An ensemble of mathematical models showing diauxic growth behaviour

BackgroundCarbon catabolite repression (CCR) controls the order in which different carbon sources are metabolised. Although this system is one of the paradigms of regulation in bacteria, the underlying mechanisms remain controversial. CCR involves the coordination of different subsystems of the cell - responsible for the uptake of carbon sources, their breakdown for the production of energy and precursors, and the conversion of the latter to biomass. The complexity of this integrated system, with regulatory mechanisms cutting across metabolism, gene expression, and signalling, has motivated important modelling efforts over the past four decades, especially in the enterobacterium Escherichia coli.ResultsStarting from a simple core model with only four intracellular metabolites, we develop an ensemble of model variants, all showing diauxic growth behaviour during a batch process. The model variants fall into one of the four categories: flux balance models, kinetic models with growth dilution, kinetic models with regulation, and resource allocation models. The model variants differ from one another in only a single aspect, each breaking the symmetry between the two substrate assimilation pathways in a different manner, and can be quantitatively compared using a so-called diauxic growth index. For each of the model variants, we predict the behaviour in two new experimental conditions, namely a glucose pulse for a culture growing in minimal medium with lactose and a batch culture with different initial concentrations of the components of the transport systems. When qualitatively comparing these predictions with experimental data for these two conditions, a number of models can be excluded while other model variants are still not discriminable. The best-performing model variants are based on inducer inclusion and activation of enzymatic genes by a global transcription factor, but the other proposed factors may complement these well-known regulatory mechanisms.ConclusionsThe model ensemble presented here offers a better understanding of the variety of mechanisms that have been proposed to play a role in CCR. In addition, it provides an educational resource for systems biology, as it gives an introduction to a broad range of modeling approaches in the context of a simple but biologically relevant example.

[1]  Radhakrishnan Mahadevan,et al.  Economics of membrane occupancy and respiro-fermentation , 2011, Molecular systems biology.

[2]  U. Sauer,et al.  Coordination of microbial metabolism , 2014, Nature Reviews Microbiology.

[3]  Philippe Nghe,et al.  Single-Cell Dynamics Reveals Sustained Growth during Diauxic Shifts , 2013, PloS one.

[4]  Edda Klipp,et al.  Prediction of temporal gene expression. Metabolic opimization by re-distribution of enzyme activities. , 2002, European journal of biochemistry.

[5]  Wolfram Liebermeister,et al.  The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization , 2016, PLoS Comput. Biol..

[6]  M. A. de Menezes,et al.  Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity , 2007, Proceedings of the National Academy of Sciences.

[7]  G. T. Tsao,et al.  Cybernetic modeling of microbial growth on multiple substrates , 1984, Biotechnology and bioengineering.

[8]  Hidde de Jong,et al.  The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes , 2010, PLoS Comput. Biol..

[9]  Doraiswami Ramkrishna,et al.  Dynamic models of metabolism: Review of the cybernetic approach , 2012 .

[10]  Eva Balsa-Canto,et al.  DOTcvpSB, a software toolbox for dynamic optimization in systems biology , 2009, BMC Bioinformatics.

[11]  Judith B. Zaugg,et al.  Bacterial adaptation through distributed sensing of metabolic fluxes , 2010, Molecular systems biology.

[12]  B. Palsson Systems Biology: Constraint-based Reconstruction and Analysis , 2015 .

[13]  D. Chu,et al.  Limited by sensing - A minimal stochastic model of the lag-phase during diauxic growth. , 2017, Journal of theoretical biology.

[14]  J. Stelling,et al.  Ensemble modeling for analysis of cell signaling dynamics , 2007, Nature Biotechnology.

[15]  R. Heinrich,et al.  The Regulation of Cellular Systems , 1996, Springer US.

[16]  F. Bolivar,et al.  Current knowledge of the Escherichia coli phosphoenolpyruvate–carbohydrate phosphotransferase system: peculiarities of regulation and impact on growth and product formation , 2012, Applied Microbiology and Biotechnology.

[17]  C. Eberlein,et al.  Benzoate Mediates Repression of C4-Dicarboxylate Utilization in “Aromatoleum aromaticum” EbN1 , 2011, Journal of bacteriology.

[18]  Nan Xiao,et al.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli , 2008, Bioinform..

[19]  Ophelia S. Venturelli,et al.  Population Diversification in a Yeast Metabolic Program Promotes Anticipation of Environmental Shifts , 2014, bioRxiv.

[20]  T. Hwa,et al.  A growth-rate composition formula for the growth of E.coli on co-utilized carbon substrates. , 2015, Molecular systems biology.

[21]  Michael Springer,et al.  Natural Variation in Preparation for Nutrient Depletion Reveals a Cost–Benefit Tradeoff , 2014, bioRxiv.

[22]  Glenn Ledder,et al.  Scaling for Dynamical Systems in Biology , 2017, Bulletin of mathematical biology.

[23]  C. Francke,et al.  How Phosphotransferase System-Related Protein Phosphorylation Regulates Carbohydrate Metabolism in Bacteria , 2006, Microbiology and Molecular Biology Reviews.

[24]  J. Geiselmann,et al.  Understanding carbon catabolite repression in Escherichia coli using quantitative models. , 2015, Trends in microbiology.

[25]  C. Ghim,et al.  Rewiring carbon catabolite repression for microbial cell factory. , 2012, BMB reports.

[26]  F. Doyle,et al.  Dynamic flux balance analysis of diauxic growth in Escherichia coli. , 2002, Biophysical journal.

[27]  R Ramakrishna,et al.  Cybernetic modeling of growth in mixed, substitutable substrate environments: Preferential and simultaneous utilization. , 1996, Biotechnology and bioengineering.

[28]  B. Palsson,et al.  Transcriptional regulation in constraints-based metabolic models of Escherichia coli Covert , 2002 .

[29]  Eva Balsa-Canto,et al.  Global dynamic optimization approach to predict activation in metabolic pathways , 2014, BMC Systems Biology.

[30]  Atul Narang,et al.  Bacterial gene regulation in diauxic and non-diauxic growth. , 2006, Journal of theoretical biology.

[31]  Chase L. Beisel,et al.  Bacterial sugar utilization gives rise to distinct single‐cell behaviours , 2014, Molecular microbiology.

[32]  Andreas Kremling,et al.  A Quantitative Approach to Catabolite Repression in Escherichia coli* , 2006, Journal of Biological Chemistry.

[33]  Dennis Eichmann,et al.  Metabolic Engineering Principles And Methodologies , 2016 .

[34]  Reinhard Guthke,et al.  Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs , 2011, Molecular Systems Biology.

[35]  James C Liao,et al.  Ensemble Modeling for Robustness Analysis in engineering non-native metabolic pathways. , 2014, Metabolic engineering.