Deciphering Cell’s Robustness by a Multi-scale Framework Integrating Cell Cycle and Metabolism in Budding Yeast
Cell cycle and metabolism are coupled networks. Cell growth and division require synthesis of macromolecules which is dependent on metabolic cues. Conversely, metabolites involved in storage carbon-, lipid- and nucleotide metabolism have been observed to fluctuate periodically as a function of cell cycle progression. In budding yeast, detailed connections among cell cycle and metabolism have been recently elucidated. However, high-throughput and manually curated studies spanning 15 years point at many more physical interactions between these two networks. Computational models of cell cycle and metabolism are being developed for some time. Notably, kinetic and Boolean models are used to simulate the former, while Flux Balance Analysis (FBA) can be used to study genome-wide models of metabolism. However, to date no effort has been made to integrate, and to investigate the mutual regulation of, these two systems in any organism. A multi-scale framework is presented that integrates a Boolean cell cycle model with a constraint-based model of metabolism, incorporating mechanistic and high-throughput interactions. Directionality and effect are incorporated for the mechanistic interactions. Conversely, as this information is unknown for the high-throughput interactions, an evolutionary optimization algorithm has been developed to generate models that incorporate it iteratively, to explore directionality and effect. Model results are verified against metabolic pathway activity and enzyme concentrations. Through Boolean logic, activity of cell cycle nodes can activate or inhibit metabolic reactions. Conversely, presence or absence of a metabolic flux can promote or prevent the activity of cell cycle nodes, respectively. Seven known interactions in which cell cycle components regulate metabolic enzymes were used to integrate the two models. 23% of the flux changes showed similar dynamics to the concentrations of 163 tracked enzymes. Seven out of 22 KEGG pathway activity changes were correctly predicted. Implementing the in silico framework such that (i) it utilizes trehalose through Nth1 trehalase under the control of the Clb5,6/Cdk1 enzymatic activity (which drives DNA replication dynamics) and (ii) it allows lipolysis of triacyl glycerol (TAG) under the control of the Cln1,2/Cdk1 enzymatic activity, increased the alignment of flux changes with enzyme concentration changes to 46%. This results confirms the importance of storage metabolites for metabolic changes during the growth phase of the cell cycle. The multi-scale model predicted the production of dTMP to be constrained to the G1/S transition, and that glucans were not produced during early M phase. By using the evolutionary algorithm to explore directionality and effect of interactions, the alignment with the data does not significantly increase. Remarkably, a large group of interaction networks are able to reproduce the metabolic pathway activity changes and enzyme concentration dynamics. These networks show temporal dTMP production and increased flux throughout all metabolism during S and early M phase. The first computer model that integrates cell cycle to metabolic networks reveals marked changes in flux distributions through different cell cycle phases. Further integration of high-throughput interaction networks may lead to quantitative predictions about the interface between cell cycle and metabolism, to be verified experimentally. Our integrative, multi-scale framework may be employed to capture the mechanistic basis of robustness of cell cycle networks by highlighting metabolic causes of cell cycle arrest.