Automated Parameterization of Predictive Kinetic Metabolic Models from Sparse Datasets for Efficient Optimization of Many-Enzyme Heterologous Pathways

Abstract

Engineered heterologous metabolic pathways can convert low-cost feedstock into high-value products, though it remains a significant challenge to reliably and efficiently maximize end-product biosynthesis, particularly when many enzymes must be co-expressed together. When current approaches are applied to many-enzyme pathways, the construction and characterization process is highly iterative and laborious, while generating high-dimensional datasets that remain difficult to analyze for forward engineering efforts. To overcome these challenges, we developed a new algorithm that determines the highly non-linear and high-dimensional relationship between a pathways enzyme expression levels and its end-product productivity from common characterization of a small number of heterologous pathway variants. We combined kinetic metabolic modeling, elementary mode analysis, model reduction, de-dimensionalization, and genetic algorithm optimization into an automated procedure that parameterizes accurate kinetic metabolic models from sparsely characterized pathway variant libraries with varied enzyme expression levels. The resulting Pathway Maps are used to determine rate-limiting steps, predict optimal expression levels, identify allosteric interactions, rank-order enzyme kinetics, and prioritize protein engineering efforts. We demonstrate the Pathway Map Calculator algorithm on two experimental datasets, a 3-enzyme carotenoid biosynthesis pathway and a 9-enzyme limonene biosynthesis pathway, as well as a series of in silico pathway examples to rigorously demonstrate the algorithms accuracy, linear scaling, and high tolerance to measurement noise. By greatly reducing experimental efforts and providing quantitative forward engineering predictions, the Pathway Map Calculator has the potential to dramatically accelerate the engineering of many-enzyme heterologous metabolic pathways.\n\nHighlightsO_LIWe developed an automated algorithm that uses a small number of characterized pathway variants to determine the pathways expression-productivity relationship.\nC_LIO_LIThe Pathway Map Calculator is accurate, scales linearly on many-enzyme pathways, distinguishes allosteric interactions, and tolerates substantial measurement noise.\nC_LIO_LIPathway Maps are used to predict optimal enzyme expression levels, identify rate-limiting steps, and prioritize protein engineering efforts\nC_LI