The past two decades have witnessed great advances in
the computational modeling and systems biology fields.
Soon after the first models of metabolism were developed,
several methods for the prediction of phenotypes were also
put forward. With the ever-growing information provided by
such methods, new questions arose. Metabolic Engineering
in particular posed some interesting questions. Recently,
Schuetz and co-workers proposed that the metabolism of
bacteria operates close to the Pareto-optimal surface of a
three-dimensional space definedned by competing
objectives and demonstrated the validity of their claims for
various environmental perturbations.
However, phenotype prediction methods have all been
developed to operate based on the assumption of a given
single-objective, as an example Flux Balance Analysis
(FBA) often assumes that the organisms are evolutionarily
optimized towards optimal growth. On the other hand, Minimization
of Metabolic Adjustment (MOMA) proposes that
after a perturbation, the goal of the organisms shifts from
optimal growth to the minimization of the global metabolic
adjustment relative to the wild-type. Albeit multi-objective
approaches focused on the bio-engineering objectives
have been proposed, none tackles the multi-objective
nature of the cellular objectives.
In this work we analyze the inuence of several phenotype
prediction methods on the strainsdesigned by metaheuristic
algorithms and suggest a multi-objective approach capable
of finding designs compliant with the cellular objectives assumed
by the various phenotype prediction methods.
Using a recent model of Escherichia coli K12, we observed
the effect of different phenotype prediction methods in the
convergence of metaheuristic algorithms performing strain
optimization, evolving growth-coupled production mutants
in aerobic and anaerobic conditions. A critical analysis of
the different mutant ux distributions was performed, and
we concluded that, for a selected phenotype prediction
method, the strain designs proposed by the optimization
algorithms were generally not robust when another method
was used to predict their phenotypes.
There is variation in the Biomass-product coupled yield
(BPCY) of aerobically succinate producing mutants with
glucose as carbon source, when solutions generated with
either pFBA (a variation of FBA that minimizes the overall
use of enzyme-associated flux) or LMOMA (a linear implementation
of MOMA) (box colors) are simulated with the
other (x-axis). Besides the great variation in fitness for the
different phenotype simulation methods, we veri_ed that in
some cases less than 10% of the solutions generated by
pFBA are valid in LMOMA (BPCY _ 0:0001).
Assumptions regarding the cellular objectives of an organism
when subjected to distinct conditions (environmental,
genetic, etc.) are still the object of active discussion. This
fact motivated us to develop a method capable of suggesting
designs compliant with more than one phenotype
prediction method. Solutions generated by our method are
simulated using pFBA and LMOMA and plotted by BPCY
for both phenotype simulation methods. The ad-hoc clusters
reveal a group of interesting solutions (cluster 2). An
analysis on the flux distribution of the solutions presented in
these clusters is also provided and a rational for robust
solution design is derived
