Michael Binns , Pedro de Atauri , Marta Cascante , Constantinos Theodoropoulos
{"title":"利用控制偏差确定生物生产改进的初始目标。","authors":"Michael Binns , Pedro de Atauri , Marta Cascante , Constantinos Theodoropoulos","doi":"10.1016/j.nbt.2025.07.008","DOIUrl":null,"url":null,"abstract":"<div><div>Sensitivity analysis of bioprocess metabolic reaction networks analysis allows the prediction of system parameters such as those associated with the enzyme activity of certain reaction steps which significantly affect the overall production. However, uncertainties in kinetic rate expressions and in the resulting steady-state flux distributions limit the accuracy of these predictions. Starting from minimal information (reaction stoichiometry, and external fluxes in/out of the system and potentially identification of steps at equilibrium) a new preliminary method is proposed using sampling of elasticities and metabolic fluxes to calculate the control bias. The calculated control bias identifies steps which are likely to have positive control, negative control or negligible/uncertain control. This is intended to give initial guidance before further detailed investigation is carried out, identifying targets for any organism to enhance production of valuable chemicals. As a case study, this methodology is applied to succinic acid bioproduction using <em>Actinobacillus succinogenes</em> and analysis successfully reveals the reaction steps having the greatest positive and negative influence on biosuccinic acid production.</div></div>","PeriodicalId":19190,"journal":{"name":"New biotechnology","volume":"89 ","pages":"Pages 130-140"},"PeriodicalIF":4.9000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using control bias to identify initial targets for bioproduction improvement\",\"authors\":\"Michael Binns , Pedro de Atauri , Marta Cascante , Constantinos Theodoropoulos\",\"doi\":\"10.1016/j.nbt.2025.07.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Sensitivity analysis of bioprocess metabolic reaction networks analysis allows the prediction of system parameters such as those associated with the enzyme activity of certain reaction steps which significantly affect the overall production. However, uncertainties in kinetic rate expressions and in the resulting steady-state flux distributions limit the accuracy of these predictions. Starting from minimal information (reaction stoichiometry, and external fluxes in/out of the system and potentially identification of steps at equilibrium) a new preliminary method is proposed using sampling of elasticities and metabolic fluxes to calculate the control bias. The calculated control bias identifies steps which are likely to have positive control, negative control or negligible/uncertain control. This is intended to give initial guidance before further detailed investigation is carried out, identifying targets for any organism to enhance production of valuable chemicals. As a case study, this methodology is applied to succinic acid bioproduction using <em>Actinobacillus succinogenes</em> and analysis successfully reveals the reaction steps having the greatest positive and negative influence on biosuccinic acid production.</div></div>\",\"PeriodicalId\":19190,\"journal\":{\"name\":\"New biotechnology\",\"volume\":\"89 \",\"pages\":\"Pages 130-140\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"New biotechnology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1871678425000755\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"New biotechnology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1871678425000755","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Using control bias to identify initial targets for bioproduction improvement
Sensitivity analysis of bioprocess metabolic reaction networks analysis allows the prediction of system parameters such as those associated with the enzyme activity of certain reaction steps which significantly affect the overall production. However, uncertainties in kinetic rate expressions and in the resulting steady-state flux distributions limit the accuracy of these predictions. Starting from minimal information (reaction stoichiometry, and external fluxes in/out of the system and potentially identification of steps at equilibrium) a new preliminary method is proposed using sampling of elasticities and metabolic fluxes to calculate the control bias. The calculated control bias identifies steps which are likely to have positive control, negative control or negligible/uncertain control. This is intended to give initial guidance before further detailed investigation is carried out, identifying targets for any organism to enhance production of valuable chemicals. As a case study, this methodology is applied to succinic acid bioproduction using Actinobacillus succinogenes and analysis successfully reveals the reaction steps having the greatest positive and negative influence on biosuccinic acid production.
期刊介绍:
New Biotechnology is the official journal of the European Federation of Biotechnology (EFB) and is published bimonthly. It covers both the science of biotechnology and its surrounding political, business and financial milieu. The journal publishes peer-reviewed basic research papers, authoritative reviews, feature articles and opinions in all areas of biotechnology. It reflects the full diversity of current biotechnology science, particularly those advances in research and practice that open opportunities for exploitation of knowledge, commercially or otherwise, together with news, discussion and comment on broader issues of general interest and concern. The outlook is fully international.
The scope of the journal includes the research, industrial and commercial aspects of biotechnology, in areas such as: Healthcare and Pharmaceuticals; Food and Agriculture; Biofuels; Genetic Engineering and Molecular Biology; Genomics and Synthetic Biology; Nanotechnology; Environment and Biodiversity; Biocatalysis; Bioremediation; Process engineering.