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, methods for phenotype
prediction were put forward, as well as strain optimization
methods, within the field of Metabolic Engineering. Evolutionary
computation has been on the front line, with the proposal of
bilevel metaheuristics, where EC works over phenotype simulation,
selecting the most promising solutions for bioengineering tasks.
Recently, Schuetz and co-workers proposed that the metabolism
of bacteria operates close to the Pareto-optimal surface of a
three-dimensional space defined by competing objectives. Albeit
multi-objective strain optimization approaches focused on bioengineering
objectives have been proposed, none tackles the multiobjective
nature of the cellular objectives. In this work, we propose
multi-objective evolutionary algorithms for strain optimization,
where objective functions are defined based on distinct phenotype
prediction methods, showing that those can lead to more robust
designs, allowing to find solutions in more complex scenarios.(undefined)info:eu-repo/semantics/publishedVersio
