Cell factory design with advanced metabolic modelling empowered by artificial intelligence.

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production.

Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https://github.com/chang88ye/OptDesign.

Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism.

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

Metabolic engineering of Mannheimia succiniciproducens for succinic acid production based on elementary mode analysis with clustering.

Mannheimia succiniciproducens, a capnophilic gram-negative rumen bacterium, has been employed for the efficient production of succinic acid. Although M. succiniciproducens metabolism was previously studied using a genome-scale metabolic model, more metabolic characteristics are to be understood. To this end, elementary mode analysis accompanied with clustering ('EMC' analysis) is used to gain further insights on metabolic characteristics of M. succiniciproducens allowing efficient succinic acid production. Elementary modes (EMs) generated from the central carbon metabolic network of M. succiniciproducens are clustered to systematically analyze succinic acid production routes. Based on the results of EMC analysis, zwf gene is identified as a novel overexpression target for the improved succinic acid production. This gene is overexpressed in a previously constructed succinic acid-overproducing M. succiniciproducens LPK7 strain. Heterologous NADPH-dependent mdh is later intuitively selected for overexpression to synergistically improve succinic acid production by utilizing abundant NADPH pool mediated by the overexpressed zwf. The LPK7 strains co-expressing mdh alone and both zwf and mdh genes are subjected to fed-batch fermentation to better examine their succinic acid production performances. Strategies of EMC analysis will be useful for further metabolic engineering of M. succiniciproducens and other microorganisms to improve production of succinic acid and other chemicals of interest.