Feedforward Regulation Ensures Stability and Rapid Reversibility of a Cellular State

Doncic, Andreas; Skotheim, Jan M.

doi:10.1016/j.molcel.2013.04.014

Cited by 57 publications

(90 citation statements)

References 58 publications

(90 reference statements)

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“…The pheromone-arrested cells take much longer to reenter the cell cycle than cycling cells, which are not previously exposed to the high-concentration pulse. This finding shows the hysteresis behavior in pheromone-arrested cells as observed in the experiment [12]. …”

supporting

confidence: 81%

“…The hysteresis phenomenon disappears, suggesting that Cln1 and Cln2 are completely inhibited in pheromone-arrested cells [12]. The hysteresis experiment is also simulated for cells lacking the gene cln3.…”

Section: © Higher Education Press and Springer-verlag Berlin Heidelbementioning

confidence: 99%

“…The studies in both [7] and [10] are based on positive feedback regulation. Furthermore, a recent experiment about cellular transitions has shown that the stability of the pheromone-arrested state results from the coherent feedforward regulation of the cell cycle inhibitor Far1 [11,12].Due to complexity in highly interconnected biochemical networks, many related questions need to be further explored. For example, whether S cycling proteins at G1/ S transition may interfere with the molecular regulation that allows a single cell to choose between two different fates is still unknown.…”

mentioning

confidence: 99%

“…As before, if no special circumstances are noted, a pheromone-arrested cell is termed a pheromonearrested WT cell. The Whi5-threshold is approximately 0.6 for pheromone-arrested cells [12].To characterize the Reentry point, our original definition is modified by adding pheromone to pheromonearrested cells at 0~40 min and measuring the fraction of Whi5 removed from the nucleus at 50 min (the preexposed duration is not included). The details are presented below.…”

mentioning

confidence: 99%

“…Therefore, it is more difficult for pheromonearrested cells to reenter the cell cycle than for cycling cells [12].…”

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confidence: 99%

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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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supporting

confidence: 81%

Section: © Higher Education Press and Springer-verlag Berlin Heidelbementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Therefore, it is more difficult for pheromonearrested cells to reenter the cell cycle than for cycling cells [12].…”

mentioning

confidence: 99%

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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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Modeling population dynamics in a microbial consortium under control of a synthetic pheromone‐mediated communication system

Hoffmann

Haas

Hennig

et al. 2018

Engineering in Life Sciences

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Microbial consortia can be used to catalyze complex biotransformations. Tools to control the behavior of these consortia in a technical environment are currently lacking. In the present study, a synthetic biology approach was used to build a model consortium of two Saccharomyces cerevisiae strains where growth and expression of the fluorescent marker protein EGFP by the receiver strain is controlled by the concentration of α‐factor pheromone, which is produced by the emitter strain. We have developed a quantitative experimental and theoretical framework to describe population dynamics in the model consortium. We measured biomass growth and metabolite production in controlled bioreactor experiments, and used flow cytometry to monitor changes of the subpopulations and protein expression under different cultivation conditions. This dataset was used to parameterize a segregated mathematical model, which took into account fundamental growth processes, pheromone‐induced growth arrest and EGFP production, as well as pheromone desensitization after extended exposure. The model was able to predict the growth dynamics of single‐strain cultures and the consortium quantitatively and provides a basis for using this approach in actual biotransformations.

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SOX2-Dependent Regulation of Pluripotent Stem Cells

Wong

Chambers

Mullin

2016

Sox2

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Feedforward Regulation Ensures Stability and Rapid Reversibility of a Cellular State

Cited by 57 publications

References 58 publications

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

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

Modeling population dynamics in a microbial consortium under control of a synthetic pheromone‐mediated communication system

SOX2-Dependent Regulation of Pluripotent Stem Cells

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