Photons, photosynthesis, and high-performance computing: challenges, progress, and promise of modeling metabolism in green algae

Chang, Cheng-Hsien; Gräf, Peter; Alber, David M.; Kim, K; Murray, Glenn A.; Posewitz, Matthew C.; Seibert, Michael

doi:10.1088/1742-6596/125/1/012048

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The starch degradation model was expressed in Systems Biology Markup Language (SBML) [ 45 ] and input into our metabolic simulation software framework, the High-Performance Systems Biology Toolkit (HiPer SBTK) [ 52 ]. The time evolution of metabolite concentrations and fluxes was simulated, and the possible space of rate and binding parameters sampled.…”

Section: Methodsmentioning

confidence: 99%

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation

et al. 2011

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BackgroundHigher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.ResultsSimulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing β-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter kcat and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase kcat and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.ConclusionsWe find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

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Section: Methodsmentioning

confidence: 99%

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation

et al. 2011

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“…The research has delivered some very promising speedup results (47 and 15 times over 32 nodes using different architectures) that could lead to implementation on a grid system but is limited to this class of model. Chang, et al (2008) have developed the metabolic simulation code HiPer SBTK. Initial performance studies using a master-slave architecture have been performed for a 64-parameter sensitivity minimization on our prototype model of C. reinhardtii.…”

Section: Related Workmentioning

confidence: 99%

Investigating the speedup of systems biology simulation using the SZTAKI Desktop Grid

Ghorbani¹,

Mustafee²,

Kiss³

et al. 2014

Proceedings of the Winter Simulation Conference 2014

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Systems biology studies the complex interactions of biological and biochemical systems rather than their individual molecular components. System biology simulations can be embarrassingly parallel jobs that have no dependency among individual simulation instances, and thus lend themselves to parallel execution over distributed resources to reduce their overall execution time. One example of such distributed resources is a Desktop Grid for Volunteer Computing that aims to use vast numbers of computers to support scientific applications. The SZTAKI Desktop Grid (SZDG) uses a modified form of the volunteer computing software BOINC to implement an institution-wide Desktop Grid. This paper reports on experiences of porting the SIMAP systems biology ODE simulator to SZDG. A case study using a simulation of the mammalian ErbB signaling pathway reports on significant speedup.

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Simulation, Characterization, and Optimization of Metabolic Models with the High Performance Systems Biology Toolkit

Lunacek¹,

Nag²,

Alber³

et al. 2011

SIAM J. Sci. Comput.

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Photons, photosynthesis, and high-performance computing: challenges, progress, and promise of modeling metabolism in green algae

Cited by 3 publications

References 23 publications

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation

Investigating the speedup of systems biology simulation using the SZTAKI Desktop Grid

Simulation, Characterization, and Optimization of Metabolic Models with the High Performance Systems Biology Toolkit

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