In the modern era of omics analyses, understanding and designing genetic interventions to achieve desired microbial phenotypes remains a challenge. Recent advancements in genetic engineering have reduced timelines for building and testing strain designs, enabling a more efficient closed-loop iteration between experiment and analysis. However, the complexity of multiomics datasets necessitates computational techniques to aid in the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. While traditional statistical approaches have been successful in reducing dataset dimensionality and identifying common motifs among high-performing strains, they often underutilize connections between genes, proteins, and metabolic networks. Model-aided design, which integrates experimental data with systems biology modeling frameworks, holds promise for generating effective and non-intuitive design predictions.
The biorefinery concept, aiming for sustainable production of chemicals and fuels from biomass, heavily relies on engineered microbes for high selectivity and yield. However, optimizing microbial metabolism for specific processes is time-consuming and costly due to the intricate relationship between genotype and phenotype. The formalized strain engineering process, encompassing the DBTL cycle, leverages genetic engineering and high-throughput characterization to efficiently screen larger libraries of strain modifications. Computational techniques play a crucial role in interpreting experimental results and suggesting further modification targets, especially in the Learn and Design stages of the DBTL cycle.
Omics data, including transcriptomics, proteomics, metabolomics, and fluxomics, provide valuable insights into cell physiology and metabolic pathways. Integrating these diverse datasets into strain design requires constraint-based methods, kinetic simulations, and machine learning approaches. Constraint-Based Reconstruction and Analysis (COBRA) methods, such as Flux Balance Analysis (FBA), use biological knowledge to constrain intracellular fluxes. Extensions to COBRA frameworks incorporate additional constraints from experimental observations, enhancing the accuracy of metabolic flux predictions. Kinetic metabolic models capture dynamic enzyme behavior and predict steady-state flux distributions based on enzyme expression levels, offering detailed insights into pathway dynamics.
Machine learning methods, with their ability to predict future targets for strain engineering, are increasingly used to interpret omics data and map genotype-phenotype relationships. Integrative omics analyses, regulatory gene prediction, and metabolic performance prediction are some applications of machine learning in microbial strain design. While COBRA methods benefit from strong software support, kinetic models face challenges due to computational intensity, and machine learning approaches require tailored libraries for omics-specific analyses. Standardized computational workflows are essential for reproducible analyses in the iterative DBTL cycle, calling for further software development and best practices in the metabolic modeling community.
Tags: validation, mass spectrometry, enzyme production, transcriptomics, regulatory, metabolic engineering, cell culture, multiomics, metabolomics
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