Constrained Allocation Flux Balance Analysis

Mori, Matteo; Hwa, Terence; Martin, Olivier; Martino, Andrea De; Marinari, Enzo

doi:10.1371/journal.pcbi.1004913

Cited by 153 publications

(245 citation statements)

References 77 publications

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“…Can one imagine hybrid FBA-self-replicator models that combine the strengths of both? Given that the model simplifications underlying the two approaches are quite different, this may not be easy to achieve, although some interesting variants of FBA, including additional flux constraints derived from the catalytic activity and molecular weight of proteins, should be mentioned here [34,106,107,109,110]. An alternative strategy would be to embed a detailed kinetic model of some module of interest within a coarse-grained model of the entire cell.…”

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confidence: 99%

Mathematical modelling of microbes: metabolism, gene expression and growth

Jong

Casagranda

Giordano

et al. 2017

J. R. Soc. Interface.

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The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.

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confidence: 99%

Mathematical modelling of microbes: metabolism, gene expression and growth

Jong

Casagranda

Giordano

et al. 2017

J. R. Soc. Interface.

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“…the Crabtree effect in yeast [16,17] or the Warburg effect in cancer cells [18][19][20]). Several phenomenological models have tackled the issue of how metabolism and gene expression coordinate to optimize growth in bacteria [2,[21][22][23], while mechanistic genomescale models can provide a more detailed picture of the crossovers in metabolic strategies that occur as the growth rate * Co-last authors is tuned [24][25][26]. Here we combine in silico genome-scale modeling with experimental data analysis to obtain a quantitative characterization of the trade-off between growth and its metabolic costs in E. coli.…”

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confidence: 99%

“…In turn, metabolic fluxes can be seen as the brokers of proteome re-shaping assuming they are proportional to enzyme levels [1, 3,26]. In particular, for carbon-limited growth (2) can be re-cast as (see Fig.1b, [3,26])where v C is the rate of carbon intake, v i is the flux of reaction i, the sum runs over enzyme-catalyzed reactions and φ max is a constant that includes all µ-independent terms (equal to about 0.48 or 48% in E. coli). The three terms on the left-hand side of (3) give explicit representations to the µ-dependent part of the proteome.…”

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confidence: 99%

“…The coefficients w i quantify the cost of each reaction i ∈ E in terms of the proteome fraction to be allocated to its enzyme per unit of net flux. For sakes of simplicity, we assume here that w i is the same for each i (but see [26] for a discussion of alternative choices).Note that (2) and (3) have the form Limiting growth rate by targeting a specific cellular activity L As w L increases, growth rate µ and the efficiency of the NL-proteome decrease, thus freeing resources to counteract the growth limitation states, so that, in principle, any re-shaping of the non-limiting sector will be tied to a change in the overall organization of metabolic activity. Therefore, nutrient stress (i.e.…”

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confidence: 99%

“…See [26] for details. To solve CAFBA, we set the costs w i of reactions in the E-sector to the same value, namely w E = 8.3 × 10 −4 g DW h/mmol, and used E. coli-specific values for w R and φ max as done in [26]. …”

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A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth

Mori

Marinari

Martino

2017

Preprint

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Many microbial systems are known to actively reshape their proteomes in response to changes in growth conditions induced e.g. by nutritional stress or antibiotics. Part of the re-allocation accounts for the fact that, as the growth rate is limited by targeting specific metabolic activities, cells simply respond by fine-tuning their proteome to invest more resources into the limiting activity (i.e. by synthesizing more proteins devoted to it). However, this is often accompanied by an overall re-organization of metabolism, aimed at improving the growth yield under limitation by re-wiring resource through different pathways. While both effects impact proteome composition, the latter underlies a more complex systemic response to stress. By focusing on E. coli's 'acetate switch', we use mathematical modeling and a re-analysis of empirical data to show that the transition from a predominantly fermentative to a predominantly respirative metabolism in carbon-limited growth results from the trade-off between maximizing the growth yield and minimizing its costs in terms of required the proteome share. In particular, E. coli's metabolic phenotypes appear to be Pareto-optimal for these objective functions over a broad range of dilutions.The physiology of cell growth can nowadays be experimentally probed in exponentially growing bacteria both at bulk (see e.g. the bacterial growth laws detailed in [1][2][3]) and at single cell resolution [4][5][6][7]. Refining the picture developed since the 1950s [8,9], recent studies have shown that changes in growth conditions are accompanied by a massive re-organization of the cellular proteome, whereby resources are re-distributed among protein classes (e.g. transporters, metabolic enzymes, ribosome-affiliated proteins, etc.) so as to achieve optimal growth performance [10]. This in turn underlies significant modifications in cellular energetics to cope with the increasing metabolic burden of fast growth [11][12][13].E. coli's 'acetate switch' is a major manifestation of the existence of a complex interplay between metabolism and gene expression. Slowly growing E. coli cells tend to operate close to the theoretical limit of maximum biomass yield [14,15]. Fast-growing cells, instead, typically show lower yields, together with the excretion of carbon equivalents such as acetate [13]. One can argue that, in the latter regime, cells optimize enzyme usage, i.e. they minimize the protein costs associated to growth, while at slow growth they try to use nutrients as efficiently as possible [13]. As one crosses over from one regime to the other, E. coli's growth physiology appears to be determined to a significant extent by the trade-off between growth and its biosynthetic costs. Interestingly, a similar overflow scenario appears in other cell types in proliferating regimes (see e.g. the Crabtree effect in yeast [16,17] or the Warburg effect in cancer cells [18][19][20]).Several phenomenological models have tackled the issue of how metabolism and gene expression coordinate to optimize growth in ...

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