Data files

Nuclear Receptor PPARalpha Regulates Drug Metabolism in Human Liver

The data set was generated from four samples (mice). For each sample, three conditions were measured:
a) unsorted cells
b) sorted cells: low insulin binding type
c) sorted cells: high insuin binding type
Altogether, 4x3 =12 experimental data sets were investigated.

Gene expression profiling was performed using an AffymetrixGeneChip mouse gene 2.0 ST Array (Affymetrix Europe, Wooburn Green, UK).
The probe sequences can be downloaded from the Affymetrix website.
The following files contain probe
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reconstruction of BC network

Lecture notes from Winterschool in Juelich:

N.J. Bruce, Daria B. Kokh, M. Ozboyaci and Rebecca C. Wade.
Modelling of solvation effects for brownian dynamics simulation of biomolecular recognition.
In Computational Trends in Solvation and Transport in Liquids Lecture Notes, volume 28 of IAS Series, (2015) pages 259-280. Eds. Sutmann G., Grotendorst, J., Gompper,G., Marx,D. Schriften des Forschungszentrums Juelich.

https://juser.fz-juelich.de/record/188877/files/IAS-Band-28-V2.pdf

Tutorial workshop outline

Supplement 5 of publication: HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology

Primary hepatocytes were exposed to a stimulus by exchanging culture medium, thereby simulating changes in the blood composition. The expression of genes at different time points was recorded. Differentially expressed genes were clustered using fuzzy c-means algorithm into five groups. The arcs of the possible network were identified using the NetGenerator algorithm under the restriction of biological knowledge. The analysis was restricted to the main metabolic pathways of hepatocytes. The reverse
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Von der hepatischen Sternzelle zum 3D-Modell der Leberregeneration
In German, publication in Systembiologie 5, 2012

GRN2SBML

Copyright (c) 2012-2013 Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e.V. - Hans-Knoell-Institut (HKI).

This program is free software; you can redistribute it and/or modify it under the terms of the
GNU General Public License as published by the Free Software Foundation, Version 3.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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KEGGViewer is an open-source tool freely available at the BioJS Registry. Renders a KEGGML file and displays a KEGG pathway.

Contains all experimental data used for the study "Coordinating role of RXRα in downregulating hepatic detoxification during inflammation revealed by fuzzy logic modeling" by Keller and colleagues.

LigDig is a web application for investigating ligand-protein interactions. LigDig can be used to query structural and functional properties. LigDig can be accessed at http://mcm.h-its.org/ligdig

Transgenic mice with a conditional hepatocyte-specific deletion of the Smoothened gene after adolescence were characterized for alterations in gene expression via microarrays.

The project A3.4 will create a combined model that containes metabolism, gene regulation and signal transduction with main focus on the influence of the nuclear receptors PXR, CAR, and PPARa. To this end, information is gathered from HepatoNet1, TRANSFAC, and Ingenuity aiming at creating an overal model, which will be equipped with kinetic information subsequently. In project B.5 a model of IL6 signal transduction that links of STAT3 and MAPK (ERK) to ADME genes is created using TRANSFAC, Ingenuity,
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Workshop outline

Experimental data digitized from publication figure.

Twenty-four-hour endocrine and metabolic profiles following consumption of high-fructose corn syrup-, sucrose-, fructose-, and glucose-sweetened beverages with meals.
Stanhope KL, Griffen SC, Bair BR, Swarbrick MM, Keim NL, Havel PJ.
Am J Clin Nutr. 2008 May;87(5):1194-203.
PMID: 18469239
http://www.ncbi.nlm.nih.gov/pubmed/18469239

PsicquicGraph, a BioJS component to visualize molecular inteactions from PSICQUIC servers. PsicquicGraph is freely available at the BioJS Registry for download and enhancement. Instructions on how to use the tool are available here http://goo.gl/kDaIgZ and the source code can be found at http://github.com/biojs/biojs and DOI:10.5281/zenodo.7709

SDA7 can be used to carry out Brownian dynamics simulations of the diffusional association in a continuum aqueous solvent of two solute molecules, e.g. proteins, or of a solute molecule to an inorganic surface. SDA7 can also be used to simulate the diffusion of multiple proteins, in dilute or concentrated solutions, e.g., to study the effects of macromolecular crowding.

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques. Link to the project is given.

Enzyme protein chain lengths

Invited conference talk at Cold Spring Harbour Asia Meeting Shuzou 2012 Liver Metabolism, Diseases & Cancer

Morpheus XML model of patterning of HUVEC cells in Matrigel culture, based on retention of external VEGF to cell-modified matrix.

As used in reference:
A Köhn-Luque, W de Back, Y Yamaguchi, K Yoshimura, M A Herrero, T Miura (2013) Dynamics of VEGF matrix-retention in vascular network patterning, Physical Biology, 10(6) : 066007, doi: 10.1088/1478-3975/10/6/066007

Virtual Liver Retreat 2012 (Poster)
Quantitative Proteomics of liver

S. Babak Azimifar, Kirti Sharma, Mara Monetti, Nagarjuna Nagaraj and Matthias Mann*.
*Corresponding author; contact: S. Babak Azimifar (Max Planck Institute of Biochemistry, Martinsried, Germany; Azimifar@biochem.mpg.de).

Despite enormous progress in MS based proteomics, accurate quantification of in vivo proteomic and phosphoproteomic changes such as those in liver remains a challenge. We developed a new strategy which we termed
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webSDA: a web server to simulate macromolecular diffusional association.

The use of biological networks for interpreting data is becoming a standard in bioinformatics analysis. It enables us to look at a process from the perspective of pathways and network modules rather than single genes. Yet, there are still many obstacles when dealing with biological networks, such as protein-protein interaction or genetic networks. Firstly, these type of networks represent binary, rather than complex biological interactions; then, networks are still viewed as ‘static’ and do not
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