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

Metabolic Regulation and Engineering Strategies of Carbon and Nitrogen Metabolism in Escherichia coli

The intricacies of carbon and nitrogen metabolism in Escherichia coli indeed present both challenges and opportunities for metabolic engineering aimed at optimizing microbial production processes. Carbon is the primary energy source and building block for biomolecules at the cellular level, while nitrogen is vital for synthesizing amino acids, nucleotides, and other nitrogen-containing compounds. This review provides a comprehensive summary of the metabolic regulation of central metabolism and outlines engineering strategies for carbon and nitrogen metabolism in E. coli. This perspective enhances our understanding of the molecular mechanisms involved and enables the development of rational metabolic engineering strategies. One key aspect of metabolic engineering consists of understanding the regulatory networks that govern these processes. Both carbon and nitrogen metabolisms are tightly regulated to ensure cellular homeostasis. By elucidating the interconnected nature of carbon and nitrogen metabolism, this review serves not just to better inform the academic community but also to stimulate advancements in biotechnological applications. Metabolic engineering in E. coli, targeting these complex networks, holds immense promise for the sustainable production of chemicals, biofuels, and pharmaceuticals.

CarveAdornCurate: a versatile cloud-based platform for constructing multiscale metabolic models

Multiscale modeling is a promising approach for understanding cellular behaviors. However, existing multiscale modeling tools require meticulously curated genome-scale metabolic models (GEMs) as inputs, limiting the broad applications of multiscale models due to complex and time-consuming construction processes. To this end, we developed a novel workflow named CarveAdornCurate (CAC) for de novo multiscale modeling. The Carve module generates an ensemble of GEMs with strong genetic evidence, which is then upgraded to multiscale models using Adorn module. The Curate module was designed to find features important to the generated models. These three modules are integrated into a cloud-based platform to promote broad accessibility. As proof of concept, we constructed CAC-based multiscale models for Corynebacterium glutamicum and Yarrowia lipolytica, demonstrating their potential in guiding metabolic engineering. Overall, CAC is demonstrated to be an efficient and user-friendly tool for constructing multiscale models. It is available online at www.carveadorncurate.com/.

Systematic development of a highly efficient cell factory for 5-aminolevulinic acid production

The versatile applications of 5-aminolevulinic acid (5-ALA) across the fields of agriculture, livestock, and medicine necessitate a cost-efficient biomanufacturing process. In this study, we achieved the economic viability of biomanufacturing this compound through a systematic engineering framework. First, we obtained a 5-ALA synthase (ALAS) with superior performance by exploring its natural diversity with divergent evolution. Subsequently, using a genome-scale model, we identified and modified four key targets from distinct pathways in Escherichia coli, resulting in a final enhancement of 5-ALA titers up to 21.82 g/l in a 5-l bioreactor. Furthermore, recognizing that an imbalance of redox equivalents hindered further titer improvement, we developed a dynamic control system that effectively balances redox status and carbon flux. Ultimately, we collaboratively optimized the artificial redox homeostasis system at the transcription level with other cofactors at the feeding level, demonstrating the highest recorded performance to date with a titer of 63.39 g/l for the biomanufacturing of 5-ALA.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

BACKGROUND

Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions.

RESULTS

We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies.

CONCLUSIONS

We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Coupling and uncoupling growth and product formation for producing chemicals

Microbial fermentation employs two strategies: growth- and nongrowth-coupled productions. Stoichiometric metabolic models with flux balance analysis enable pathway engineering to couple target synthesis with growth, yielding numerous successful results. Growth-coupled engineering also contributes to improving bottleneck flux through subsequent adaptive evolution. However, because growth-coupled production inevitably shares resources between biomass and target syntheses, the cost-effective production of bulk chemicals mandates a nongrowth-coupled approach. In such processes, understanding how and when to transition the metabolic state from growth to production modes becomes crucial, as does maintaining cellular activity during the nongrowing state to achieve high productivity. In this paper, we review recent technologies for growth-coupled and nongrowth-coupled production, considering their advantages and disadvantages.

Designing gene manipulation schedules for high throughput parallel construction of objective strains

Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.