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

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.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 ©...

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop

“…The multi-channel process in the coherent feed-forward loop is advantageous to the propagation of signals, which means that the expression noise level of downstream gene can be reduced due to feed forward loop. Our finding may present a clue to understand why the fate decision system in budding yeast would evolve into a coherent feed forward structure (Li et al, 2015). …”

“…It has been recognized that feedback loops play significant roles in a variety of biological processes, such as calcium signaling (Berridge, 2001; Lewis, 2001), p53 regulation (Harris and Levine, 2005), galactose regulation (Acar et al, 2005), cell cycle (Morgan, 2006; Yang et al, 2013; Liu et al, 2015), and cell fate decision in budding yeast (Li et al, 2014, 2015). Some studies suggested that negative feedbacks typically attenuated noise and positive feedbacks tended to amplify noise (Becskei and Serrano, 2000; Austin et al, 2006; Alon, 2007); however, there were other studies revealing that positive feedbacks could attenuate noises and there were no strong correlations between the sign of feedbacks (negative or positive) and the noise attenuation properties (Hooshangi and Weiss, 2006; Hornung and Barkai, 2008).…”

“…The current study has ever studied the mechanisms of feed-forward regulation in cell fate decisions in budding yeast (Li et al, 2015). As is shown in (Mangan and Alon, 2003), the authors have found that the coherent feed-forward loop rejects transient input pulses and responds only to persistent stimuli.…”

Noise decomposition algorithm and propagation mechanism in feed-forward gene transcriptional regulatory loop

“…[22,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] The regulation mechanisms of feed-forward loops in cell fate decisions in budding yeast were studied. [20] It is found that the coherent feed-forward loop responds only to persistent stimuli and rejects transient input pulses as shown in Ref. [28].…”

“…It is recognized that feedback loops and noise play important roles in a wide variety of biological processes, such as galactose regulation, [14] calcium signalling, [15,16] p53 regulation, [17] cell cycle, [18,19] and cell fate decision in budding yeast. [20,21] Some studies indicated that positive feedbacks tended to magnify noise and negative feedbacks typically attenuated noise. [22][23][24] However, most of the positive feedback loops are bistable where stationary noise in a real system is rarely observed, and only transition rate can be measured.…”