Closing loop: Systems-level integration of synthetic consortia and process engineering for lignocellulosic microbial lipid biomanufacturing
Source abstract
Abstract
Amidst the urgent quest for carbon neutrality, lignocellulosic biomass has emerged as a key feedstock for sustainable biomanufacturing. However, the commercial viability of converting this recalcitrant resource into microbial lipids remains constrained by fragmented unit operations, particularly the trade-offs between biomass deconstruction efficiency and downstream inhibitor toxicity. This review moves beyond a linear technological summary to propose an integrated roadmap for next-generation biorefineries. We analyze the convergence of flexible and broadly applicable pretreatment strategies across diverse lignocellulosic feedstocks and synthetic biology-driven strain engineering, highlighting how tools such as Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 and RNA interference enable more precise control of metabolic flux toward lipid precursors. Furthermore, we extend the discussion from monoculture systems to emerging artificial microbial consortia, which offer opportunities for functional division of labor in simultaneous inhibitor detoxification and lipid accumulation, while also presenting challenges in stability and metabolic coordination. In addition, we discuss how data-driven strategies, including machine learning and techno-economic analysis, can help bridge the gap between laboratory-scale advances and industrial implementation. By integrating insights from feedstock chemistry, microbial physiology, and process engineering, this review provides a systems-level perspective on the development of economically viable and low-carbon lipid biomanufacturing platforms.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microbial Metabolic Engineering and Bioproduction · Biochemical and biochemical processes · Enzyme Catalysis and Immobilization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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