Identification of the Molecular Mechanisms for Cell-Fate Selection in Budding Yeast through Mathematical Modeling

Li, Yongkai; Yi, Ming; Zou, Xiufen

doi:10.1016/j.bpj.2013.03.057

Cited by 9 publications

(21 citation statements)

References 66 publications

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“…As reported previously, the Start point is accurately predicted by the nuclear Whi5 concentration independent of cell size, type and G1 duration2. Our recent research has also confirmed that Start can be characterized by entropy4, as entropy defines the height in the landscape of cell fate decision-making.…”

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confidence: 86%

The linear interplay of intrinsic and extrinsic noises ensures a high accuracy of cell fate selection in budding yeast

Zou

2014

Sci Rep

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To gain insights into the mechanisms of cell fate decision in a noisy environment, the effects of intrinsic and extrinsic noises on cell fate are explored at the single cell level. Specifically, we theoretically define the impulse of Cln1/2 as an indication of cell fates. The strong dependence between the impulse of Cln1/2 and cell fates is exhibited. Based on the simulation results, we illustrate that increasing intrinsic fluctuations causes the parallel shift of the separation ratio of Whi5P but that increasing extrinsic fluctuations leads to the mixture of different cell fates. Our quantitative study also suggests that the strengths of intrinsic and extrinsic noises around an approximate linear model can ensure a high accuracy of cell fate selection. Furthermore, this study demonstrates that the selection of cell fates is an entropy-decreasing process. In addition, we reveal that cell fates are significantly correlated with the range of entropy decreases.

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confidence: 86%

The linear interplay of intrinsic and extrinsic noises ensures a high accuracy of cell fate selection in budding yeast

Zou

2014

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“…It is known that the selection of cell fate in response to internal and external stimuli is essential to a cell's life (24). Since the occurrence of bistability defines the switch between two different cell fates (25), these rich dynamical behaviors contribute to the specification and maintenance of cell fate.…”

Section: Dynamical Analysis For the Degree Model Of The Mapk Cascade mentioning

confidence: 99%

Enhancement of Tunability of MAPK Cascade Due to Coexistence of Processive and Distributive Phosphorylation Mechanisms

Sun

Yang

et al. 2014

Biophysical Journal

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The processive phosphorylation mechanism becomes important when there is macromolecular crowding in the cytoplasm. Integrating the processive phosphorylation mechanism with the traditional distributive one, we propose a mixed dual-site phosphorylation (MDP) mechanism in a single-layer phosphorylation cycle. Further, we build a degree model by applying the MDP mechanism to a three-layer mitogen-activated protein kinase (MAPK) cascade. By bifurcation analysis, our study suggests that the crowded-environment-induced pseudoprocessive mechanism can qualitatively change the response of this biological network. By adjusting the degree of processivity in our model, we find that the MAPK cascade is able to switch between the ultrasensitivity, bistability, and oscillatory dynamical states. Sensitivity analysis shows that the theoretical results remain unchanged within a reasonably chosen variation of parameter perturbation. By scaling the reaction rates and also introducing new connections into the kinetic scheme, we further construct a proportion model of the MAPK cascade to validate our findings. Finally, it is illustrated that the spatial propagation of the activated MAPK signal can be improved (or attenuated) by increasing the degree of processivity of kinase (or phosphatase). Our research implies that the MDP mechanism makes the MAPK cascade become a flexible signal module, and the coexistence of processive and distributive phosphorylation mechanisms enhances the tunability of the MAPK cascade.

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“…Due to the lack of G1 cyclins, check point analysis in cell cycle is unable to be developed in this model. In [7], G1 cyclins are taken into account, and a new model for analyzing the cell fate decision mechanism at the Start transition point is constructed. This model explains how the crosstalk between cyclins and signal pathways determines the cell fate decision.…”

confidence: 99%

“…Motivated by some previous works [5][6][7]10], a full mathematical model is built by combining the schematic diagrams in Figure 1A and Figure 1B. Several assumptions, which are listed below, simplify the model.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…In the recent work of our group, the Start point is quantified mathematically by the largest derivative in the dose-response curve, which can be obtained from the relationship between the activation of Whi5 (i.e., the proportion of Whi5 that has been exported from the nucleus at 30 min) and the pheromone application time [7]. The pheromone application time at the Start point is considered the critical addition time, and the fraction of Whi5 at the Start point is termed the Whi5-threshold.The critical fractions of Whi5 for cycling cells obtained from the statistical analysis of biological data and the mathematical definition are both approximately equal to 0.5 [7,13]. Therefore, in this study, we still use our original definition [7] to characterize Start point.…”

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

Zou

2015

Quant. Biol.

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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.Keywords: cell fate decision; feedforward mechanism; mathematical modeling; hysteresis; reversibility INTRODUCTIONCells from across biological kingdoms are continuously engaged in the process of decision making. Through decision making, unicellular and multicellular organisms take information from their surroundings (including neighboring cells) and process these data through complex signal transduction and genetic circuits to further modulate cellular phenotypes. Therefore, the selection of cell fate in response to both internal and external stimuli is essential in a cell's life [1]. Much progress in understanding the circuitry underlying decision making, as well as the advantages of the underlying strategic logic implemented therein, has come from budding yeast, the simplest eukaryote [2].Mathematical modelling is an important method in ©...

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Identification of the Molecular Mechanisms for Cell-Fate Selection in Budding Yeast through Mathematical Modeling

Cited by 9 publications

References 66 publications

The linear interplay of intrinsic and extrinsic noises ensures a high accuracy of cell fate selection in budding yeast

The linear interplay of intrinsic and extrinsic noises ensures a high accuracy of cell fate selection in budding yeast

Enhancement of Tunability of MAPK Cascade Due to Coexistence of Processive and Distributive Phosphorylation Mechanisms

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

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