Ines Thiele

TL;DR: This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.

TL;DR: The subsystem approach is described, the first release of the growing library of populated subsystems is offered, and the SEED is the first annotation environment that supports this model of annotation.

TL;DR: The constraint-based reconstruction and analysis toolbox as discussed by the authors is a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraintbased approach and allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.

TL;DR: This protocol provides a helpful manual for all stages of the reconstruction process and presents a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction.

TL;DR: The reconstruction process is described and it is demonstrated how the resulting genome-scale (or global) network can be used for the discovery of missing information, for the formulation of an in silico model, and as a structured context for analyzing high-throughput biological data sets.