MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering.

Area of Science:

• Metabolic Engineering
• Systems Biology
• Biotechnology

Background:

• Addresses multi-objective optimization challenges in metabolic engineering involving mixed continuous and integer variables.
• Proposes a novel multi-objective model for identifying reaction deletions to simultaneously optimize multiple cellular objectives.
• Highlights common applications such as maximizing bioproduct and biomass co-production or optimizing bioproduct yield while minimizing by-products.

Purpose of the Study:

• To develop and validate a computational framework for multi-objective metabolic engineering.
• To identify specific genetic modifications (reaction deletions) that improve key performance indicators in microbial cell factories.
• To demonstrate the practical utility of the proposed optimization approach in a relevant bioproduction context.

Main Methods:

• Development of a multi-objective optimization model incorporating both continuous and integer decision variables.
• Application of the model to a case study involving ethanol production in Saccharomyces cerevisiae.
• In vivo validation of predicted genetic modifications (reaction deletions).

Main Results:

• The proposed multi-objective optimization approach successfully identified genetic manipulations for improving ethanol production in Saccharomyces cerevisiae.
• In vivo experiments confirmed that certain predicted reaction deletions led to increased ethanol levels compared to the wild-type strain.
• Demonstrated the model's capability to guide genetic engineering strategies for enhanced bioproduct formation.

Conclusions:

• The developed multi-objective programming framework, MOMO, provides an effective tool for metabolic engineering.
• MOMO integrates with the POLYSCIP multi-objective solver for robust optimization.
• The open-source nature of MOMO facilitates its adoption and further development in the field.