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Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Imprimir: 0278-940X
ISSN En Línea: 1943-619X

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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2016016559
pages 323-346

Multiscale Modeling of Cellular Epigenetic States: Stochasticity in Molecular Networks, Chromatin Folding in Cell Nuclei, and Tissue Pattern Formation of Cells

Jie Liang
Program in Bioinformatics, Department of Bioengineering, University of Illinois at Chicago, IL, 60612, USA
Youfang Cao
Theoretical Biology and Biophysics (T-6) and Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
Gamze Gursoy
Program in Bioinformatics, Department of Bioengineering, University of Illinois at Chicago, IL, 60612, USA
Hammad Naveed
Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637, USA
Anna Terebus
Program in Bioinformatics, Department of Bioengineering, University of Illinois at Chicago, IL, 60612, USA
Jieling Zhao
Program in Bioinformatics, Department of Bioengineering, University of Illinois at Chicago, IL, 60612, USA

SINOPSIS

Genome sequences provide the overall genetic blueprint of cells, but cells possessing the same genome can exhibit diverse phenotypes. There is a multitude of mechanisms controlling cellular epigenetic states and that dictate the behavior of cells. Among these, networks of interacting molecules, often under stochastic control, depending on the specific wirings of molecular components and the physiological conditions, can have a different landscape of cellular states. In addition, chromosome folding in three-dimensional space provides another important control mechanism for selective activation and repression of gene expression. Fully differentiated cells with different properties grow, divide, and interact through mechanical forces and communicate through signal transduction, resulting in the formation of complex tissue patterns. Developing quantitative models to study these multi-scale phenomena and to identify opportunities for improving human health requires development of theoretical models, algorithms, and computational tools. Here we review recent progress made in these important directions.


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