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Aida H, Ying BW. Efforts to Minimise the Bacterial Genome as a Free-Living Growing System. BIOLOGY 2023; 12:1170. [PMID: 37759570 PMCID: PMC10525146 DOI: 10.3390/biology12091170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023]
Abstract
Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.
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Affiliation(s)
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Ibaraki, Japan
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Hashizume T, Ozawa Y, Ying BW. Employing active learning in the optimization of culture medium for mammalian cells. NPJ Syst Biol Appl 2023; 9:20. [PMID: 37253825 DOI: 10.1038/s41540-023-00284-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/18/2023] [Indexed: 06/01/2023] Open
Abstract
Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems. Here, we addressed this issue by employing active learning. Specifically, we introduced machine learning to cell culture experiments to optimize culture medium. The cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used to find optimized media as pilot studies. To acquire the training data, cell culture was performed in a large variety of medium combinations. The cellular NAD(P)H abundance, represented as A450, was used to indicate the goodness of culture media. In active learning, regular and time-saving modes were developed using culture data at 168 h and 96 h, respectively. Both modes successfully fine-tuned 29 components to generate a medium for improved cell culture. Intriguingly, the two modes provided different predictions for the concentrations of vitamins and amino acids, and a significant decrease was commonly predicted for fetal bovine serum (FBS) compared to the commercial medium. In addition, active learning-assisted medium optimization significantly increased the cellular concentration of NAD(P)H, an active chemical with a constant abundance in living cells. Our study demonstrated the efficiency and practicality of active learning for medium optimization and provided valuable information for employing machine learning technology in cell biology experiments.
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Affiliation(s)
- Takamasa Hashizume
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572, Ibaraki, Japan
| | - Yuki Ozawa
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572, Ibaraki, Japan
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572, Ibaraki, Japan.
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Borkowski O, Koch M, Zettor A, Pandi A, Batista AC, Soudier P, Faulon JL. Large scale active-learning-guided exploration for in vitro protein production optimization. Nat Commun 2020; 11:1872. [PMID: 32312991 PMCID: PMC7170859 DOI: 10.1038/s41467-020-15798-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 03/24/2020] [Indexed: 12/21/2022] Open
Abstract
Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free buffer compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.
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Affiliation(s)
- Olivier Borkowski
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Mathilde Koch
- Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Agnès Zettor
- Chemogenomic and Biological Screening Core Facility, Institut Pasteur, Department of Structural Biology and Chemistry, Center for Technological Resources and Research (C2RT), 25/28 rue du Dr Roux, 75724, Paris Cedex 15, France
| | - Amir Pandi
- Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | | | - Paul Soudier
- Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France. .,Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France. .,SYNBIOCHEM Center, Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, UK.
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The cystic fibrosis transmembrane conductance regulator (CFTR) and its stability. Cell Mol Life Sci 2016; 74:23-38. [PMID: 27734094 PMCID: PMC5209436 DOI: 10.1007/s00018-016-2386-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 09/28/2016] [Indexed: 12/11/2022]
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is responsible for the disease cystic fibrosis (CF). It is a membrane protein belonging to the ABC transporter family functioning as a chloride/anion channel in epithelial cells around the body. There are over 1500 mutations that have been characterised as CF-causing; the most common of these, accounting for ~70 % of CF cases, is the deletion of a phenylalanine at position 508. This leads to instability of the nascent protein and the modified structure is recognised and then degraded by the ER quality control mechanism. However, even pharmacologically ‘rescued’ F508del CFTR displays instability at the cell’s surface, losing its channel function rapidly and it is rapidly removed from the plasma membrane for lysosomal degradation. This review will, therefore, explore the link between stability and structure/function relationships of membrane proteins and CFTR in particular and how approaches to study CFTR structure depend on its stability. We will also review the application of a fluorescence labelling method for the assessment of the thermostability and the tertiary structure of CFTR.
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de Ridder D, de Ridder J, Reinders MJT. Pattern recognition in bioinformatics. Brief Bioinform 2013; 14:633-47. [DOI: 10.1093/bib/bbt020] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Fu Y, Zhu X, Elmagarmid AK. Active Learning With Optimal Instance Subset Selection. IEEE TRANSACTIONS ON CYBERNETICS 2013; 43:464-75. [PMID: 22910122 DOI: 10.1109/tsmcb.2012.2209177] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.
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