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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational

David R Penas¹, David Henriques¹, Patricia González²

¹BioProcess Engineering Group. IIM-CSIC, Spanish National Research Council. C/Eduardo Cabello, 6. E-36208 Vigo, Spain.

|August 17, 2017

Summary

This summary is machine-generated.

We developed saCeSS2, a parallel method for solving large mixed-integer dynamic optimization problems in computational systems biology. This approach enhances reverse engineering of complex biological pathways, achieving significant speedups and improved performance.

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Area of Science:

Computational systems biology
Bioinformatics
Mathematical modeling

Background:

Addresses global optimization problems with nonlinear dynamic models.
Focuses on reverse engineering in computational systems biology, leading to large mixed-integer dynamic optimization (MIDO) problems.
Utilizes the framework of logic-based ordinary differential equations (ODEs).

Purpose of the Study:

To present saCeSS2, a novel parallel method for solving large MIDO problems.
To enhance the reverse engineering of complex biological pathways using computational systems biology.
To improve the efficiency and scalability of solving large-scale dynamic optimization problems.

Main Methods:

Introduces saCeSS2, a parallel cooperative scatter search metaheuristic.
Incorporates self-adaptation mechanisms and extensions for large mixed-integer problems.
Employs adaptive cooperation strategies to prevent convergence stagnation.

Main Results:

Demonstrates saCeSS2's performance on three challenging cell signaling pathway models.
Achieves superlinear speedups (e.g., 15x with 10 cores) through cooperative parallel searches.
Shows significant performance improvements (>60%) over non-cooperative parallel schemes and good scalability up to 300 cores.

Conclusions:

saCeSS2 successfully reverse engineers large dynamic models of complex biological pathways.
Opens new avenues for MIDO applications in metabolic engineering, synthetic biology, and drug scheduling.
Highlights the effectiveness of parallel cooperative search for large-scale optimization in life sciences.