Imperial College London

Krishnan

Faculty of EngineeringDepartment of Chemical Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6633j.krishnan

 
 
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Location

 

C503Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Seaton:2016:10.1371/journal.pcbi.1004604,
author = {Seaton, D and Krishnan, J},
doi = {10.1371/journal.pcbi.1004604},
journal = {PLOS Computational Biology},
title = {Model-based analysis of cell cycle responses to dynamically varying environments},
url = {http://dx.doi.org/10.1371/journal.pcbi.1004604},
volume = {12},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Cell cycle progression is carefully coordinated with a cell’s intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments.
AU - Seaton,D
AU - Krishnan,J
DO - 10.1371/journal.pcbi.1004604
PY - 2016///
SN - 1553-734X
TI - Model-based analysis of cell cycle responses to dynamically varying environments
T2 - PLOS Computational Biology
UR - http://dx.doi.org/10.1371/journal.pcbi.1004604
UR - http://hdl.handle.net/10044/1/32766
VL - 12
ER -