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Prog Biophys Mol Biol. 2015 Jan;117(1):78-86. doi: 10.1016/j.pbiomolbio.2014.11.005. Epub 2014 Nov 26.

Unraveling liver complexity from molecular to organ level: challenges and perspectives.

Author information

1
Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.
2
Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany.
3
Obsidian Biomedical Consulting Ltd., Macclesfield, UK; The German Virtual Liver Network, University of Heidelberg, 69120 Heidelberg, Germany.
4
Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany; Institut National de Recherche en Informatique et en Automatique (INRIA), Domaine de Voluceau, 78150 Rocquencourt, France; University Pierre and Marie Curie and CNRS UMR 7598, LJLL, F-75005 Paris, France; CNRS, 7598 Paris, France.
5
Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany. Electronic address: u.klingmueller@dkfz.de.

Abstract

Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.

KEYWORDS:

Hepatocyte; Liver; Modeling; Multi-scale; Tissue

[Indexed for MEDLINE]

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