School of Natural and Computing Sciences, University of Aberdeen, Computing Science Department, Aberdeen, AB24 3UE, UK
School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Aberdeen, AB25 3ZD, UK
This the model from the article: A quantitative model for mRNA translation in Saccharomyces cerevisiae. You T, Coghill GM, Brown AJ. Yeast. 2010 Mar 19. epub ahead of print, PMID: 20306461 , doi: 10.1002/yea.1770 Abstract: Messenger RNA (mRNA) translation is an essential step in eukaryotic gene expression that contributes to the regulation of this process. We describe a deterministic model based on ordinary differential equations that describe mRNA translation in Saccharomyces cerevisiae. This model, which was parameterized using published data, was developed to examine the kinetic behaviour of translation initiation factors in response to amino acid availability. The model predicts that the abundance of the eIF1-eIF3-eIF5 complex increases under amino acid starvation conditions, suggesting a possible auxiliary role for these factors in modulating translation initiation in addition to the known mechanisms involving eIF2. Our analyses of the robustness of the mRNA translation model suggest that individual cells within a randomly generated population are sensitive to external perturbations (such as changes in amino acid availability) through Gcn2 signalling. However, the model predicts that individual cells exhibit robustness against internal perturbations (such as changes in the abundance of translation initiation factors and kinetic parameters). Gcn2 appears to enhance this robustness within the system. These findings suggest a trade-off between the robustness and performance of this biological network. The model also predicts that individual cells exhibit considerable heterogeneity with respect to their absolute translation rates, due to random internal perturbations. Therefore, averaging the kinetic behaviour of cell populations probably obscures the dynamic robustness of individual cells. This highlights the importance of single-cell measurements for evaluating network properties. Copyright (c) 2010 John Wiley and Sons, Ltd. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.