Document Detail


Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.
MedLine Citation:
PMID:  22777818     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.
Authors:
Taráz E Buck; Jieyue Li; Gustavo K Rohde; Robert F Murphy
Related Documents :
10077868 - Martian stable isotopes: volatile evolution, climate change and exobiological implicati...
24125378 - Beating the rayleigh limit: orbital-angular-momentum-based super-resolution diffraction...
3053048 - Bacterial alternative nitrogen fixation systems.
22333978 - Depth discontinuity-based cup segmentation from multi-view colour retinal images.
3079308 - A new subtraction method for obtaining myocardial perfusion images with oxygen-15 water...
25137718 - High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing.
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Review     Date:  2012-07-10
Journal Detail:
Title:  BioEssays : news and reviews in molecular, cellular and developmental biology     Volume:  34     ISSN:  1521-1878     ISO Abbreviation:  Bioessays     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-08-14     Completed Date:  2012-12-28     Revised Date:  2013-09-03    
Medline Journal Info:
Nlm Unique ID:  8510851     Medline TA:  Bioessays     Country:  United States    
Other Details:
Languages:  eng     Pagination:  791-9     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 WILEY Periodicals, Inc.
Affiliation:
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Automatic Data Processing / methods*
Cell Physiological Processes
Cell Shape
Cell Size
Cellular Structures / metabolism,  physiology*
Computational Biology / methods*
Computer Simulation
HeLa Cells
Humans
Image Processing, Computer-Assisted / methods*
Microscopy / methods*
Models, Biological*
Molecular Conformation
Organelle Shape
Organelle Size
Grant Support
ID/Acronym/Agency:
GM075205/GM/NIGMS NIH HHS; GM088816/GM/NIGMS NIH HHS; GM090033/GM/NIGMS NIH HHS; R01 GM075205/GM/NIGMS NIH HHS; R01 GM090033/GM/NIGMS NIH HHS; R21 GM088816/GM/NIGMS NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Targeting of CD22-positive B-cell lymphoma cells by synthetic divalent sialic acid analogues.
Next Document:  Admittance tympanometry with 2-kHz probe tones in patients with low-frequency hearing loss.