Computational models are expected to increase understanding on how complex biological functions arise fromthe interactions of large numbers of gene products and metabolic molecules. Recent studies underline the need todevelop quantitative models of the whole cell in order to tackle this challenge and to accelerate biologicaldiscoveries [1]. Here we present a way to integrate two distinct models: one dealing with Metabolism andGrowth (MGM), the other dealing with Growth and Cycle (GCM). The MGM is a modified version of apreviously published model [2] that quantitatively connects glucose availability, type of metabolism (i.e.,fermentative vs respiratory), macromolecular composition - i.e. the relative ribosome level - and cell growth atsteady state. The GCM is an ODE model describing the growth dynamics for ribosome and protein accumulationand the major, growth-triggered cell cycle events. The two sub-models are linked together in a unified, lowgranularity fashion where the MGM acts as a parameter generator for some of the GCM parameters.It is shownhow such a simple (though coherent and accurate) model can faithfully predict the protein distribution of cellsexponentially growing with different concentrations of glucose as a sole carbon source: the idea is that differentenvironments stimulate different metabolisms, thus providing different growth conditions that, in turns, providedifferent cycle timings. The MGCM simulations show a good agreement with the experimental data involvingpopulations of S. cerevisiae growing in 6 batch cultures with different glucose concentrations. [1] Karr, J.R., Sanghvi, J.C., et al. (2012), Cell150, 389-401; [2] Molenaar, D., van Berlo, R., et al. (2009)Molecular Systems Biology, 5:323.
An integrated metabolism, growth and cycle model for Saccharomyces cerevisiae
Palumbo Pasquale;Papa Federico;
2015
Abstract
Computational models are expected to increase understanding on how complex biological functions arise fromthe interactions of large numbers of gene products and metabolic molecules. Recent studies underline the need todevelop quantitative models of the whole cell in order to tackle this challenge and to accelerate biologicaldiscoveries [1]. Here we present a way to integrate two distinct models: one dealing with Metabolism andGrowth (MGM), the other dealing with Growth and Cycle (GCM). The MGM is a modified version of apreviously published model [2] that quantitatively connects glucose availability, type of metabolism (i.e.,fermentative vs respiratory), macromolecular composition - i.e. the relative ribosome level - and cell growth atsteady state. The GCM is an ODE model describing the growth dynamics for ribosome and protein accumulationand the major, growth-triggered cell cycle events. The two sub-models are linked together in a unified, lowgranularity fashion where the MGM acts as a parameter generator for some of the GCM parameters.It is shownhow such a simple (though coherent and accurate) model can faithfully predict the protein distribution of cellsexponentially growing with different concentrations of glucose as a sole carbon source: the idea is that differentenvironments stimulate different metabolisms, thus providing different growth conditions that, in turns, providedifferent cycle timings. The MGCM simulations show a good agreement with the experimental data involvingpopulations of S. cerevisiae growing in 6 batch cultures with different glucose concentrations. [1] Karr, J.R., Sanghvi, J.C., et al. (2012), Cell150, 389-401; [2] Molenaar, D., van Berlo, R., et al. (2009)Molecular Systems Biology, 5:323.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
