Search Results

You are looking at 1 - 10 of 375 items :

  • "biological networks" x
Clear All

://dx.doi.org/10.1093/nar/gkl950 [7] Ferraro N., Palopoli L., Panni S., Rombo S.E., “Master-Slave” Biological Network Alignment, In: Borodovsky M., Gogarten J.P., Przytycka T.M., Rajasekaran S. (Eds.), Proceedings of the 6th International symposium on Bioinformatics Research and Applications (ISBRA 2010) (23–26 May, 2010, Storrs, Connecticut, USA), Springer, 2010, 215–229 [8] Fionda V., Palopoli L., Biological Network Querying Techniques: Analysis and Comparison, J COMPUT BIOL, 2011, 18, 595–625 http://dx.doi.org/10.1089/cmb.2009.0144 [9] Fionda V., Palopoli L., Panni S., Rombo

Z. Phys. Chem. 216 (2002) 235–247  by Oldenbourg Wissenschaftsverlag, München Towards a Calculus of Biological Networks By H. S. Mortveit∗ and C. M. Reidys Los Alamos National Laboratory, D-2, 87545 New Mexico, USA Dedicated to Prof. Dr. Peter Schuster on the occasion of his 60th birthday (Received July 2, 2001; accepted July 20, 2001) Sequential Dynamical Systems / Graph Morphisms / Phase Space Embedding In this paper we present a new framework for studying the dynamics of biological networks. A specific class of dynamical systems, Sequential Dynamical Systems

Reconstruction of biological networks based on life science data integration Benjamin Kormeier1,*, Klaus Hippe1, Patrizio Arrigo2, Thoralf Töpel1, Sebastian Janowski1 and Ralf Hofestädt1 1Bielefeld University, Bioinformatics Department PO Box 100131, D-33501 Bielefeld, Germany 2CNR ISMAC, Section of Genoa, Via De Marini 6, 16149 Genova, Italy Summary For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological

2.5D Visualisation of Overlapping Biological Networks David CY Fung1, Seok-Hee Hong1, Dirk Koschützki2, Falk Schreiber3,4 and Kai Xu5 1The University of Sydney, Sydney (Australia) 2Furtwangen University of Applied Sciences, Furtwangen (Germany) 3IPK Gatersleben, Gatersleben (Germany) 4Martin-Luther University Halle-Wittenberg (Germany) 5ICT Centre, CSIRO, Hobart (Australia) Abstract Biological data is often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. In this paper, we investigate a new prob- lem

Journal of Integrative Bioinformatics 2007 http://journal.imbio.de/ Community-based Linking of Biological Network Resources: Databases, Formats and Tools Michael Telgkamp, Dirk Koschützki, Henning Schwöbbermeyer and Falk Schreiber Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr. 3, D-06466 Gatersleben, Germany {telgkamp,koschuet,schwoebb,schreibe}@ipk-gatersleben.de Summary The analysis of biological networks is increasingly important in the life sciences and in particular in systems biology. Computer-based analysis tools are

An integrative approach to modeling biological networks Vesna Memišević1, Tijana Milenković1, and Nataša Pržulj2,∗ 1Department of Computer Science, University of California, Irvine, CA 92697-3435, USA 2Department of Computing, Imperial College London, London, SW7 2AZ, UK ∗Corresponding author (e-mail: natasha@imperial.ac.uk) Summary Networks are used to model real-world phenomena in various domains, including systems biology. Since proteins carry out biological processes by interacting with other proteins, it is expected that cellular functions are reflected

Monika Piwowar and Wiktor Jurkowski 1 Selected aspects of biological network analysis 1.1 Introduction Muchhas beenmade of theHumanGenomeProject’s potential to unlock the secrets of life [1, 2].Mapping the entire humanDNAwas expected to provide answers to unsolved problems of heredity, evolution, protein structure and function, disease mechanisms and many others. The actual outcome of the project, however, differed from expecta- tions. It turned out that coding fragments – genes – constitute only a minute fraction (approximately 2%) of humanDNA. Furthermore

Volume 9, Issue 1 2010 Article 9 Statistical Applications in Genetics and Molecular Biology An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data Andrea Rau, Purdue University, INRA AgroParisTech Florence Jaffrézic, INRA AgroParisTech Jean-Louis Foulley, INRA AgroParisTech Rebecca W. Doerge, Purdue University Recommended Citation: Rau, Andrea; Jaffrézic, Florence; Foulley, Jean-Louis; and Doerge, Rebecca W. (2010) "An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data," Statistical

Visualization and Analysis of a Cardio Vascular Disease- and MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches Björn Sommer1*, Evgeny S. Tiys2, Benjamin Kormeier1, Klaus Hippe1, Sebastian J. Janowski1, Timofey V. Ivanisenko2, Anatoly O. Bragin2, Patrizio Arrigo3, Pavel S. Demenkov4, Alexey V. Kochetov2, Vladimir A. Ivanisenko2, Nikolay A. Kolchanov2, Ralf Hofestädt1 1Bio-/Medical Informatics Department, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany 2 Institute of Cytology and Genetics