FAME, the Flux Analysis and Modeling Environment

<p>Abstract</p> <p>Background</p> <p>The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists.</p> <p>Results</p> <p>The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at <url>http://f-a-m-e.org/</url>.</p> <p>Conclusions</p> <p>With FAME, we present the community with an open source, user-friendly, web-based "one stop shop" for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike.</p

A Data Integration and Visualization Resource for the Metabolic Network of Synechocystis sp. PCC 6803

Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight besides statistical information. In this article, we present a visualization tool for the metabolic network of Synechocystis PCC6803, an important model cyanobacterium for sustainable biofuel production. We illustrate how this metabolic map can be used to integrate experimental and computational data for Synechocystis systems biology and metabolic engineering studies. Additionally, we discuss how this map, and the software infrastructure that we supply with it, can be used in the development of other organism-specific metabolic network visualizations. Besides a Python console package VoNDA (http://vonda.sf.net), we provide a working demonstration of the interactive metabolic map and the associated Synechocystis genome-scale stoichiometric model, as well as various ready-to-visualize microarray data sets, at http://f-a-m-e.org/synechocystis/

Cerebellar plasticity and associative memories are controlled by perineuronal nets.

Perineuronal nets (PNNs) are assemblies of extracellular matrix molecules, which surround the cell body and dendrites of many types of neuron and regulate neural plasticity. PNNs are prominently expressed around neurons of the deep cerebellar nuclei (DCN), but their role in adult cerebellar plasticity and behavior is far from clear. Here we show that PNNs in the mouse DCN are diminished during eyeblink conditioning (EBC), a form of associative motor learning that depends on DCN plasticity. When memories are fully acquired, PNNs are restored. Enzymatic digestion of PNNs in the DCN improves EBC learning, but intact PNNs are necessary for memory retention. At the structural level, PNN removal induces significant synaptic rearrangements in vivo, resulting in increased inhibition of DCN baseline activity in awake behaving mice. Together, these results demonstrate that PNNs are critical players in the regulation of cerebellar circuitry and function

De Hanzehogeschool Groningen is beVLOGen over open science

Wat is open science, waarom is het zo belangrijk en hoe wordt het in de praktijk gebracht door hogescholen? In deze video laat de Hanzehogeschool Groningen zien hoe zij open science mogelijk maken

De Hanzehogeschool Groningen is beVLOGen over open science

Wat is open science, waarom is het zo belangrijk en hoe wordt het in de praktijk gebracht door hogescholen? In deze video laat de Hanzehogeschool Groningen zien hoe zij open science mogelijk maken