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A Rapid Method for Modeling a Variable Cycle Engine
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solveME: fast and reliable solution of nonlinear ME models
Laurence Yang1, Ding Ma2, Ali Ebrahim1
1Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA.
BMC Bioinformatics
|September 24, 2016
Summary
New computational methods accelerate the analysis of genome-scale metabolism and macromolecular expression (ME) models. These faster, reliable solutions for ME models will enhance their use in systems biology research.
Area of Science:
- Systems Biology
- Computational Biology
- Metabolic Engineering
Background:
- Genome-scale models of metabolism and macromolecular expression (ME) expand constraint-based modeling capabilities.
- ME models are computationally challenging due to their size, multiscale nature, and nonlinear programming (NLP) requirements for growth maximization.
Purpose of the Study:
- To develop computationally efficient and numerically reliable solution methods for ME models.
- To address the challenges associated with large-scale ME model simulations and analyses.
Main Methods:
- Developed a fast, quad-precision NLP solver (Quad MINOS) for growth maximization in ME models.
- Implemented a fast, quad-precision flux variability analysis accelerated by solver warm-starts.
- Applied these tools to investigate growth-coupled succinate overproduction under proteome constraints.
Main Results:
- The new method for growth maximization was up to 45% faster than binary search for high precision.
- Flux variability analysis achieved up to a 60x speedup using solver warm-starts.
- Successfully investigated succinate overproduction using the developed ME modeling tools.
Conclusions:
- The developed solution methods significantly improve the speed and reliability of ME model analysis.
- These advancements are expected to accelerate the adoption and application of ME models in systems biology and related fields.
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