Limits...
DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

Baroukh C, Muñoz-Tamayo R, Steyer JP, Bernard O - PLoS ONE (2014)

Bottom Line: Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed.DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles.It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

View Article: PubMed Central - PubMed

Affiliation: INRA UR050, Laboratoire des Biotechnologies de l'Environnement, Narbonne, France; INRIA-BIOCORE, Sophia-Antipolis, France.

ABSTRACT
Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

Show MeSH

Related in: MedlinePlus

Comparison of simulation results with experimental data.Simulation results were obtained by simulation of system (7) and are represented by dashed or dotted lines. Experimental results were taken from [16] and are represented by dots, diamonds or squares. A. Evolution of total biomass in terms of carbon content. Dashed line: model; Circles: experimental data; Grey line: light intensity. B. Evolution of total biomass in terms of nitrogen content. Dashed line: model; Diamonds: experimental data; Grey line: light intensity. C. Evolution of chlorophyll (computed as a fixed percentage of functional biomass). Dashed line: model; Circles: experimental data; Grey line: light intensity. D. Evolution of “energy and carbon” metabolites. Dashed line and Circles: carbohydrates (CARB); Dotted line and Diamonds: lipids (PA); Grey line: light intensity. Accumulation of carbon and energy metabolites during the day and their consumption during the night for growth and maintenance purpose is well represented. E. Evolution of functional biomass B. Dashed line: model; Squares: experimental data; Grey line: light intensity. F. Evolution of “buffer” metabolites at branching points, as predicted by the model. Dashed line: glyceraldehyde 3-phosphate (GAP); Dotted line: glucose 6-phosphate (G6P); Small-dashed line: phosphoenolpyruvate (PEP); Black line: GAP + PEP + G6P; Grey line: light intensity. Note that their carbon mass quota is relatively small (less than 4%).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4126706&req=5

pone-0104499-g001: Comparison of simulation results with experimental data.Simulation results were obtained by simulation of system (7) and are represented by dashed or dotted lines. Experimental results were taken from [16] and are represented by dots, diamonds or squares. A. Evolution of total biomass in terms of carbon content. Dashed line: model; Circles: experimental data; Grey line: light intensity. B. Evolution of total biomass in terms of nitrogen content. Dashed line: model; Diamonds: experimental data; Grey line: light intensity. C. Evolution of chlorophyll (computed as a fixed percentage of functional biomass). Dashed line: model; Circles: experimental data; Grey line: light intensity. D. Evolution of “energy and carbon” metabolites. Dashed line and Circles: carbohydrates (CARB); Dotted line and Diamonds: lipids (PA); Grey line: light intensity. Accumulation of carbon and energy metabolites during the day and their consumption during the night for growth and maintenance purpose is well represented. E. Evolution of functional biomass B. Dashed line: model; Squares: experimental data; Grey line: light intensity. F. Evolution of “buffer” metabolites at branching points, as predicted by the model. Dashed line: glyceraldehyde 3-phosphate (GAP); Dotted line: glucose 6-phosphate (G6P); Small-dashed line: phosphoenolpyruvate (PEP); Black line: GAP + PEP + G6P; Grey line: light intensity. Note that their carbon mass quota is relatively small (less than 4%).

Mentions: Microalgae store energy and carbon during the day so as to support growth and maintenance during the night, because of their autotrophic metabolism and the synchronization of their circadian cycle on the daily light [15]. Therefore, intermediate metabolites such as carbohydrates and lipids accumulate during the day and are remobilized during the night (Figure 1D) [16]. This behavior cannot be described under the balanced-growth assumption. One way to circumvent this issue is to represent these metabolites as product of the cell during the day and substrate during the night. Therefore applying one of the above-cited QSSA metabolic modeling frameworks could a priori be possible to represent carbon storage and better understand microalgae metabolism submitted to day/night cycles. In literature, only Knoop et al. [17], using the DFBA framework, computed metabolic fluxes for a full day/night cycle. However, determining an optimization function to represent carbon storage during the day and its consumption during the night is not a trivial task. Indeed, the classical optimization function “maximization of biomass production” does not work: when applying it, all the carbon available will go to biomass synthesis, and none to carbon storage. To circumvent this issue, the solution is to either force fluxes to carbon storage or to force the fluxes of biomass synthesis and maintenance () and other futile cycles. In their work, Knoop et al. [17], forced fluxes to carbon storage by changing the biomass composition at each time step. Their method indeed predicted metabolic fluxes dynamically but did not allow predicting the fluxes toward carbon storage and hence the dynamic change of biomass composition. In a context of better understanding and predicting microalgae metabolism for biofuels production, prediction of carbon storage fluxes is essential if one seeks the conditions in which microalgae accumulates more lipids or starch to improve biofuels production yield. Hence, to model such bioprocesses, a metabolic modeling framework that handles non balanced-growth and dynamics behaviors is necessary.


DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

Baroukh C, Muñoz-Tamayo R, Steyer JP, Bernard O - PLoS ONE (2014)

Comparison of simulation results with experimental data.Simulation results were obtained by simulation of system (7) and are represented by dashed or dotted lines. Experimental results were taken from [16] and are represented by dots, diamonds or squares. A. Evolution of total biomass in terms of carbon content. Dashed line: model; Circles: experimental data; Grey line: light intensity. B. Evolution of total biomass in terms of nitrogen content. Dashed line: model; Diamonds: experimental data; Grey line: light intensity. C. Evolution of chlorophyll (computed as a fixed percentage of functional biomass). Dashed line: model; Circles: experimental data; Grey line: light intensity. D. Evolution of “energy and carbon” metabolites. Dashed line and Circles: carbohydrates (CARB); Dotted line and Diamonds: lipids (PA); Grey line: light intensity. Accumulation of carbon and energy metabolites during the day and their consumption during the night for growth and maintenance purpose is well represented. E. Evolution of functional biomass B. Dashed line: model; Squares: experimental data; Grey line: light intensity. F. Evolution of “buffer” metabolites at branching points, as predicted by the model. Dashed line: glyceraldehyde 3-phosphate (GAP); Dotted line: glucose 6-phosphate (G6P); Small-dashed line: phosphoenolpyruvate (PEP); Black line: GAP + PEP + G6P; Grey line: light intensity. Note that their carbon mass quota is relatively small (less than 4%).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126706&req=5

pone-0104499-g001: Comparison of simulation results with experimental data.Simulation results were obtained by simulation of system (7) and are represented by dashed or dotted lines. Experimental results were taken from [16] and are represented by dots, diamonds or squares. A. Evolution of total biomass in terms of carbon content. Dashed line: model; Circles: experimental data; Grey line: light intensity. B. Evolution of total biomass in terms of nitrogen content. Dashed line: model; Diamonds: experimental data; Grey line: light intensity. C. Evolution of chlorophyll (computed as a fixed percentage of functional biomass). Dashed line: model; Circles: experimental data; Grey line: light intensity. D. Evolution of “energy and carbon” metabolites. Dashed line and Circles: carbohydrates (CARB); Dotted line and Diamonds: lipids (PA); Grey line: light intensity. Accumulation of carbon and energy metabolites during the day and their consumption during the night for growth and maintenance purpose is well represented. E. Evolution of functional biomass B. Dashed line: model; Squares: experimental data; Grey line: light intensity. F. Evolution of “buffer” metabolites at branching points, as predicted by the model. Dashed line: glyceraldehyde 3-phosphate (GAP); Dotted line: glucose 6-phosphate (G6P); Small-dashed line: phosphoenolpyruvate (PEP); Black line: GAP + PEP + G6P; Grey line: light intensity. Note that their carbon mass quota is relatively small (less than 4%).
Mentions: Microalgae store energy and carbon during the day so as to support growth and maintenance during the night, because of their autotrophic metabolism and the synchronization of their circadian cycle on the daily light [15]. Therefore, intermediate metabolites such as carbohydrates and lipids accumulate during the day and are remobilized during the night (Figure 1D) [16]. This behavior cannot be described under the balanced-growth assumption. One way to circumvent this issue is to represent these metabolites as product of the cell during the day and substrate during the night. Therefore applying one of the above-cited QSSA metabolic modeling frameworks could a priori be possible to represent carbon storage and better understand microalgae metabolism submitted to day/night cycles. In literature, only Knoop et al. [17], using the DFBA framework, computed metabolic fluxes for a full day/night cycle. However, determining an optimization function to represent carbon storage during the day and its consumption during the night is not a trivial task. Indeed, the classical optimization function “maximization of biomass production” does not work: when applying it, all the carbon available will go to biomass synthesis, and none to carbon storage. To circumvent this issue, the solution is to either force fluxes to carbon storage or to force the fluxes of biomass synthesis and maintenance () and other futile cycles. In their work, Knoop et al. [17], forced fluxes to carbon storage by changing the biomass composition at each time step. Their method indeed predicted metabolic fluxes dynamically but did not allow predicting the fluxes toward carbon storage and hence the dynamic change of biomass composition. In a context of better understanding and predicting microalgae metabolism for biofuels production, prediction of carbon storage fluxes is essential if one seeks the conditions in which microalgae accumulates more lipids or starch to improve biofuels production yield. Hence, to model such bioprocesses, a metabolic modeling framework that handles non balanced-growth and dynamics behaviors is necessary.

Bottom Line: Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed.DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles.It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

View Article: PubMed Central - PubMed

Affiliation: INRA UR050, Laboratoire des Biotechnologies de l'Environnement, Narbonne, France; INRIA-BIOCORE, Sophia-Antipolis, France.

ABSTRACT
Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

Show MeSH
Related in: MedlinePlus