From Strain Readiness to Systems Readiness in Industrial Bioprocessing

Industrial biotechnology can now generate new hosts, pathways, feedstocks, and product concepts with unprecedented speed. Yet promising laboratory performance often fails to become credible manufacturing performance. This article argues that the gap cannot be addressed by strain readiness alone. It also requires systems readiness: the extent to which a biologically promising process can be observed, controlled, transferred, and supported by evidence beyond proof-of-concept. This is especially important between strain development, upstream process development, and scale-up, where weak observability and fragmented system architecture can turn promising biology into uncertain industrial performance. Analytics, automation, sampling, digital models, and infrastructure should therefore be treated as essential parts of scalable process development.

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Publication Details

Published
2026-11-11
DOI
https://doi.org/10.31224/8790
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
preprint
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preprint

From Strain Readiness to Systems Readiness in Industrial Bioprocessing

Furkan Gökçe
Microbial Metabolic Engineering and Bioproduction
preprint

From Strain Readiness to Systems Readiness in Industrial Bioprocessing

Furkan Gökçe
preprint en

Abstract

Industrial biotechnology can now generate new hosts, pathways, feedstocks, and product concepts with unprecedented speed. Yet promising laboratory performance often fails to become credible manufacturing performance. This article argues that the gap cannot be addressed by strain readiness alone. It also requires systems readiness: the extent to which a biologically promising process can be observed, controlled, transferred, and supported by evidence beyond proof-of-concept. This is especially important between strain development, upstream process development, and scale-up, where weak observability and fragmented system architecture can turn promising biology into uncertain industrial performance. Analytics, automation, sampling, digital models, and infrastructure should therefore be treated as essential parts of scalable process development.

Microbial Metabolic Engineering and Bioproduction
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