre, CMBI); Philip R. Kensche (Radboud University Nijmegen Medical Centre, CMBI); Martijn A. Huynen (Radboud University Nijmegen Medical Centre, CMBI); |
| Short Abstract: Relationships between enzymes are often asymmetric: protein A requires protein B to function, but B can function without A (A->B). This asymmetry predictably biases genomic variables like presence of orthologs, gene expression and the effect of knockouts. This bias is strongest for proteins whose asymmetric relationship is evolutionarily conserved. |
| Long Abstract: Click Here |
| Poster X053 |
| Predicting pathways in yeast using genome-wide phenotype data. |
| Brian Peyser- USAMRIID |
| No additional authors |
| Short Abstract: I developed a score connecting yeast deletion mutants by adapting Resnik's application of shared information content. Translation of the shared information technique to distributions of phenotypes generated similarity scores which connect genes with related functions. This does not require similar phenotype data distributions and scores are easily updated. |
| Long Abstract: Click Here |
| Poster X054 |
| HEFalMp: Integrating 30,000 experimental conditions to predict systems-level relationships in H. sapiens |
| Curtis Huttenhower- Princeton University |
| Erin Haley (Princeton University, Molecular Biology); Hilary Coller (Princeton University, Molecular Biology); Olga Troyanskaya (Princeton University, Computer Science); |
| Short Abstract: The HEFalMp system is a data integration technique that produces functional maps, each summarizing the results most relevant to a particular gene, pathway, or disease of interest. These comprise over 200 areas of human cellular biology, include information from ~30,000 genome-scale experiments, and provide a focus on the molecular mechanisms of genetic disorders. |
| Long Abstract: Click Here |
| Poster X056 |
| Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data |
| Tijana Milenkovic- University of California, Irvine |
| Natasa Przulj (University of California, Irvine, Computer Science); Vesna Memisevic (University of California, Irvine, Computer Science); Anand Ganesan (University of California, Dermatology and Biological Chemistry); |
| Short Abstract: We use a sensitive graph-theoretic method for comparing local structures of node neighborhoods to demonstrate that in human protein interaction network, topology around cancer genes is different from topology around non-cancer genes. From this observation, we identify novel cancer gene candidates, validating our predictions both biologically and in the literature. |
| Long Abstract: Click Here |
| Poster X057 |
| Biological convergence of cancer signatures |
| Xavier Solé- Catalan Institute of Oncology - IDIBELL |
| Núria Bonifaci (Catalan Institute of Oncology, IDIBELL, Bioinformatics and Biostatistics Unit); Núria López-Bigas (Barcelona Biomedical Research Park, Research Unit on Biomedical Informatics of IMIM/UPF); Antoni Berenguer (Catalan Institute of Oncology, IDIBELL, Bioinformatics and Biostatistics Unit); Pilar Hernández (Catalan Institute of Oncology, IDIBELL, Translational Research Laboratory); Oscar Reina (Catalan Institute of Oncology, IDIBELL, Unit of Infections and Cancer, CIBERESP, Epidemiology Research of Cancer Program); Christopher A. Maxwell (Catalan Institute of Oncology, IDIBELL, Translational Research Laboratory); Helena Aguilar (Catalan Institute of Oncology, IDIBELL, Translational Research Laboratory); Ander Urruticoechea (Catalan Institute of Oncology, IDIBELL, Translational Research Laboratory); Silvia de Sanjosé (Catalan Institute of Oncology, IDIBELL, Unit of Infections and Cancer, CIBERESP, Epidemiology Research of Cancer Program); Francesc Comellas (Technical University of Catalonia, Department of Applied Mathematics IV); Gabriel Capellá (Catalan Institute of Oncology, IDIBELL, Translational Research Laboratory); Víctor Moreno (Catalan Institute of Oncology, IDIBELL, Bioinformatics and Biostatistics Unit); Miguel Angel Pujana (Catalan Institute of Oncology, IDIBELL, Bioinformatics and Biostatistics Unit); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster X058 |
| Transcriptional gene regulatory network analysis with CoryneRegNet |
| Jan Baumbach- International Computer Science Institute |
| No additional authors |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster X059 |
| Co-Expression among Constituents of a Motif in the Protein-Protein Interaction Network |
| Nitin Bhardwaj- Yale University |
| No additional authors |
| Short Abstract: We show that the robustness of protein interaction system emerges from a proportionate synchronicity among the constituents of motifs via co-expression and this co-expression correlates strongly with the motif complexity. We further show that such biological coherence among component proteins can be reinforced by integrating conservation data in the analysis. |
| Long Abstract: Click Here |
| Poster X060 |
| Analysis of Gene Sets Based on the Underlying Regulatory Network |
| Ali Shojaie- Student/University of Michigan |
| George Michailidis (University of Michigan, Statistics); |
| Short Abstract: We consider simultaneous analysis of changes in expression levels and interactions among genes. We propose a latent variable model that can directly incorporate network information. Exploiting the flexibility of mixed linear models, we propose an efficient inference procedure for analysis of pathways which isolates the effect of each subnetwork. |
| Long Abstract: Click Here |
| Poster X061 |
| Benchmarking examples for pathway analysis tools |
| Luis de Figueiredo- Friedrich-Schiller-Universität Jena |
| Stefan Schuster (Friedrich-Schiller-Universität Jena, Lehrstuhl Bioinformatik); Christoph Kaleta (Friedrich-Schiller-Universität Jena, Lehrstuhl Bioinformatik); David A. Fell (Oxford Brookes University, School of Life Sciences); |
| Short Abstract: The concept of elementary flux modes has been used for prediction of metabolic pathways. Alternative approaches have been proposed, which are based on graph theory and neglect the mass balance of co-substrates and by-products. Here, we present benchmark examples by which pathway analysis tools can be compared. |
| Long Abstract: Click Here |
| Poster X062 |
| Robust simplifications of multiscale biochemical networks with application to NFkB modeling |
| Andrei Zinovyev- Institut Curie |
| Ovidiu Radulescu (University of Rennes, Mathematics); Alexander Gorban (University of Leicester, Mathematics); Alain Liliennbaum (CNRS, UMR 7000); |
| Short Abstract: We develop a limitation-based theory of model reduction both for linear and nonlinear networks. We demonstrate how our methods can be applied for simplifying a complex model of NFkB signalling. Our approach generates a hierarchy of simplified models, levels of which can be compared with existing models of NFkB signalling. |
| Long Abstract: Click Here |
| Poster X063 |
| On the use and misuse of GO biological process annotations |
| Monica Chagoyen- Centro Nacional de Biotecnologia - CSIC |
| Florencio Pazos (Centro Nacional de Biotecnologia - CSIC , de); |
| Short Abstract: We analyze the biological cohesiveness of the processes in the Gene Ontology (GO) using data from STRING functional network. We also evaluate the direct and indirect relationships established in GO, and discuss its implications for the analysis of system-wide data. |
| Long Abstract: Click Here |
| Poster X064 |
| Generalized Nested Effects Models for Reconstructing Protein Signaling Networks from Multiple Interventions |
| Christian Bender- German Cancer Research Center |
| Holger Fröhlich (Cellzome AG, Bioinformatics); Özgur Sahin (German Cancer Research Center, Molecular Genome Analysis); Dorit Arlt (German Cancer Research Center, Molecular Genome Analysis); Tim Beissbarth (University of Goettingen, Medical Statistics); |
| Short Abstract: In this work we propose an approach to analyse protein expression data after siRNA-mediated knockdowns called 'Generalized Nested Effects Models'. We monitor the intervention effects in few timepoint-measurements. Reconstruction of signaling networks happens by combining incoming signals at each node via boolean functions and propagating effects accordingly through the network. |
| Long Abstract: Click Here |
| Poster X065 |
| InnateDB and Cerebral Facilitate the Systems-Level Analysis of Large Datasets: Case Studies |
| Jennifer Gardy- University Of British Columbia |
| David Lynn (Simon Fraser University, Department of Molecular Biology & Biochemistry); Robert Hancock (University Of British Columbia, Centre For Microbial Diseases & Immunity Research); Fiona Brinkman (Simon Fraser University, Department of Molecular Biology & Biochemistry); |
| Short Abstract: InnateDB (www.innatedb.ca) is a comprehensive interaction/pathway database and systems biology analysis environment designed to facilitate analysis and visualization (through Cytoscape/Cerebral) of complex quantitative datasets from human and mouse. We describe a workflow taking an InnateDB user from spreadsheet to network, and apply this workflow to complex host response expression profiles. |
| Long Abstract: Click Here |
| Poster X066 |
| Learning the role of the transcriptional co-regulator NC2 in early mesoderm development |
| Mikhail Spivakov- European Molecular Biology Laboratory |
| Hitoshi Aihara (Cornell University, Weill Medical College); Ya-Hsin Liu (European Molecular Biology Laboratory, Gene Expression Unit); Robert Zinzen (European Molecular Biology Laboratory, Gene Expression Unit); Charles Girardot (European Molecular Biology Laboratory, Gene Expression Unit); Martina Braun (European Molecular Biology Laboratory, Gene Expression Unit); Yutaka Nibu (Cornell University, Weill Medical College); Ewan Birney (European Molecular Biology Laboratory, EMBL-EBI); Eileen Furlong (European Molecular Biology Laboratory, Gene Expression Unit); |
| Short Abstract: We analysed the binding of transcriptional regulator NC2 in Drosophila mesoderm and integrated the data with our previous analyses of mesodermal transcriptional networks. The application of statistical learning approaches suggests a co-operative effect of NC2 and Twist in the 'priming' of mesodermal CRMs for future binding of lineage-specific transcription factors. |
| Long Abstract: Click Here |
| Poster X067 |
| A Method for Inference of Gene Regulatory Networks based on Bayesian Network with Clustering of Time-Series Subsequences |
| Yuya Shuto- Graduate School of Information Science and Technology, Osaka University |
| Shigeto Seno (Graduate School of Information Science and Technology, Osaka University, Bioinfomatic Engineering); Yoichi Takenaka (Graduate School of Information Science and Technology, Osaka University, Bioinfomatic Engineering); Hideo Matsuda (Graduate School of Information Science and Technology, Osaka University, Bioinfomatic Engineering); |
| Short Abstract: We propose a method for the inference of gene regulatory networks based on Bayesian network with subsequences of time-series gene expression profiles. Proposed method is effective for analysis of time-series expression data and infers more accurate networks than the other existing methods. |
| Long Abstract: Click Here |
| Poster X068 |
| Mathematical modelling of cell-fate decision in response to death receptor engagement |
| Laurent Tournier- Institut Curie |
| Laurence Calzone (Institut Curie, U900 INSERM - Curie - Ecole des Mines ParisTech); Simon Fourquet (Institut Curie, U900 INSERM - Curie - Ecole des Mines ParisTech); Emmanuel Barillot (Institut Curie, U900 INSERM - Curie - Ecole des Mines ParisTech); Andrei Zinovyev (Institut Curie, U900 INSERM - Curie - Ecole des Mines ParisTech); Denis Thieffry (Technologies Avancées pour le Génome et la Clinique, Département de Biologie); |
| Short Abstract: We present a model of cell decision between three cellular fates: survival, apoptosis and non-apoptotic cell death, in response to death receptor engagement. A qualitative dynamical model is constructed, and analysed using discrete systems techniques. The obtained qualitative results are then used to validate the network structure. |
| Long Abstract: Click Here |
| Poster X069 |
| Semantic signature: comparative concept lattice analysis for gene expression microarray data on a semantic space |
| Jihun Kim- Seoul National University Biomedical Informatics (SNUBI) |
| Keewon Kim (Seoul National University Biomedical Informatics, Department of medicine); SangJay Bien (Seoul National University Biomedical Informatics, Department of medicine); Juhan Kim (Seoul National University Biomedical Informatics, Department of medicine); |
| Short Abstract: In this study, the semantic space was constructed as absolute universal platform for the comparative microarray analysis. Comparing different geographies of concept lattices from different categories of microarray experiments revealed the semantic signatures. |
| Long Abstract: Click Here |
| Poster X070 |
| Discovering cancer pathways through logic network inference |
| Jeroen de Ridder- Delft University of Technology |
| Jan Bot (Delft University of Technology, Bioinformatics Group); Jaap Kool (Netherlands Cancer Institute, Division of Molecular Genetics); Anthony Uren (Netherlands Cancer Institute, Division of Molecular Genetics); Marcel Reinders (Delft University of Technology, Bioinformatics Group); Lodewyk Wessels (Netherlands Cancer Institute, Bioinformatics and Statistics Group); |
| Short Abstract: In this study we have expression profiled 43 tumors that were induced by retroviral insertional mutagenesis. From these data we infer combinatorial association logic networks - using a novel method - that are capable of capturing complex associations between the initiating events (the viral integration sites) and the consequent downstream expression profiles. |
| Long Abstract: Click Here |
| Poster X071 |
| Ondex data integration applied to the annotation of durum wheat time-series microarray data |
| Matthew Hindle- Rothamsted Research |
| Marcela Baudo (Rothamsted Research, Plant Science); Micheal Defoin-Platel (Rothamsted Research, Biomathematics and Bioinformatics); Stephen Powers (Rothamsted Research, Biomathematics and Bioinformatics); Charlie Hodgman (University of Nottingham, Multidisciplinary Centre for Integrative Biology); Christopher Rawlings (Rothamsted Research, Biomathematics and Bioinformatics); Mansoor Saqi (Rothamsted Research, Bioinformatics and Biomathmatics); Dimah Habash (Rothamsted Research, Plant Science); |
| Short Abstract: A common challenge in microarray analysis is the compiling of up-to-date annotations from multiple biological domains. This situation is compounded in partially sequenced organisms like durum wheat. This study demonstrates how data integration techniques can be used to extract the latest data from multiple databases that span disparate biological domains. |
| Long Abstract: Click Here |
| Poster X072 |
| Characterizing microRNA regulatory modules that govern combinatorial regulation using matched microRNA-mRNA expression profile data |
| Young-Ji Na- Seoul National University Biomedical Informatics (SNUBI) |
| Ju Han Kim (Seoul National University Biomedical Informatics (SNUBI) , Biomedical science); |
| Short Abstract: There are attempts to identify miRNA regulatory modules by research groups. The novelty of our approach is characterizing miRNA regulatory modules that govern combinatorial regulation. We report that the distinct regulatory roles of the miRNAs using matched microRNA-mRNA expression profile data. |
| Long Abstract: Click Here |
| Poster X073 |
| Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation |
| Daniele Merico- University of Toronto |
| Ruth Isserlin (University of Toronto, Banting & Best Dept. of Medical Research - Donnelly CCBR); Oliver Stueker (University of Toronto, Banting & Best Dept. of Medical Research - Donnelly CCBR); Andrew Emili (University of Toronto, Banting & Best Dept. of Medical Research - Donnelly CCBR); Gary Bader (University of Toronto, Banting & Best Dept. of Medical Research - Donnelly CCBR); |
| Short Abstract: High-throughput genomic experiments typically lead to the identification of large gene-lists. Gene-set enrichment analysis has been successfully applied to functionally summarize such data. However, the number and mutual overlap of gene-sets constitutes a critical barrier. To address this problem, we propose a network visualization of enrichment results, called Enrichment Map. |
| Long Abstract: Click Here |
| Poster X074 |
| A framework for identifying cross patterns in systems biology: application to chemogenomics |
| Tara Gianoulis- Yale University |
| Ashish Agarwal (Yale University, Molecular Biophysics and Biochemistry); Michael Snyder (Yale University, Molecular, Cellular, and Developmental Biology); Mark Gerstein (Yale University, Computational Biology & Bioinformatics, Molecular Biophysics & Biochemistry, Computer Science); |
| Short Abstract: Straightforward integration, as currently done in genomics, does not provide enough flexibilitywhen datasets can no longer be indexed on a single class of variable. We developed a formalism to identify “cross patterns” between differently indexed metadata and applied this concept to reveal novel and non-obvious connections in chemogenomics data. |
| Long Abstract: Click Here |
| Poster X075 |
| Metabolite and reaction inference based on enzyme specificities |
| Dick de Ridder- Delft University Of Technology |
| Marco J.L. de Groot (Delft University of Technology, Bioinformatics Lab, Faculty Of Electrical Engineering, Mathematics And Computer Science); Rogier J.P. van Berlo (Delft University of Technology, Bioinformatics Lab, Faculty Of Electrical Engineering, Mathematics And Computer Science); Wouter A. van Winden (Delft University of Technology, Bioprocess Technology Group, Dept. of Biotechnology); Peter J.T. Verheijen (Delft University of Technology, Bioprocess Technology Group, Dept. of Biotechnology); Sef J. Heijnen (Delft University of Technology, Bioprocess Technology Group, Dept. of Biotechnology); Marcel J.T. Reinders (Delft University of Technology, Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science); |
| Short Abstract: Many enzymes are aspecific: they can catalyze transformations of more compounds than the traditional ones listed in e.g. KEGG. We model this aspecificity by predicting whether a certain input substrate is likely to be transformed by an enzyme. To train this predictor, we use the BRENDA enzyme activity database. |
| Long Abstract: Click Here |
| Poster X076 |
| Integrated Weighted Gene Co-expression Module Analyses with an Application to Sheep Gastrointestinal Nematode Infection |
| Haja Kadarmideen- Commonwealth Scientific and Industrial Research Organisation (CSIRO) |
| Nick Andronicos (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Livestock Industries); Nathan Watson-Haigh (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Livestock Industries); |
| Short Abstract: Microarray gene expression data, from sheep gastrointestinal nematode infection experiments, were used to detect/annotate differentially expressed genes across parasites, tissues and length of infection. We constructed four weighted gene co-expression networks and detected several important gene modules. Biologically relevant candidate genes/biomarkers were then identified using systems biology approach. |
| Long Abstract: Click Here |
| Poster X077 |
| Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics |
| Tarmo Äijö- Tampere University of Technology |
| Harri Lähdesmäki (Tampere University of Technology, Department of Signal Processing); |
| Short Abstract: We propose a novel method for modeling transcriptional level regulation which is built on the use of Bayesian analysis with ordinary differential equations and nonparametric Gaussian process modeling. Evaluation of the proposed structure and dynamics inference method using a recently published in vivo data set demonstrates improved performance. |
| Long Abstract: Click Here |
| Poster X078 |
| Logical Modeling of Gene Regulatory Networks |
| Guy Karlebach- Tel-Aviv University |
| Ron Shamir (Tel-Aviv University, The Blavatnik school of computer science); |
| Short Abstract: Understanding gene regulatory networks is a challenge. We present an algorithm that determines the smallest perturbation sets for manipulating the dynamics of a network and implement it using Petri nets. By modifying McMillan’s unfolding algorithm, we handle partial knowledge and reduce computation cost. The methodology is demonstrated on a glioma network |
| Long Abstract: Click Here |
| Poster X079 |
| Scoring disease relevance of proteins through network topology mediated information dissemination |
| Emre Guney- Pompeu Fabra University |
| Oriol Fornes (Pompeu Fabra University, Health and Experimental Sciences); Baldo Oliva (Pompeu Fabra University, Health and Experimental Sciences); |
| Short Abstract: Based on the intuition that crucial proteins involved in a specific biological pathway might be identified by inspecting their kinship with other proteins in an interaction network, we present NetZcore, a novel algorithm to score pathway relevance of proteins involved in a particular pathology. |
| Long Abstract: Click Here |
| Poster X080 |
| Refinement of Calvin cycle models with allosteric regulations inferred from metabolomic data |
| Zoran Nikoloski- University of Potsdam |
| Sergio Grimbs (University of Potsdam, Institute of Biochemistry and Biology); Joachim Selbig (Max-Planck Institute of Molecular Plant Physiology, Bioinformatics); |
| Short Abstract: A first step towards practical solutions for increasing photosynthesis is developing a precise model of the Calvin cycle that includes different regulatory mechanisms. We propose an approach for determining putative allosteric regulations, based on a hierarchy of higher-order partial correlation matrices and apply it to Arabidopsis metabolomic high-throughput data. |
| Long Abstract: Click Here |
| Poster X081 |
| Functional characterisation of Mediator subcomplexes: Application of Nested Effects Models in a genetical genomics approach |
| Theresa Niederberger- Ludwig-Maximilians-University Munich |
| Dietmar Martin (Ludwig-Maximilians-University Munich, Gene Center); Michael Lidschreiber (Ludwig-Maximilians-University Munich, Gene Center); Achim Tresch (Ludwig-Maximilians-University Munich, Gene Center); |
| Short Abstract: The Mediator multiprotein complex is a transcriptional coregulator that acts via physical interactions with transcription factors. Several genome-wide gene expression data sets of yeast knockout strains are analysed with Nested Effects Models in order to predict these interactions. This combines classical epistasis analysis with network reconstruction approaches. |
| Long Abstract: Click Here |
| Poster X082 |
| Prioritizing functional modules mediating genetic perturbations and their phenotypic effects: a global strategy |
| Li Wang- University of Southern California |
| Ting Chen (University of Southern California, Molecular and Computational Biology); Fengzhu Sun (University of Southern California, Molecular and Computational Biology); |
| Short Abstract: We have developed a global strategy based on the Bayesian network framework to prioritize the functional modules mediating genetic perturbations and their phenotypic effects among a set of overlapping candidate modules. We take lethality in S. cerevisiae and human cancer as two examples to show effectiveness of this approach. |
| Long Abstract: Click Here |
| Poster X083 |
| Inferring Cancer-associated Signaling Networks Based on Significance Analysis of microRNA-mRNA Targeting |
| Xiaobo Zhou- The Methodist Hospital Research Institute |
| Di Huang (The Methodist Hospital Research Institute, Radiology); Stephen T. C. Wong (The Methodist Hospital Research Institute, Radiology); |
| Short Abstract: To infer signal networks, we develop a method in which mRNA expression data and microRNA information are integrated. By this mean, two major factors impacting gene expression, mRNA abundance and microRNA effect, are considered. To evaluate the effect of microRNA, we explore the expression data as well as sequence data. |
| Long Abstract: Click Here |
| Poster X084 |
| Integrative analysis of complex disease network underlying epistatic loci cross multiple tissues |
| Zhidong Tu- Rosetta/Merck |
| Chunsheng Zhang (Rosetta/Merck, Informatics); I-Ming Wang (Rosetta/Merck, Genetics); Hongyue Dai (Rosetta/Merck, Informatics); Pek Lum (Rosetta/Merck, Genetics); Mark Keller (UW-Madison, Genetics); Alan Attie (UW-Madison, Genetics); Jun Zhu (Rosetta/Merck, Genetics); Eric Schadt (Rosetta/Merck, Genetics); |
| Short Abstract: By constructing an F2 mouse cross and performing molecular profiling, we demonstrate how multiple genetic factors interact with each other to cause the underlying molecular network perturbation in a tissue specific manner. A network based prioritization approach identified candidate genes whose perturbations lead to changes in particular phenotypes as predicted. |
| Long Abstract: Click Here |
| Poster X085 |
| Dynamic modeling of FGF-2 signaling pathway in human embryonic stem cells |
| Geoffrey Koh- Bioprocessing Technology Institute, Agency for Science and Technology Research |
| Dong-Yup Lee (Bioprocessing Technology Institute, Agency for Science and Technology Research, Bioinformatics); Vanessa Ding (Bioprocessing Technology Institute, Agency for Science and Technology Research, Stem Cell); Andre Choo (Bioprocessing Technology Institute, Agency for Science and Technology Research, Stem Cell); |
| Short Abstract: We present a mathematical model of the FGF-2 signaling pathway in human embryonic stem cells. Our study focuses on the activity of the four members in the FGF receptor family. Through combinatorial knockdown simulations, we show that receptors FGFR1 and FGFR4 are crucial for activating the downstream ERK1/2 signaling pathway. |
| Long Abstract: Click Here |
| Poster X086 |
| Visualizing properties of protein interaction networks in functional space. |
| John Pinney- Imperial College London |
| William Kelly (Imperial College London, Centre for Bioinformatics); |
| Short Abstract: Using GLASS (Gene Layout by Semantic Similarity), a recently developed methodology for the visualization of genome-scale data, we demonstrate the striking differences between network data sets in terms of their biases towards different cellular components, and the knock-on effects of these biases for derived network statistics. |
| Long Abstract: Click Here |
| Poster X087 |
| Exploration of Arabidopsis regulome using data integration and network analysis approaches |
| Artem Lysenko- Rothamsted Research |
| Chris Rawlings (Rothamsted Research, Centre for Mathematical and Computational Biology); Charlie Hodgman (University of Nottingham, Multidisciplinary Centre for Integrative Biology); Tony Miller (Rothamsted Research, Plant Pathology and Microbiology ); |
| Short Abstract: We have constructed a network of predicted and experimentally proven protein-protein interactions, gene regulatory relationships and gene coexpression. This integrated network was analysed to identify modular structures and linked to the metabolic pathways and Gene Ontology. Centrality and betweenness analyses were then used to identify important regulatory modules. |
| Long Abstract: Click Here |
| Poster X088 |
| An automated approach for identification of parameter relations resulting in a switch-like behavior of pathway activation |
| Yvonne Koch- German Cancer Research Center |
| Hannah Schmidt-Glenewinkel (German Cancer Research Center, Theoretical Bioinformatics); Roland Eils (German Cancer Research Center, Theoretical Bioinformatics); Benedikt Brors (German Cancer Research Center, Theoretical Bioinformatics); |
| Short Abstract: An automated approach for scanning parameter space for models of differential equations combined with a decision tree algorithm was developed to find component relations responsible for pathway activation. Applied to EGF receptor internalization the method shows to be a useful tool in identifying parameter relations evoking a switch-like pathway response. |
| Long Abstract: Click Here |
| Poster X089 |
| A SREBP Centered Transcriptional Networks Controlling Lung Lipids And Surfactant Homeostasis |
| Yan Xu- Cincinnati Children's Hospital Medical Center |
| Minlu Zhang (University of Cincinnati, Computer Science); Yanhua Wang (CCHMC, Pulmonary Biology); Vrushank Dave (CCHMC, Pulmonary Biology); Jason Lu (CCHMC, Biomedical Informatics); Jeffrey Whitsett (CCHMC, Pulmonary Biology); |
| Short Abstract: We integrated microarray data with array independent data to enable the elucidation of genetic networks regulating the lipids related biological processes in lung. Using this strategy, we identified the critical components in lung transcriptional network directing lipogenesis, lipid sensing or trafficking, controlling surfactant homeostasis in the mouse lung. |
| Long Abstract: Click Here |
| Poster X090 |
| BIANA: A Cytoscape Plugin for Compiling Biological Interactions and for Network Analysis |
| Javier Garcia- Research Unit on Biomedical Informatics of IMIM/UPF |
| Emre Güney (UPF, Structural Bioinformatics Lab); JOAN PLANAS-IGLESIAS (UPF, Structural Bioinformatics Lab (GRIB)); BALDO OLIVA (UPF, Structural Bioinformatics Lab); RAMON ARAGUES (UPF, Structural Bioinformatics Lab); |
| Short Abstract: BIANA, a Python framework that can be used as a Cytoscape Plugin, is a tool for biological information integration and network management. It achieves the goal of integrating, managing and inferring latent biomolecular relationships from multiple sources of biological information. |
| Long Abstract: Click Here |
| Poster X091 |
| Predicting interacting motifs from protein-protein interaction networks |
| Jaume Bonet- GRIG-IMIM/UPF |
| Baldo Oliva (GRIB-IMIM/UPF, SBI); Ramon Aragues (GRIB-IMIM/UPF, SBI); Pascal Braun (DFCI-Harvard, CCSB); Marc Vidal (DFCI-Harvard, CCSB); |
| Short Abstract: Here we present a method to identify protein-protein interacting regions by using only sequence and topological information of the protein-protein interaction network (ppin). The method is based on the concept that proteins sharing common interactors might perform those same interactions through the same region. |
| Long Abstract: Click Here |
| Poster X092 |
| Novel Approach for Reconstructing the Metabolic Network |
| David Alarcon- GRIB |
| No additional authors |
| Short Abstract: We propose a novel method for reconstructing the metabolic network that avoids arbitrarily removing frequent chemical compounds (water, ATP) to prevent spureous relationships between enzymes. Our approach does not assume any a priori topoloy for the network. The method is focussed on accurately predicting enzyme relationships driven by these compounds. |
| Long Abstract: Click Here |
| Poster X093 |
| Dynamic modeling of E. coli central carbon metabolism combining different kinetic rate laws |
| Rafael Costa- University of Minho |
| Daniel Machado (University of Minho, Centre of Biological Engineering); Isabel Rocha (University of Minho, Centre of Biological Engineering); Eugenio Ferreira (University of Minho, Centre of Biological Engineering); |
| Short Abstract: In this work, we analyze four alternative hybrid modeling strategies to the reference large scale mechanistic E. coli central carbon metabolic network model based on mechanistic Michaelis-Menten equation for the bimolecular reactions and the other reactions with different formats of approximative rate kinetics (Generalized Mass-Action, convenience equation, Lin-Log and Power-Law). |
| Long Abstract: Click Here |
| Poster X095 |
| Multi-criterion approaches for the in silico optimization of mutant microorganisms |
| Isabel Rocha- University of Minho |
| Paulo Maia (University of Minho, IBB - Institute for Biotechnology and Bioengineering); Eugenio Ferreira (University of Minho, IBB - Institute for Biotechnology and Bioengineering); Miguel Rocha (University of Minho, CCTC); |
| Short Abstract: In Metabolic Engineering, Evolutionary Computation (EC) techniques to find optimal mutants (sets of gene knockouts) for the production of valuable compounds have been recently presented with good results. In this work, we view the problem as a multi-criterion optimization task, proposing adequate EC techniques, validated with some real world case-studies. |
| Long Abstract: Click Here |
| Poster X096 |
| The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles |
| Dirk Walther- MPI Molecular Plant Physiology |
| No additional authors |
| Short Abstract: We investigated the relationships between metabolic pathways and protein interaction networks. Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. |
| Long Abstract: Click Here |
| Poster X097 |
| Inferring cluster-based networks from differently stimulated multiple time-course gene expression data |
| Yuichi Shiraishi- RIKEN Advanced Science Institute |
| Shuhei Kimura (Tottori University, Department of Information and Knowledge Engineering); Mariko Hatakeyama (RIKEN Advanced Science Institute , Advanced Computational Sciences Department); |
| Short Abstract: In this paper,we propose a new statistical method based on state space models for estimating cluster-based gene networks.First, our model can treat several temporal profiles stimulated via different ligands.Second, our model performs clustering and estimation of relationships between clusters and outside stimuli via a unified criterion. |
| Long Abstract: Click Here |
| Poster X098 |
| Data integration for systems biology - The Ondex SABR Project |
| Chris Rawlings- The Ondex SABR Consortium |
| No additional authors |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster X099 |
| Investitating the Mechanisms of Transcriptional Timing in S. cerevisiae |
| Nathan Lewis- University of California, San Diego |
| Bernhard Palsson (University of California, San Diego, Bioengineering); Eran Segal (Weizmann Institute of Science, Department of Computer Science And Applied Mathematics); |
| Short Abstract: Various timing motifs for gene expression in linear metabolic pathways have been witnessed in yeast. Here various methods are applied to identify the mechanisms which contribute to transcriptional timing motifs in metabolic networks. |
| Long Abstract: Click Here |
| Poster X100 |
| Prediction of cardiac transcription networks based on molecular data and complex clinical phenotypes |
| Markus Schueler- Max Planck Institute for Molecular Genetics |
| Martje Tönjes (Max Planck Institute for Molecular Genetics, Vertebrate Genomics); Stefanie Hammer (Max Planck Institute for Molecular Genetics, Vertebrate Genomics); Utz J. Pape (Max Planck Institute for Molecular Genetics, Computational Molecular Biology); Felix Berger (Department of Pediatric Cardiology, German Heart Center); Martin Vingron (Max Planck Institute for Molecular Genetics, Computational Molecular Biology); Silke Sperling (Max Planck Institute for Molecular Genetics, Vertebrate Genomics); |
| Short Abstract: We analyzed transcription levels in biopsies derived from hearts of 190 patients. After defining informative meta-phenotypes from a detailed phenotype ontology we could identify specific disease associated transcription profiles by applying linear models. We predicted regulatory networks for genes showing highly correlated expression patterns using ChIP-optimized TFBS predictions. |
| Long Abstract: Click Here |
| Poster X101 |
| Leveraging the context-specific coordination of transcript and metabolite concentrations to discover gene-metabolite interactions. |
| Patrick Bradley- Princeton University |
| Matthew Brauer (Princeton University, Molecular Biology); Joshua Rabinowitz (Princeton University, Chemistry); Olga Troyanskaya (Princeton University, Computer Science); |
| Short Abstract: Understanding the mutual regulation between metabolites and gene products could benefit areas as diverse as medicine and bioengineering. We measure metabolite and transcript concentrations in yeast, and provide new quantitative evidence that these concentrations are coordinated. We then develop a Bayesian approach that effectively finds gene-metabolite functional relationships from this experimental data. |
| Long Abstract: Click Here |
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