Multi-scale stochastic organization-oriented coarse-graining exemplified on the human mitotic checkpoint View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Richard Henze, Chunyan Mu, Mate Puljiz, Nishanthan Kamaleson, Jan Huwald, John Haslegrave, Pietro Speroni di Fenizio, David Parker, Christopher Good, Jonathan E. Rowe, Bashar Ibrahim, Peter Dittrich

ABSTRACT

The complexity of biological models makes methods for their analysis and understanding highly desirable. Here, we demonstrate the orchestration of various novel coarse-graining methods by applying them to the mitotic spindle assembly checkpoint. We begin with a detailed fine-grained spatial model in which individual molecules are simulated moving and reacting in a three-dimensional space. A sequence of manual and automatic coarse-grainings finally leads to the coarsest deterministic and stochastic models containing only four molecular species and four states for each kinetochore, respectively. We are able to relate each more coarse-grained level to a finer one, which allows us to relate model parameters between coarse-grainings and which provides a more precise meaning for the elements of the more abstract models. Furthermore, we discuss how organizational coarse-graining can be applied to spatial dynamics by showing spatial organizations during mitotic checkpoint inactivation. We demonstrate how these models lead to insights if the model has different "meaningful" behaviors that differ in the set of (molecular) species. We conclude that understanding, modeling and analyzing complex bio-molecular systems can greatly benefit from a set of coarse-graining methods that, ideally, can be automatically applied and that allow the different levels of abstraction to be related. More... »

PAGES

3902

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-40648-w

DOI

http://dx.doi.org/10.1038/s41598-019-40648-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112604573

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30846816


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40648-w'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40648-w'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40648-w'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40648-w'


 

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295 schema:name Chair of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University of Jena, Jena, Germany
296 Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
297 rdf:type schema:Organization
 




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