References of "Gudmundsson, Steinn"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailToward systems metabolic engineering in cyanobacteria: opportunities and bottlenecks.
Nogales, Juan; Gudmundsson, Steinn; Thiele, Ines UL

in Bioengineered (2013), 4(3), 158-163

We recently assessed the metabolism of Synechocystis sp PCC6803 through a constraints-based reconstruction and analysis approach and identified its main metabolic properties. These include reduced ... [more ▼]

We recently assessed the metabolism of Synechocystis sp PCC6803 through a constraints-based reconstruction and analysis approach and identified its main metabolic properties. These include reduced metabolic robustness, in contrast to a high photosynthetic robustness driving the optimal autotrophic metabolism. Here, we address how these metabolic features affect biotechnological capabilities of this bacterium. The search for growth-coupled overproducer strains revealed that the carbon flux re-routing, but not the electron flux, is significantly more challenging under autotrophic conditions than under mixo- or heterotrophic conditions. We also found that the blocking of the light-driven metabolism was required for carbon flux re-routing under mixotrophic conditions. Overall, our analysis, which represents the first systematic evaluation of the biotechnological capabilities of a photosynthetic organism, paradoxically suggests that the light-driven metabolism itself and its unique metabolic features are the main bottlenecks in harnessing the biotechnological potential of Synechocystis. [less ▲]

Detailed reference viewed: 73 (2 UL)
Full Text
Peer Reviewed
See detailDetailing the optimality of photosynthesis in cyanobacteria through systems biology analysis.
Nogales, Juan; Gudmundsson, Steinn; Knight, Eric M. et al

in Proceedings of the National Academy of Sciences of the United States of America (2012), 109(7), 2678-2683

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will ... [more ▼]

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms. [less ▲]

Detailed reference viewed: 116 (5 UL)
Full Text
Peer Reviewed
See detailComputationally efficient flux variability analysis
Gudmundsson, Steinn; Thiele, Ines UL

in BMC Bioinformatics (2010), 11

BACKGROUND: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time ... [more ▼]

BACKGROUND: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods. RESULTS: We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed. CONCLUSIONS: Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology. [less ▲]

Detailed reference viewed: 105 (4 UL)