Perspectives in growth production trade-off in microbial bioproduction

Banerjee, Deepanwita; Mukhopadhyay, Aindrila

doi:10.1039/d2su00066k

Cited by 8 publications

(7 citation statements)

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“…Genome scale metabolic models are comprehensive representations of cellular metabolism that have potential for use in the predictive design of microbes. GSMMs have been extended to heterologous pathways and many other organisms (Maia et al , 2016; Banerjee & Mukhopadhyay, 2023). These methods are ideal for optimizing bioconversion processes used to produce commodity chemicals as most require high productivity using a range of carbon sources.…”

Section: Discussionmentioning

confidence: 99%

Ensemble and Iterative Engineering for Maximized Bioconversion to the Blue Pigment, Indigoidine from Non-Canonical Sustainable Carbon Sources

Eng

Banerjee

Menasalvas

et al. 2023

Preprint

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While many heterologous molecules can be produced at trace concentrations via microbial bioconversion processes, maximizing their titers, rates, and yields from lignin-derived carbon streams remains challenging. Growth coupling can not only increase titers and yields but also shift the production period from stationary phase to growth phase. These methods for designing growth-coupling strains however require multi-gene edits for implementation which may be perceived as impractical. Here, we computationally evaluated 4,114 potential solutions for growth couplingpara-coumarate to indigoidine production and prototype two cut sets inPseudomonas putidaKT2440. We used adaptive laboratory evolution (ALE) on the initial triple deletion strain to restore growth onp-CA. Using X-ray tomography on this post-ALE strain we revealed increased cell density and decreased cell volume. Proteomics identified upregulated peroxidases that mitigate reactive oxygen species formation. Nine iterative stepwise modifications further informed by model-guided and rational approaches realized a growth coupled strain that produced 7.3 g/L indigoidine at 77% MTY inpara-coumarate minimal media. These ensemble strategies provide a blueprint for producing target molecules at high product titers, rates, and yields.

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Section: Discussionmentioning

confidence: 99%

Ensemble and Iterative Engineering for Maximized Bioconversion to the Blue Pigment, Indigoidine from Non-Canonical Sustainable Carbon Sources

Eng

Banerjee

Menasalvas

et al. 2023

Preprint

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“…A typical metabolic trade-off occurs when an increase in product yield diverts resources from biomass synthesis, consequently reducing biomass yields and volumetric productivity rates . To address this issue, dynamic transitions between metabolic states, such as those from growth to production, can lead to improved volumetric productivity in bioprocesses. − Dynamic metabolic engineering can also be considered for modulating the flux through production pathways toward achieving a desired product composition.…”

Section: Introductionmentioning

confidence: 99%

“…There are different strategies to enable dynamic metabolic engineering. − At the transcriptional level, the most widely implemented strategy, the gene transcription of key metabolic enzymes is modulated over time. With this, the intracellular concentration of these enzymes is adjusted dynamically, influencing the achievable metabolic fluxes in the cell.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis

Espinel-Ríos,

Avalos

2024

Ind. Eng. Chem. Res.

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Metabolic cybergenetics is a promising concept that interfaces gene expression and cellular metabolism with computers for real-time dynamic metabolic control. The focus is on control at the transcriptional level, serving as a means to modulate intracellular metabolic fluxes. Recent strategies in this field have employed constraint-based dynamic models for process optimization, control, and estimation. However, this results in bilevel dynamic optimization problems, which pose considerable numerical and conceptual challenges. In this study, we present an alternative hybrid physics-informed dynamic modeling framework for metabolic cybergenetics, aimed at simplifying optimization, control, and estimation tasks. By utilizing machine-learning surrogates, our approach effectively embeds the physics of metabolic networks into the process rates of structurally simpler macrokinetic models coupled with gene expression. These surrogates, informed by flux balance analysis, link the domains of manipulatable intracellular enzymes to metabolic exchange fluxes. This ensures that critical knowledge captured by the system's metabolic network is preserved. The resulting models can be integrated into metabolic cybergenetic schemes involving single-level optimizations. Additionally, the hybrid modeling approach maintains the number of system states at a necessary minimum, easing the burden of process monitoring and estimation. Our hybrid physics-informed metabolic cybergenetic framework is demonstrated using a computational case study on the optogenetically assisted production of itaconate by Escherichia coli.

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“…By implementing the required gene deletions, these changes dramatically improve the productivity of a targeted metabolite or a final product where the chosen metabolite is generated concomitant with microbial growth. Growth coupling has been demonstrated for products using relaxed yield thresholds and model carbon substrates (i.e., glucose) (1,2), but there are fewer examples of such approaches for non-sugar carbon streams (3)(4)(5) from renewable bioenergy streams like depolymerized plastics or aromatics from plant-derived lignocellulosic biomass.…”

Section: Introductionmentioning

confidence: 99%

“…Growth coupling algorithms, which promise substantial improvements to titer rates and yield (TRY) are termed “strong” growth coupling, also demand many gene modifications (upwards of 15 gene deletions) that are nontrivial for implementation (6). Both partial and complete implementation of such designs, by themselves or in conjunction with other approaches have shown useful applications for bioproduction (1,7–9) and reveal key insights into the host strain physiology.…”

Section: Introductionmentioning

confidence: 99%

Bottlenecks in the Implementation of Genome Scale Metabolic Model Based Designs for Bioproduction from Aromatic Carbon Sources

Banerjee,

Menasalvas,

Chen

et al. 2024

Preprint

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Genome scale metabolic models (GSMM) are commonly used to identify gene deletion sets that result in growth coupling, pairing product formation with substrate utilization. While such approaches can improve strain performance beyond levels typically accessible using targeted strain engineering approaches, sustainable feedstocks often pose a challenge for GSMM-based methods due to incomplete underlying metabolic data. Specifically, we address a four-gene deletion design for the lignin-derived non-sugar carbon source, para-coumarate, that proved challenging to implement. We examine the performance of the fully implemented design for p-coumarate to glutamine, a useful biomanufacturing intermediate. In this study glutamine is then converted to indigoidine, an alternative sustainable pigment and a model heterologous product. Through omics, promoter-variation and growth characterization of a fully implemented gene deletion design, we provide evidence that aromatic catabolism in the completed design is rate-limited by fumarate hydratase activity in the citrate cycle and required careful optimization of the final fumarate hydratase protein (PP_0897) expression to achieve growth and production. A metabolic cross-feeding experiment with the completed design strain also revealed an unanticipated nutrient requirement suggesting additional functions for the fumarate hydratase protein. A double sensitivity analysis confirmed a strict requirement for fumarate hydratase activity in the strain where all genes in the growth coupling design have been implemented. While a complete implementation of the design was achieved, this study highlights the challenge of precisely inactivating metabolic reactions encoded by under-characterized proteins especially in the context of multi-gene edits.

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Perspectives in growth production trade-off in microbial bioproduction

Abstract: Metabolic engineering of microbial systems and conversion routes provide robust platforms for production of bulk commodities for food, materials and fuel targets. For products in this range, maximal conversion of...

Cited by 8 publications

References 82 publications

Ensemble and Iterative Engineering for Maximized Bioconversion to the Blue Pigment, Indigoidine from Non-Canonical Sustainable Carbon Sources

Ensemble and Iterative Engineering for Maximized Bioconversion to the Blue Pigment, Indigoidine from Non-Canonical Sustainable Carbon Sources

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis

Bottlenecks in the Implementation of Genome Scale Metabolic Model Based Designs for Bioproduction from Aromatic Carbon Sources

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