Revision as of 03:07, 8 February 2013

Framework description

The new version of OptFlux's Strain Optimization plug-in includes all the previous single objective (SO) optimization methods, seamlessly integrated with the new multi-objective (MO) architecture.

In this version of the Optimization plug-in, the following features are available:

• Phenotype methods:
  • FBA, pFBA, ROOM, MOMA, Linear MOMA, MiMBL
• Optimization methods:
  • EA, SA, SPEA2
  • Single and multi-objective
• Types of Knockout:
  • Reactions, Genes
• Over/under expression optimization

Overview of the developed framework. The upper part shows the major steps of the three algorithms. The evaluation gray box illustrates the processes of solution decoding, from Gene Knockout Sets (GKS) to Reaction Knockout Sets (RKS) (upper- left), phenotype simulation showing the added constraints (bottom) and fitness evaluation for both MO and SO cases (upper-right).

The Archive Manager

In this version of OptFlux, some changes were applied to the EA and SA algorithms, regarding the information that is kept during each run, although not changing their functioning nor the best final solution. The major change was the implementation of an archive that keeps the best solutions found by the EA and SA during the run. This is not used by the algorithms for selection or for creating new solutions, but allows having a richer final result. The archive runs in a parallel thread, and smartly manages the solutions that are kept, removing duplicate solutions and non-better super solutions of already existing ones.

Solution Simplification

The end step of an optimization procedure is the solution simplification. These heuristics, although intelligent, may generate solutions that include unnecessary knockouts/regulations. To prevent this, only the optimal genetic modifications are kept.

Methods

The optimization problem can be formulated as:

where N corresponds to the set of reactions and M to the set of metabolites in the model.

The layer (a) addresses the objective functions at the bioengineering level to be addressed by the MO algorithm, while the layer (b) depicts the constraints of the inner phenotype simulation (cellular level). In this bi-level formulation the strain optimization and the phenotype simulation methods can be chosen independently from all available options (e.g. SPEA2, EA or SA in a) and FBA, MOMA or ROOM in b)). The output of phenotype simulation is the set of flux values for all reactions in the model. These are used by the optimization algorithms to compute the fitness value of the solution, using an appropriate objective function.