title: Identification of the molecular mechanisms for cell-fate selection in budding yeast through mathematical modeling.
abstract: The specification and maintenance of cell fates is essential to the development of multicellular organisms. However, the precise molecular mechanisms in cell fate selection are, to our knowledge, poorly understood due to the complexity of multiple interconnected pathways. In this study, model-based quantitative analysis is used to explore how to maintain distinguished cell fates between cell-cycle commitment and mating arrest in budding yeast. We develop a full mathematical model of an interlinked regulatory network based on the available experimental data. By theoretically defining the Start transition point, the model is able to reproduce many experimental observations of the dynamical behaviors in wild-type cells as well as in Ste5-8A and Far1-S87A mutants. Furthermore, we demonstrate that a moderate ratio between Cln1/2→Far1 inhibition and Cln1/2→Ste5 inhibition is required to ensure a successful switch between different cell fates. We also show that the different ratios of the mutual Cln1/2 and Far1 inhibition determine the different cell fates. In addition, based on a new, definition of network entropy, we find that the Start point in wild-type cells coincides with the system's point of maximum entropy. This result indicates that Start is a transition point in the network entropy. Therefore, we theoretically explain the Start point from a network dynamics standpoint. Moreover, we analyze the biological bistablity of our model through bifurcation analysis. We find that the Cln1/2 and Cln3 production rates and the nonlinearity of SBF regulation on Cln1/2 production are potential determinants for irreversible entry into a new cell fate. Finally, the quantitative computations further reveal that high specificity and fidelity of the cell-cycle and mating pathways can guarantee specific cell-fate selection. These findings show that quantitative analysis and simulations with a mathematical model are useful tools for understanding the molecular mechanisms in cell-fate decisions.
authors.affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, P. R. China.
authors.forename: Yongkai
authors.initials: Y
authors.lastname: Li
authors.forename: Ming
authors.initials: M
authors.lastname: Yi
authors.forename: Xiufen
authors.initials: X
authors.lastname: Zou
mesh_terms: D048868:Adaptor Proteins, Signal Transducing / Q000378:metabolism; D002453:Cell Cycle; D050756:Cyclin-Dependent Kinase Inhibitor Proteins / Q000378:metabolism; D016213:Cyclins / Q000378:metabolism; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000166:cytology* / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D048868:Adaptor Proteins, Signal Transducing; C082719:CLN1 protein, S cerevisiae; D050756:Cyclin-Dependent Kinase Inhibitor Proteins; D016213:Cyclins; C081353:FAR1 protein, S cerevisiae; C080321:STE5 protein, S cerevisiae; D029701:Saccharomyces cerevisiae Proteins