Constraint-based reconstruction and analysis of metabolic and regulatory networks (Part 2) – Lars Nielsen

Implicit in the collection of high ‐ throughput data is an assumption that computational models ultimately will facilitate biological discovery, reconciliation of heterogeneous data types, identify inconsistencies and enable the systematic generation of hypotheses. Current naïve statistical models fall well short of this ambition, while the use of the conventional dynamic models of physics suffers from our inability to accurately determine in vivo parameters. Constraint ‐ based models use stoichiometry, thermodynamics, physical capacity constraints and regulation to define feasible solution spaces, which can then be explored using a range tools to infer network behavior. The framework has proven surprisingly powerful in its ability to predict the behavior of natural as well as engineered networks.

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