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Multi-objective mixed integer strategy for the optimisation of biological networks.

J O H Sendín¹, O Exler, J R Banga

¹Process Engineering Group, Vigo, SpainUniversity of Bayreuth, Department of Computer Science, Bayreuth, Germany. julio@iim.csic.es

IET Systems Biology

|May 27, 2010

Summary

This study introduces a new global optimization strategy for complex systems biology problems involving mixed-integer non-linear programming. The method efficiently identifies the Pareto front, aiding in optimal manipulation of biological systems.

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Area of Science:

Systems Biology
Computational Biology
Optimization

Background:

Systems biology problems often involve complex optimization with both continuous and integer variables.
These are mathematically defined as mixed-integer non-linear programming (MINLP) problems.
Efficiently solving these MINLP problems is crucial for understanding and manipulating biological systems.

Purpose of the Study:

To present a novel global optimization strategy for multi-criteria optimization problems in systems biology.
To address challenges posed by mixed-integer non-linear programming formulations.
To demonstrate the strategy's effectiveness in obtaining the Pareto front for metabolic engineering case studies.

Main Methods:

Development of a novel global optimization approach.
Application of the strategy to mixed-integer non-linear programming problems.
Validation using two metabolic engineering case studies.

Main Results:

Successful and efficient computation of the Pareto front for the studied problems.
Demonstration of the strategy's capability in handling multi-criteria optimization.
Gained insights into the optimal manipulation of biological systems.

Conclusions:

The proposed global optimization strategy is effective for solving complex systems biology MINLP problems.
The method facilitates the efficient determination of optimal solution sets (Pareto front).
This approach provides valuable insights for the optimal engineering of biological systems.