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

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

Published on: July 25, 2013

Robust mutant strain design by pessimistic optimization.

Meltem Apaydin¹, Liang Xu², Bo Zeng²

¹Dept. of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, USA.

|October 7, 2017

Summary

This summary is machine-generated.

This study introduces pessimistic optimization for microbial strain design, creating more robust mutants that reliably overproduce biochemicals even with uncertain cell survival models. This improves experimental design for microbial engineering.

Keywords:

Pessimistic bi-level optimization Stoichiometric models Strain optimization

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Area of Science:

Metabolic Engineering
Computational Biology
Systems Biology

Background:

Flux Balance Analysis (FBA) enables in silico prediction of metabolic networks.
Existing bi-level optimization methods for strain engineering assume cooperative mutants, which may not hold true in practice.

Purpose of the Study:

To evaluate the robustness of existing bi-level strain optimization methods under uncertainty and non-cooperative conditions.
To propose novel pessimistic optimization formulations (P-ROOM, P-OptKnock) for designing robust microbial mutants.

Main Methods:

Developed pessimistic optimization formulations (P-ROOM, P-OptKnock) considering uncertainty in cell survival models.
Converted multi-level formulations into single-level Mixed Integer Programming (MIP) problems using strong duality.
Tested formulations on E. coli core and iAF1260 metabolic networks.

Main Results:

Optimistic methods (ROOM, OptKnock) may fail to achieve predicted overproduction under uncertainty.
Pessimistic formulations (P-ROOM, P-OptKnock) yield more robust mutants with higher chemical production rates.
Higher uncertainty levels led to identical mutant strains under both pessimistic approaches.

Conclusions:

Proposed a new pessimistic optimization framework for robust mutant strain design.
Pessimistic methods provide reliable solutions irrespective of approximate cell survival models.
Results offer confidence for in vivo experimental design of microbial mutants.