Because the MetaFlux component of Pathway Tools generates constraint-based models directly from the EcoCyc PGDBs, as the database is refined through new curation, those refinements are automatically incorporated into newly generated versions of the model.
These generated constraints may be of a form that is easily extended for general case of the underlying problem, or they may be in a more complicated form where a generalisable pattern is difficult to identify.
The parity-check matrix is so named because it provides n−k parity check equations that generate constraints between data bits and parity bits.
The generated constraints seek to implement one of four power optimization approaches: slack minimization, clock tree paring, N-terminal net colocation, and area minimization.
The tabu search, which employs dynamically generated constraints to guide the search for optimum solutions, was found to be the most efficient among three tuned tested metaheuristics.
Using the part geometry and parametric punch shape models, we automatically generated constraints on tool parameters that eliminate the possibility of part-tool interference.
Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.
Since we use only pathways or sub-networks that are directly extracted from data without any combinatorial optimization, it provides an efficient method to generate constraints needed to improve the stability and reliability of detailed network reconstruction approaches.
