http://precedings.nature.com/tags/systems%20biology Recently posted documents tagged with 'systems biology' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3932/version/1 Metabolic networks are among the most widely studied biological systems. The topology and interconnections of metabolic reactions have been well described for many species, but are not sufficient to understand how their activity is regulated in living organisms. The principles directing the dynamic organisation of reaction fluxes remain poorly understood. Cyclic structures are thought to play a central role in the homeostasis of biological systems and in their resilience to a changing environment. In this work, we investigate the role of fluxes of matter cycling in metabolic networks. First, we introduce a methodology for the computation of cyclic and acyclic fluxes in metabolic networks, adapted from an algorithm initially developed to study cyclic fluxes in trophic networks. Subsequently, we apply this methodology to the analysis of three metabolic systems, including the central metabolism of wild type and a deletion mutant of Escherichia coli, erythrocyte metabolism and the central metabolism of the bacterium Methylobacterium extorquens. The role of cycles in driving and maintaining the performance of metabolic functions upon perturbations is unveiled through these examples. This methodology may be used to further investigate the role of cycles in living organisms, their pro-activity and organisational invariance, leading to a better understanding of biological entailment and information processing. http://precedings.nature.com/documents/3932/version/1 Mon, 02 Nov 2009 17:25:38 UTC Organising metabolic networks: cycles in flux distributions hdl:10101/npre.2009.3932.1 2009-11-02 Jean-Marc Schwartz Nature Precedings 2009-11-02T17:25:38Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3724.1 Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialized notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Descriptions, the Entity Relationships and the Activity Flows. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage ranging from textbooks and teaching in high schools to peer reviewed articles in scientific journals. The first level of the SBGN Activity Flow language has been publicly released. Shared by the communities of biochemists, genomic scientists, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signaling pathways, metabolic networks and gene regulatory maps. http://dx.doi.org/10.1038/npre.2009.3724.1 Mon, 07 Sep 2009 19:43:48 UTC Systems Biology Graphical Notation: Activity Flow language Level 1 doi:10.1038/npre.2009.3724.1 2009-09-07 Huaiyu Mi Nature Precedings 2009-09-07T19:43:48Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3721.1 Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps. http://dx.doi.org/10.1038/npre.2009.3721.1 Mon, 07 Sep 2009 07:52:01 UTC Systems Biology Graphical Notation: Process Description language Level 1 doi:10.1038/npre.2009.3721.1 2009-09-07 Stuart L. Moodie Nature Precedings 2009-09-07T07:52:01Z Manuscript Biotechnology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3719.1 Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Descriptions, the Entity Relationships and the Activity Flows. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Entity Relationship language has been publicly released. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps. http://dx.doi.org/10.1038/npre.2009.3719.1 Fri, 04 Sep 2009 15:21:22 UTC Systems Biology Graphical Notation: Entity Relationship language Level 1 doi:10.1038/npre.2009.3719.1 2009-09-04 Nicolas Le Novère Nature Precedings 2009-09-04T15:21:22Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3526/version/1 The-Cell System is an advanced open-source simulation platform to model and analyze biochemical reaction networks. The present algorithm modules of the system assume that the reacting molecules are all homogeneously distributed in the reaction compartments, which is not the case in some cellular processes. The MinCDE system in Escherichia coli, for example, relies on intricately controlled reaction, diffusion and localization of Min proteins on the membrane and in the cytoplasm compartments to inhibit cell division at the poles of the rod-shaped cell. To model such processes, we have extended the E-Cell System to support reaction-diffusion and dynamic localization of molecules in volume and surface compartments. We evaluated our method by modeling the in vivo dynamics of MinD and MinE and comparing their simulated localization patterns to the observations in experiments and previous computational work. In both cases, our simulation results are in good agreement. http://precedings.nature.com/documents/3526/version/1 Fri, 31 Jul 2009 20:48:42 UTC Modeling reaction-diffusion of molecules on surface and in volume spaces with the E-Cell System hdl:10101/npre.2009.3526.1 2009-07-31 Satya N. V. Arjunan Nature Precedings 2009-07-31T20:48:42Z Manuscript Developmental Biology Microbiology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3444.1 The BioModels database contains biochemical network models in SBML format, in which the biochemical meaning of elements is specified by MIRIAM-compliant RDF annotations. We used these annotations to define a similarity measure for models, scoring the overlap of the biochemical systems described. Based on this score, we used two-way clustering to detect groups of similar models and groups of co-occuring model elements. To recognize and compare biochemical elements, we used routines from the software semanticSBML. A Python script extracts all MIRIAM annotations (regardless of their qualifiers) using the semanticSBML annotation classes. The result is a matrix in which the rows represent the models (e.g. BioModel 001), while the columns represent specific annotations (e.g. urn:miriam:reactome:REACT_15422). A matrix element is set to 1 if an identifier occurs in a model and to 0 otherwise. This matrix was used as an input for a hierarchical clustering algorithm (implemented in Matlab) and the clustered matrix was visualized. Model clustering allows to detect models describing similar biochemical processes (e.g. glycolysis) and their specific common elements. This may help to find candidate models for completing a given initial model, which could then be merged using semanticSBML. http://dx.doi.org/10.1038/npre.2009.3444.1 Mon, 20 Jul 2009 18:16:55 UTC A simple clustering of the BioModels database using semanticSBML doi:10.1038/npre.2009.3444.1 2009-07-20 Wolfram Liebermeister Nature Precedings 2009-07-20T18:16:55Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3380/version/1 Background: The metabolic transformation that changes Weddell seal pups born on land into aquatic animals is not only interesting for the study of general biology, but it also provides a model for the acquired and congenital muscle disorders which are associated with oxygen metabolism in skeletal muscle. However, the analysis of gene expression in seals is hampered by the lack of specific microarrays and the very limited annotation of known Weddell seal (Leptonychotes weddellii) genes.Results: Muscle samples from newborn, juvenile, and adult Weddell seals were collected during an Antarctic expedition. Extracted RNA was hybridized on Affymetrix Human Expression chips. Preliminary studies showed a detectable signal from at least 7000 probe sets present in all samples and replicates. Relative expression levels for these genes was used for further analysis of the biological pathways implicated in the metabolism transformation which occurs in the transition from newborn, to juvenile, to adult seals. Cytoskeletal remodeling, WNT signaling, FAK signaling, hypoxia-induced HIF1 activation, and insulin regulation were identified as being among the most important biological pathways involved in transformation. Conclusion: In spite of certain losses in specificity and sensitivity, the cross-species application of gene expression microarrays is capable of solving challenging puzzles in biology. A Systems Biology approach based on gene interaction patterns can compensate adequately for the lack of species-specific genomics information. http://precedings.nature.com/documents/3380/version/1 Mon, 29 Jun 2009 09:39:22 UTC Transformation of metabolism with age and lifestyle in Antarctic seals: a case study of systems biology approach to cross-species microarray experiment hdl:10101/npre.2009.3380.1 2009-06-29 Andrey Ptitsyn Nature Precedings 2009-06-29T09:39:22Z Manuscript Developmental Biology Ecology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3309/version/1 The number of genome-scale metabolic models has been rising quickly in recent years, and the scope of their utilization encompasses a broad range of applications from metabolic engineering to biological discovery. However the reconstruction of such models remains an arduous process requiring a high level of human intervention. Their utilization is further hampered by the absence of standardized data and annotation formats and the lack of recognized quality and validation standards.Plants provide a particularly rich range of perspectives for applications of metabolic modeling. We here report the first effort to the reconstruction of a genome-scale model of the metabolic network of the plant Arabidopsis thaliana, including over 2300 reactions and compounds. Our reconstruction was performed using a semi-automatic methodology based on the integration of two public genome-wide databases, significantly accelerating the process. Database entries were compared and integrated with each other, allowing us to resolve discrepancies and enhance the quality of the reconstruction. This process lead to the construction of three models based on different quality and validation standards, providing users with the possibility to choose the standard that is most appropriate for a given application. First, a core metabolic model containing only consistent data provides a high quality model that was shown to be stoichiometrically consistent. Second, an intermediate metabolic model attempts to fill gaps and provides better continuity. Third, a complete metabolic model contains the full set of known metabolic reactions and compounds in Arabidopsis thaliana.We provide an annotated SBML file of our core model to enable the maximum level of compatibility with existing tools and databases. We eventually discuss a series of principles to raise awareness of the need to develop coordinated efforts and common standards for the reconstruction of genome-scale metabolic models, with the aim of enabling their widespread diffusion, frequent update, maximum compatibility and convenience of use by the wider research community and industry. http://precedings.nature.com/documents/3309/version/1 Wed, 03 Jun 2009 18:47:22 UTC Reconstruction of an in silico metabolic model of Arabidopsis thaliana through database integration hdl:10101/npre.2009.3309.1 2009-06-03 Jean-Marc Schwartz Nature Precedings 2009-06-03T18:47:22Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3137.1 Computational models are becoming more and more the central scientific paradigm for understanding the complexity of living systems. With the increasing number and size of these models there is a growing need for model reuse and exchange. Furthermore, detailed models are not manageable without computer support. There are efforts to formalise the mathematical structure of models (e.g. SBML) and to standardise the kinetic and biological meaning of model components (e.g. SBO, GO, UniProt). However, formalising only the structure of computational models is not sufficient to easily exchange and reuse models and to achieve full computer support for modelling. We also need to formalise the pragmatical and dynamical aspects of models.For this purpose we propose two ontologies: The Kinetic Simulation Algorithm Ontology (KiSAO) and the TErminology for the Description of DYnamics (TEDDY). KiSAO covers algorithms used for simulation of computational models. The ontology classifies and puts into context existing simulation algorithms. For the classification, it uses several criteria such as deterministic/stochastic or spatial/nonspatial. The aim of TEDDY is to provide terms for describing and characterising dynamical behaviours, observable dynamical phenomena, and control elements of biological models and biological systems in Systems Biology and Synthetic Biology.We demonstrate how these new ontologies can extend the formalisation of models beyond structure, using the well-known repressilator model as an example. The simulation results depend pragmatically on the used algorithm: We compare the simulation results of the deterministic Livermore solver for ordinary differential equations (KiSAO:0000071) to the simulation results of the stochastic Gibson and Bruck’s next reaction method (KiSAO:0000027). The simulation results depend dynamically on the parameter setting: While parameter * (maximum number of produced proteins per promotor) is increased the modelled dynamical system undergoes a Supercritical Hopf Bifurcation (TEDDY_0000074). Below the critical value of * the system exhibits Damped Oscillation (TEDDY_0000063) converging to a Stable Spiral Point (TEDDY_0000126). Above the bifurcation the system possesses a Stable Limit Cycle (TEDDY_0000114), i.e. it shows Sustained Oscillation. The Negative Feedback (TEDDY_0000034) of the system is a necessary precondition for the ability of the system to oscillate.For details on KiSAO see the MIASE project page, for details on TEDDY see the project page. http://dx.doi.org/10.1038/npre.2009.3137.1 Wed, 22 Apr 2009 21:16:54 UTC Beyond Structure: KiSAO and TEDDY—Two Ontologies Addressing Pragmatical and Dynamical Aspects of Computational Models in Systems Biology doi:10.1038/npre.2009.3137.1 2009-04-22 Christian Knüpfer Nature Precedings 2009-04-22T21:16:54Z Poster Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3108.1 Background – The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and growth, as well as flagella development and other cellular processes. In the era of high-throughput technologies there is an imperative need to integrate large-scale data sets from high-throughput experimental techniques using computational methods and database resources to provide comprehensive information about the whole cellular system of a single organism.Results – In the framework of the German Systems Biology initiative GoFORSYS a pathway/genome database and web-portal for Chlamydomonas reinhardtii (ChlamyCyc) was established, which currently features about 270 metabolic pathways with related genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification.Conclusion – Chlamycyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas reinhardtii. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de. http://dx.doi.org/10.1038/npre.2009.3108.1 Tue, 21 Apr 2009 17:08:33 UTC ChlamyCyc – a comprehensive database and web-portal centered on Chlamydomonas reinhardtii doi:10.1038/npre.2009.3108.1 2009-04-21 Jan-Ole Christian Nature Precedings 2009-04-21T17:08:33Z Poster Genetics & Genomics Bioinformatics Plant Biology http://creativecommons.org/licenses/by/3.0/