Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

TL;DR

This paper introduces a quadruple-precision optimization method and a hybrid solution procedure to reliably and efficiently solve large-scale genome models of metabolism and macromolecular expression, overcoming precision and speed limitations.

Contribution

Development of a quadruple-precision optimizer and a hybrid solution method (DQQ) for large-scale ME models, improving accuracy and efficiency over existing solvers.

Findings

Enables accurate solutions for large ME models with 70,000 constraints.

Significantly reduces computation time compared to exact simplex methods.

Ensures reliable solutions where standard double-precision solvers fail.

Abstract

Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers are extremely slow and hence not practical for ME models that currently have 70,000 constraints and variables and will grow larger. We have developed a quadruple-precision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves efficiency and reliability for ME models. DQQ enables extensive use of large, multiscale, linear and nonlinear models in systems biology and many other applications.