Mathematical modeling reveals the mechanisms of feedforward regulation in cell fate decisions in budding yeast

Wenlong Li, Ming Yi, Xiufen Zou

PDF(1870 KB)
PDF(1870 KB)
Quant. Biol. ›› 2015, Vol. 3 ›› Issue (2) : 55-68. DOI: 10.1007/s40484-015-0043-0
RESEARCH ARTICLE
RESEARCH ARTICLE

Mathematical modeling reveals the mechanisms of feedforward regulation in cell fate decisions in budding yeast

Author information +
History +

Abstract

The determination of cell fate is one of the key questions of developmental biology. Recent experiments showed that feedforward regulation is a novel feature of regulatory networks that controls reversible cellular transitions. However, the underlying mechanism of feedforward regulation-mediated cell fate decision is still unclear. Therefore, using experimental data, we develop a full mathematical model of the molecular network responsible for cell fate selection in budding yeast. To validate our theoretical model, we first investigate the dynamical behaviors of key proteins at the Start transition point and the G1/S transition point; a crucial three-node motif consisting of cyclin (Cln1/2), Substrate/Subunit Inhibitor of cyclin-dependent protein kinase (Sic1) and cyclin B (Clb5/6) is considered at these points. The rapid switches of these important components between high and low levels at two transition check points are demonstrated reasonably by our model. Many experimental observations about cell fate decision and cell size control are also theoretically reproduced. Interestingly, the feedforward regulation provides a reliable separation between different cell fates. Next, our model reveals that the threshold for the amount of WHIskey (Whi5) removed from the nucleus is higher at the Reentry point in pheromone-arrested cells compared with that at the Start point in cycling cells. Furthermore, we analyze the hysteresis in the cell cycle kinetics in response to changes in pheromone concentration, showing that Cln3 is the primary driver of reentry and Cln1/2 is the secondary driver of reentry. In particular, we demonstrate that the inhibition of Cln1/2 due to the accumulation of Factor ARrest (Far1) directly reinforces arrest. Finally, theoretical work verifies that the three-node coherent feedforward motif created by cell FUSion (Fus3), Far1 and STErile (Ste12) ensures the rapid arrest and reversibility of a cellular state. The combination of our theoretical model and the previous experimental data contributes to the understanding of the molecular mechanisms of the cell fate decision at the G1 phase in budding yeast and will stimulate further biological experiments in future.

Graphical abstract

Keywords

cell fate decision / feedforward mechanism / mathematical modeling / hysteresis / reversibility

Cite this article

Download citation ▾
Wenlong Li, Ming Yi, Xiufen Zou. Mathematical modeling reveals the mechanisms of feedforward regulation in cell fate decisions in budding yeast. Quant. Biol., 2015, 3(2): 55‒68 https://doi.org/10.1007/s40484-015-0043-0

References

[1]
Furlong, E. E. (2010) The importance of being specified: cell fate decisions and their role in cell biology. Mol. Biol. Cell, 21, 3797–3798
CrossRef Pubmed Google scholar
[2]
Morgan, D. O. (2007) The cell cycle. London: New Science Press
[3]
Yang, L., MacLellan, W. R., Han, Z., Weiss, J. N. and Qu, Z. (2004) Multisite phosphorylation and network dynamics of cyclin-dependent kinase signaling in the eukaryotic cell cycle. Biophys. J., 86, 3432–3443
CrossRef Pubmed Google scholar
[4]
Bardwell, L., Zou, X., Nie, Q. and Komarova, N. L. (2007) Mathematical models of specificity in cell signaling. Biophys. J., 92, 3425–3441
CrossRef Pubmed Google scholar
[5]
Zou, X., Peng, T. and Pan, Z. (2008) Modeling specificity in the yeast MAPK signaling networks. J. Theor. Biol., 250, 139–155
CrossRef Pubmed Google scholar
[6]
Kofahl, B. and Klipp, E. (2004) Modelling the dynamics of the yeast pheromone pathway. Yeast, 21, 831–850
CrossRef Pubmed Google scholar
[7]
Li, Y., Yi, M. and Zou, X. (2013) Identification of the molecular mechanisms for cell-fate selection in budding yeast through mathematical modeling. Biophys. J., 104, 2282–2294
CrossRef Pubmed Google scholar
[8]
Chang, F. and Herskowitz, I. (1990) Identification of a gene necessary for cell cycle arrest by a negative growth factor of yeast: FAR1 is an inhibitor of a G1 cyclin, CLN2. Cell, 63, 999–1011
CrossRef Pubmed Google scholar
[9]
McKinney, J. D., Chang, F., Heintz, N. and Cross, F. R. (1993) Negative regulation of FAR1 at the Start of the yeast cell cycle. Genes Dev., 7, 833–843
CrossRef Pubmed Google scholar
[10]
Yang, X., Lau, K. Y., Sevim, V. and Tang, C. (2013) Design principles of the yeast G1/S switch. PLoS Biol., 11, e1001673
CrossRef Pubmed Google scholar
[11]
Mangan, S. and Alon, U. (2003) Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. USA, 100, 11980–11985
CrossRef Pubmed Google scholar
[12]
Doncic, A. and Skotheim, J. M. (2013) Feedforward regulation ensures stability and rapid reversibility of a cellular state. Mol. Cell, 50, 856–868
CrossRef Pubmed Google scholar
[13]
Doncic, A., Falleur-Fettig, M. and Skotheim, J. M. (2011) Distinct interactions select and maintain a specific cell fate. Mol. Cell, 43, 528–539
CrossRef Pubmed Google scholar
[14]
Novak, B., Tyson, J. J., Gyorffy, B. and Csikasz-Nagy, A. (2007) Irreversible cell-cycle transitions are due to systems-level feedback. Nat. Cell Biol., 9, 724–728
CrossRef Pubmed Google scholar
[15]
Skotheim, J. M., Di Talia, S., Siggia, E. D. and Cross, F. R. (2008) Positive feedback of G1 cyclins ensures coherent cell cycle entry. Nature, 454, 291–296
CrossRef Pubmed Google scholar
[16]
Charvin, G., Oikonomou, C., Siggia, E. D. and Cross, F. R. (2010) Origin of irreversibility of cell cycle start in budding yeast. PLoS Biol., 8, e1000284
CrossRef Pubmed Google scholar
[17]
Hartwell, L. H., Culotti, J., Pringle, J. R. and Reid, B. J. (1974) Genetic control of the cell division cycle in yeast. Science, 183, 46–51
CrossRef Pubmed Google scholar
[18]
Cross, F. R. (1995) Starting the cell cycle: what’s the point? Curr. Opin. Cell Biol., 7, 790–797
CrossRef Pubmed Google scholar
[19]
Talia, S. D., Skotheim, J. M., Bean, J. M., Siggia, E. D. and Cross, F. R. (2007) The effects of molecular noise and size control on variability in the budding yeast cell cycle. Nature, 448, 947–951
CrossRef Pubmed Google scholar
[20]
Di Talia, S., Wang, H., Skotheim, J. M., Rosebrock, A. P., Futcher, B. and Cross, F. R. (2009) Daughter-specific transcription factors regulate cell size control in budding yeast. PLoS Biol., 7, e1000221
CrossRef Pubmed Google scholar
[21]
Turner, J. J., Ewald, J. C. and Skotheim, J. M. (2012) Cell size control in yeast. Curr. Biol., 22, R350–R359
CrossRef Pubmed Google scholar
[22]
Hao, N., Nayak, S., Behar, M., Shanks, R. H., Nagiec, M. J., Errede, B., Hasty, J., Elston, T. C. and Dohlman, H. G. (2008) Regulation of cell signaling dynamics by the protein kinase-scaffold Ste5. Mol. Cell, 30, 649–656
CrossRef Pubmed Google scholar
[23]
Tyers, M. and Futcher, B. (1993) Far1 and Fus3 link the mating pheromone signal transduction pathway to three G1-phase Cdc28 kinase complexes. Mol. Cell. Biol., 13, 5659–5669
Pubmed
[24]
Takahashi, S. and Pryciak, P. M. (2008) Membrane localization of scaffold proteins promotes graded signaling in the yeast MAP kinase cascade. Curr. Biol., 18, 1184–1191
CrossRef Pubmed Google scholar
[25]
Hartwell, L. H. and Unger, M. W. (1977) Unequal division in Saccharomyces cerevisiae and its implications for the control of cell division. J. Cell Biol., 75, 422–435
CrossRef Pubmed Google scholar
[26]
Qu, Z., MacLellan, W. R. and Weiss, J. N. (2003) Dynamics of the cell cycle: checkpoints, sizers, and timers. Biophys. J., 85, 3600–3611
CrossRef Pubmed Google scholar
[27]
Colman-Lerner, A., Gordon, A., Serra, E., Chin, T., Orna, R., Endy, D., Pesce, C. G. and Brent, R. (2005) Regulated cell-to-cell variation in a cell-fate decision system. Nature, 437, 699–706
CrossRef Pubmed Google scholar
[28]
Paliwal, S., Iglesias, P. A., Campbell, K., Hilioti, Z., Groisman, A. and Levchenko, A. (2007) MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast. Nature, 446, 46–51
CrossRef Pubmed Google scholar
[29]
Yu, R. C., Pesce, C. G., Colman-Lerner, A., Lok, L., Pincus, D., Serra, E., Holl, M., Benjamin, K., Gordon, A. and Brent, R. (2008) Negative feedback that improves information transmission in yeast signalling. Nature, 456, 755–761
CrossRef Pubmed Google scholar
[30]
Ubersax, J. A. and Ferrell, J. E. (2006) A noisy ‘Start’ to the cell cycle. Mol. Syst. Biol., 2, 0014
CrossRef Pubmed Google scholar
[31]
Kar, S., Baumann, W. T., Paul, M. R. and Tyson, J. J. (2009) Exploring the roles of noise in the eukaryotic cell cycle. Proc. Natl. Acad. Sci. USA, 106, 6471–6476
CrossRef Pubmed Google scholar
[32]
Li Y. K., Yi M., Zou X. F. (2014) The linear interplay of intrinsic and extrinsic noises ensures a high accuracy of cell fate selection in budding yeast. Sci. Rep, 4, 5764
[33]
Zou, X., Xiang, X., Chen, Y., Peng, T., Luo, X. and Pan, Z. (2010) Understanding inhibition of viral proteins on type I IFN signaling pathways with modeling and optimization. J. Theor. Biol., 265, 691–703
CrossRef Pubmed Google scholar
[34]
Sun, J., Yi, M., Yang, L., Wei, W., Ding, Y. and Jia, Y. (2014) Enhancement of tunability of MAPK cascade due to coexistence of processive and distributive phosphorylation mechanisms. Biophys. J., 106, 1215–1226
CrossRef Pubmed Google scholar

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(1870 KB)

Accesses

Citations

Detail

Sections
Recommended

/