optflux 3.3.0 (Rocha labs)
90
Structured Review
Rocha labs
optflux 3.3.0
Optflux 3.3.0, supplied by Rocha labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/optflux+3%2E3%2E0/optflux+3+3+0/pm33615444-72-20-22
Average 90 stars, based on 1 article reviews
Optflux 3.3.0, supplied by Rocha labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/optflux+3%2E3%2E0/optflux+3+3+0/pm33615444-72-20-22
Average 90 stars, based on 1 article reviews
optflux 3.3.0 - by Bioz Stars,
2026-09
90/100 stars
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Related Articles
In Silico:Article Title: Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors. Article Snippet: Funding information Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant/Award Number: 2016/ 10636‐8; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Grant/Award Number: Finance Code 001; Conselho Nacional de Desenvolvimento Científico e Tecnológico, Grant/Award Number: 409366/2016‐1 Abstract Various bio‐based processes depend on controlled micro‐aerobic conditions to achieve a satisfactory product yield.. However, the limiting oxygen concentration varies according to the micro‐organism employed, while for industrial applications, there is no cost‐effective way of measuring it at low levels.. This study proposes a machine learning procedure within a metabolic flux‐based control strategy (SUPERSYS_MCU) to address this issue. Software:Article Title: Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors. Article Snippet: Funding information Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant/Award Number: 2016/ 10636‐8; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Grant/Award Number: Finance Code 001; Conselho Nacional de Desenvolvimento Científico e Tecnológico, Grant/Award Number: 409366/2016‐1 Abstract Various bio‐based processes depend on controlled micro‐aerobic conditions to achieve a satisfactory product yield.. However, the limiting oxygen concentration varies according to the micro‐organism employed, while for industrial applications, there is no cost‐effective way of measuring it at low levels.. This study proposes a machine learning procedure within a metabolic flux‐based control strategy (SUPERSYS_MCU) to address this issue. |