1
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Yurchenko A, Özkul G, van Riel NAW, van Hest JCM, de Greef TFA. Mechanism-based and data-driven modeling in cell-free synthetic biology. Chem Commun (Camb) 2024; 60:6466-6475. [PMID: 38847387 DOI: 10.1039/d4cc01289e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.
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Affiliation(s)
- Angelina Yurchenko
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Gökçe Özkul
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Natal A W van Riel
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Eindhoven MedTech Innovation Center, 5612 AX Eindhoven, The Netherlands
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jan C M van Hest
- Bio-Organic Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, 3584 CB Utrecht, The Netherlands
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2
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Gilliot PA, Gorochowski TE. Transfer learning for cross-context prediction of protein expression from 5'UTR sequence. Nucleic Acids Res 2024:gkae491. [PMID: 38864396 DOI: 10.1093/nar/gkae491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 04/28/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.
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Affiliation(s)
- Pierre-Aurélien Gilliot
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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3
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Hughes AC, Pittman BG, Xu B, Gammons JW, Webb CM, Nolen HG, Chapman P, Bikoff JB, Schwarz LA. A single-vector intersectional AAV strategy for interrogating cellular diversity and brain function. Nat Neurosci 2024:10.1038/s41593-024-01659-7. [PMID: 38802592 DOI: 10.1038/s41593-024-01659-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/22/2024] [Indexed: 05/29/2024]
Abstract
As discovery of cellular diversity in the brain accelerates, so does the need for tools that target cells based on multiple features. Here we developed Conditional Viral Expression by Ribozyme Guided Degradation (ConVERGD), an adeno-associated virus-based, single-construct, intersectional targeting strategy that combines a self-cleaving ribozyme with traditional FLEx switches to deliver molecular cargo to specific neuronal subtypes. ConVERGD offers benefits over existing intersectional expression platforms, such as expanded intersectional targeting with up to five recombinase-based features, accommodation of larger and more complex payloads and a vector that is easy to modify for rapid toolkit expansion. In the present report we employed ConVERGD to characterize an unexplored subpopulation of norepinephrine (NE)-producing neurons within the rodent locus coeruleus that co-express the endogenous opioid gene prodynorphin (Pdyn). These studies showcase ConVERGD as a versatile tool for targeting diverse cell types and reveal Pdyn-expressing NE+ locus coeruleus neurons as a small neuronal subpopulation capable of driving anxiogenic behavioral responses in rodents.
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Affiliation(s)
- Alex C Hughes
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Human Cell Types, Allen Institute for Brain Science, Seattle, WA, USA
| | - Brittany G Pittman
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Beisi Xu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jesse W Gammons
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Charis M Webb
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Hunter G Nolen
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Phillip Chapman
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jay B Bikoff
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Lindsay A Schwarz
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA.
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4
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Koksaldi I, Park D, Atilla A, Kang H, Kim J, Seker UOS. RNA-Based Sensor Systems for Affordable Diagnostics in the Age of Pandemics. ACS Synth Biol 2024; 13:1026-1037. [PMID: 38588603 PMCID: PMC11036506 DOI: 10.1021/acssynbio.3c00698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/25/2024] [Accepted: 03/25/2024] [Indexed: 04/10/2024]
Abstract
In the era of the COVID-19 pandemic, the significance of point-of-care (POC) diagnostic tools has become increasingly vital, driven by the need for quick and precise virus identification. RNA-based sensors, particularly toehold sensors, have emerged as promising candidates for POC detection systems due to their selectivity and sensitivity. Toehold sensors operate by employing an RNA switch that changes the conformation when it binds to a target RNA molecule, resulting in a detectable signal. This review focuses on the development and deployment of RNA-based sensors for POC viral RNA detection with a particular emphasis on toehold sensors. The benefits and limits of toehold sensors are explored, and obstacles and future directions for improving their performance within POC detection systems are presented. The use of RNA-based sensors as a technology for rapid and sensitive detection of viral RNA holds great potential for effectively managing (dealing/coping) with present and future pandemics in resource-constrained settings.
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Affiliation(s)
- Ilkay
Cisil Koksaldi
- UNAM
− Institute of Materials Science and Nanotechnology, National
Nanotechnology Research Center (UNAM), Bilkent
University, Ankara 06800, Turkey
| | - Dongwon Park
- Department
of Life Sciences, Pohang University of Science
and Technology, Pohang 37673, South Korea
| | - Abdurahman Atilla
- UNAM
− Institute of Materials Science and Nanotechnology, National
Nanotechnology Research Center (UNAM), Bilkent
University, Ankara 06800, Turkey
| | - Hansol Kang
- Department
of Life Sciences, Pohang University of Science
and Technology, Pohang 37673, South Korea
| | - Jongmin Kim
- Department
of Life Sciences, Pohang University of Science
and Technology, Pohang 37673, South Korea
| | - Urartu Ozgur Safak Seker
- UNAM
− Institute of Materials Science and Nanotechnology, National
Nanotechnology Research Center (UNAM), Bilkent
University, Ankara 06800, Turkey
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5
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Hayashi N, Lai Y, Fuerte-Stone J, Mimee M, Lu TK. Cas9-assisted biological containment of a genetically engineered human commensal bacterium and genetic elements. Nat Commun 2024; 15:2096. [PMID: 38453913 PMCID: PMC10920895 DOI: 10.1038/s41467-024-45893-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
Sophisticated gene circuits built by synthetic biology can enable bacteria to sense their environment and respond predictably. Engineered biosensing bacteria outfitted with such circuits can potentially probe the human gut microbiome to prevent, diagnose, or treat disease. To provide robust biocontainment for engineered bacteria, we devised a Cas9-assisted auxotrophic biocontainment system combining thymidine auxotrophy, an Engineered Riboregulator (ER) for controlled gene expression, and a CRISPR Device (CD). The CD prevents the engineered bacteria from acquiring thyA via horizontal gene transfer, which would disrupt the biocontainment system, and inhibits the spread of genetic elements by killing bacteria harboring the gene cassette. This system tunably controlled gene expression in the human gut commensal bacterium Bacteroides thetaiotaomicron, prevented escape from thymidine auxotrophy, and blocked transgene dissemination. These capabilities were validated in vitro and in vivo. This biocontainment system exemplifies a powerful strategy for bringing genetically engineered microorganisms safely into biomedicine.
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Affiliation(s)
- Naoki Hayashi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- JSR-Keio University Medical and Chemical Innovation Center (JKiC), JSR Corp., 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Yong Lai
- Synthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA
- Research Laboratory of Electronics, MIT, Cambridge, MA, 02139, USA
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR
| | - Jay Fuerte-Stone
- Department of Microbiology, The University of Chicago, Chicago, IL, 60637, USA
| | - Mark Mimee
- Department of Microbiology, The University of Chicago, Chicago, IL, 60637, USA.
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA.
| | - Timothy K Lu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Synthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA.
- Research Laboratory of Electronics, MIT, Cambridge, MA, 02139, USA.
- Broad Institute, Cambridge, MA, 02139, USA.
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, 02139, USA.
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6
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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7
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Takahashi K, Galloway KE. RNA-based controllers for engineering gene and cell therapies. Curr Opin Biotechnol 2024; 85:103026. [PMID: 38052131 DOI: 10.1016/j.copbio.2023.103026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 12/07/2023]
Abstract
Engineered RNA-based genetic controllers provide compact, tunable, post-transcriptional gene regulation. As RNA devices are generally small, these devices are portable to DNA and RNA viral vectors. RNA tools have recently expanded to allow reading and editing of endogenous RNAs for profiling and programming of transcriptional states. With their expanded capabilities and highly compact, modular, and programmable nature, RNA-based controllers will support greater safety, efficacy, and performance in gene and cell-based therapies. In this review, we highlight RNA-based controllers and their potential as user-guided and autonomous systems for control of gene and cell-based therapies.
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Affiliation(s)
- Kei Takahashi
- Department of Chemical Engineering, MIT, 25 Ames St., Cambridge, MA 02139, USA
| | - Kate E Galloway
- Department of Chemical Engineering, MIT, 25 Ames St., Cambridge, MA 02139, USA.
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8
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Kang X, Zhao C, Chen S, Zhang X, Xue B, Li C, Wang S, Yang X, Xia Z, Xu Y, Huang Y, Qiu Z, Li C, Wang J, Pang J, Shen Z. Development of a cell-free toehold switch for hepatitis A virus type I on-site detection. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5813-5822. [PMID: 37870419 DOI: 10.1039/d3ay01408h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
Picornavirus hepatitis A virus (HAV) is a common cause of hepatitis worldwide. It is spread primarily through contaminated food and water or person-to-person contact. HAV I has been identified as the most common type of human HAV infection. Here, we have developed a cell-free toehold switch sensor for HAV I detection. We screened 10 suitable toehold switch sequences using NUPACK software, and the VP1 gene was used as the target gene. The optimal toehold switch sequence was selected by in vivo expression. The best toehold switch concentration was further found to be 20 nM in a cell-free system. 5 nM trigger RNA activated the toehold switch to generate visible green fluorescence. The minimum detection concentration decreased to 1 pM once combined with NASBA. HAV I trigger RNA could be detected accurately with excellent specificity. In addition, the cell-free toehold switch sensor was verified in HAV I entities. The successful construction of the cell-free toehold switch sensor provided a convenient, rapid, and accurate method for HAV I on-site detection, especially in developing countries, without the involvement of expensive facilities and additional professional operators.
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Affiliation(s)
- Xiaodan Kang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Chen Zhao
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Shuting Chen
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Xi Zhang
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Bin Xue
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Chenyu Li
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Shang Wang
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Xiaobo Yang
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Zhiqiang Xia
- The 908th Hospital of Chinese People's Liberation Army Joint Logistic Support Force, Nanchang, 330000, China
| | - Yongchun Xu
- The 908th Hospital of Chinese People's Liberation Army Joint Logistic Support Force, Nanchang, 330000, China
| | - Yongliang Huang
- The 908th Hospital of Chinese People's Liberation Army Joint Logistic Support Force, Nanchang, 330000, China
| | - Zhigang Qiu
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Chao Li
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Jingfeng Wang
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Jian Pang
- The Air Force Hospital of Northern Theater People's Liberation Army, Shenyang 110042, China.
| | - Zhiqiang Shen
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
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9
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Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
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10
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Zhang C, Liu H, Li X, Xu F, Li Z. Modularized synthetic biology enabled intelligent biosensors. Trends Biotechnol 2023; 41:1055-1065. [PMID: 36967259 DOI: 10.1016/j.tibtech.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 03/29/2023]
Abstract
Biosensors that sense the concentration of a specified target and produce a specific signal output have become important technology for biological analysis. Recently, intelligent biosensors have received great interest due to their adaptability to meet sophisticated demands. Advances in developing standard modules and carriers in synthetic biology have shed light on intelligent biosensors that can implement advanced analytical processing to better accommodate practical applications. This review focuses on intelligent synthetic biology-enabled biosensors (SBBs). First, we illustrate recent progress in intelligent SBBs with the capability of computation, memory storage, and self-calibration. Then, we discuss emerging applications of SBBs in point-of-care testing (POCT) and wearable monitoring. Finally, future perspectives on intelligent SBBs are proposed.
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Affiliation(s)
- Chao Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Hao Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Xiujun Li
- Department of Chemistry and Biochemistry, University of Texas at El Paso, 500 West University Ave, El Paso, TX 79968, USA
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China.
| | - Zedong Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China; TFX Group-Xi'an Jiaotong University Institute of Life Health, Xi'an 710049, P.R. China.
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11
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Choi SR, Lee M. Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. BIOLOGY 2023; 12:1033. [PMID: 37508462 PMCID: PMC10376273 DOI: 10.3390/biology12071033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.
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Affiliation(s)
- Sanghyuk Roy Choi
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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12
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Wong F, de la Fuente-Nunez C, Collins JJ. Leveraging artificial intelligence in the fight against infectious diseases. Science 2023; 381:164-170. [PMID: 37440620 PMCID: PMC10663167 DOI: 10.1126/science.adh1114] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.
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Affiliation(s)
- Felix Wong
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James J. Collins
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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13
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Riley AT, Robson JM, Green AA. Generative and predictive neural networks for the design of functional RNA molecules. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.549043. [PMID: 37503279 PMCID: PMC10370010 DOI: 10.1101/2023.07.14.549043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.
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Affiliation(s)
- Aidan T. Riley
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
| | - James M. Robson
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Alexander A. Green
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
- Molecular Biology, Cell Biology & Biochemistry Program, Graduate School of Arts and Sciences, Boston University, Boston, MA 02215, USA
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14
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Nguyen PQ, Huang X, Collins DS, Collins JJ, Lu T. Harnessing synthetic biology to enhance ocean health. Trends Biotechnol 2023; 41:860-874. [PMID: 36669947 DOI: 10.1016/j.tibtech.2022.12.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/16/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023]
Abstract
Ocean health is faltering, its capability for regeneration and renewal being eroded by a steady pulse of anthropomorphic impacts. Plastic waste has infiltrated all ocean biomes, climate change threatens coral reefs with extinction, and eutrophication has unleashed vast algal blooms. In the face of these challenges, synthetic biology approaches may hold untapped solutions to mitigate adverse effects, repair ecosystems, and put us on a path towards sustainable stewardship of our planet. Leveraging synthetic biology tools would enable innovative engineering approaches to augment the natural adaptive capacity of ocean biological systems to cope with the swiftness of human-induced change. Here, we present a framework for developing synthetic biology solutions for the challenges of plastic pollution, coral bleaching, and harmful algal blooms.
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Affiliation(s)
- Peter Q Nguyen
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Xiaoning Huang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Daniel S Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA; Department of Biology and Nicholas School of the Environment, Duke University, Durham, NC, USA
| | - James J Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Ting Lu
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA; Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA; Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA; National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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15
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Valeri JA, Soenksen LR, Collins KM, Ramesh P, Cai G, Powers R, Angenent-Mari NM, Camacho DM, Wong F, Lu TK, Collins JJ. BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences. Cell Syst 2023; 14:525-542.e9. [PMID: 37348466 PMCID: PMC10700034 DOI: 10.1016/j.cels.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 02/17/2023] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.
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Affiliation(s)
- Jacqueline A Valeri
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Luis R Soenksen
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Katherine M Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Department of Engineering, University of Cambridge, Trumpington St, Cambridge CB2 1PZ, UK
| | - Pradeep Ramesh
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - George Cai
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Rani Powers
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Pluto Biosciences, Golden, CO 80402, USA
| | - Nicolaas M Angenent-Mari
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Diogo M Camacho
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Felix Wong
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Timothy K Lu
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James J Collins
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA; Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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16
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Gyorgy A, Menezes A, Arcak M. A blueprint for a synthetic genetic feedback optimizer. Nat Commun 2023; 14:2554. [PMID: 37137895 PMCID: PMC10156725 DOI: 10.1038/s41467-023-37903-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/05/2023] [Indexed: 05/05/2023] Open
Abstract
Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.
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Affiliation(s)
- Andras Gyorgy
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Amor Menezes
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
| | - Murat Arcak
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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17
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Nikolados EM, Oyarzún DA. Deep learning for optimization of protein expression. Curr Opin Biotechnol 2023; 81:102941. [PMID: 37087839 DOI: 10.1016/j.copbio.2023.102941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/02/2023] [Accepted: 03/17/2023] [Indexed: 04/25/2023]
Abstract
Recent progress in high-throughput DNA synthesis and sequencing has enabled the development of massively parallel reporter assays for strain characterization. These datasets map a large number of DNA sequences to protein expression levels, sparking increased interest in data-driven methods for sequence-to-expression modeling. Here, we highlight advances in deep learning models of protein expression and their potential for optimizing strains engineered to produce recombinant proteins. We review recent works that built highly accurate models and discuss challenges that hinder adoption by end users. There is a need to better align this technology with the constraints encountered in strain engineering, particularly the cost of acquiring large amounts of data and the requirement for interpretable models that generalize beyond the training data. Overcoming these barriers will help to incentivize academic and industrial laboratories to tap into a new era of data-centric strain engineering.
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Affiliation(s)
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK; School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK; The Alan Turing Institute, London NW1 2DB, UK.
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18
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Yarra SS, Ashok G, Mohan U. "Toehold Switches; a foothold for Synthetic Biology". Biotechnol Bioeng 2023; 120:932-952. [PMID: 36527224 DOI: 10.1002/bit.28309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/24/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Toehold switches are de novo designed riboregulators that contain two RNA components interacting through linear-linear RNA interactions, regulating the gene expression. These are highly versatile, exhibit excellent orthogonality, wide dynamic range, and are highly programmable, so can be used for various applications in synthetic biology. In this review, we summarized and discussed the design characteristics and benefits of toehold switch riboregulators over conventional riboregulators. We also discussed applications and recent advancements of toehold switch riboregulators in various fields like gene editing, DNA nanotechnology, translational repression, and diagnostics (detection of microRNAs and some pathogens). Toehold switches, therefore, furnished advancement in synthetic biology applications in various fields with their prominent features.
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Affiliation(s)
- Sai Sumanjali Yarra
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education & Research (NIPER) Kolkata, Kolkata, West Bengal, India
| | - Ganapathy Ashok
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education & Research (NIPER) Kolkata, Kolkata, West Bengal, India
| | - Utpal Mohan
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education & Research (NIPER) Kolkata, Kolkata, West Bengal, India
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19
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O'Connell RW, Rai K, Piepergerdes TC, Samra KD, Wilson JA, Lin S, Zhang TH, Ramos EM, Sun A, Kille B, Curry KD, Rocks JW, Treangen TJ, Mehta P, Bashor CJ. Ultra-high throughput mapping of genetic design space. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532704. [PMID: 36993481 PMCID: PMC10055055 DOI: 10.1101/2023.03.16.532704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 5 drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.
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20
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Autocatalytic base editing for RNA-responsive translational control. Nat Commun 2023; 14:1339. [PMID: 36906659 PMCID: PMC10008589 DOI: 10.1038/s41467-023-36851-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 02/20/2023] [Indexed: 03/13/2023] Open
Abstract
Genetic circuits that control transgene expression in response to pre-defined transcriptional cues would enable the development of smart therapeutics. To this end, here we engineer programmable single-transcript RNA sensors in which adenosine deaminases acting on RNA (ADARs) autocatalytically convert target hybridization into a translational output. Dubbed DART VADAR (Detection and Amplification of RNA Triggers via ADAR), our system amplifies the signal from editing by endogenous ADAR through a positive feedback loop. Amplification is mediated by the expression of a hyperactive, minimal ADAR variant and its recruitment to the edit site via an orthogonal RNA targeting mechanism. This topology confers high dynamic range, low background, minimal off-target effects, and a small genetic footprint. We leverage DART VADAR to detect single nucleotide polymorphisms and modulate translation in response to endogenous transcript levels in mammalian cells.
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21
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An B, Wang Y, Huang Y, Wang X, Liu Y, Xun D, Church GM, Dai Z, Yi X, Tang TC, Zhong C. Engineered Living Materials For Sustainability. Chem Rev 2023; 123:2349-2419. [PMID: 36512650 DOI: 10.1021/acs.chemrev.2c00512] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Finally, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.
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Affiliation(s)
- Bolin An
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yanyi Wang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuanyuan Huang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xinyu Wang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuzhu Liu
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Dongmin Xun
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - George M Church
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston 02115, Massachusetts United States.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston 02115, Massachusetts United States
| | - Zhuojun Dai
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiao Yi
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tzu-Chieh Tang
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston 02115, Massachusetts United States.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston 02115, Massachusetts United States
| | - Chao Zhong
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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22
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Zolaktaf S, Dannenberg F, Schmidt M, Condon A, Winfree E. Predicting DNA kinetics with a truncated continuous-time Markov chain method. Comput Biol Chem 2023; 104:107837. [PMID: 36858009 DOI: 10.1016/j.compbiolchem.2023.107837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/05/2023] [Accepted: 02/21/2023] [Indexed: 03/03/2023]
Abstract
Predicting the kinetics of reactions involving nucleic acid strands is a fundamental task in biology and biotechnology. Reaction kinetics can be modeled as an elementary step continuous-time Markov chain, where states correspond to secondary structures and transitions correspond to base pair formation and breakage. Since the number of states in the Markov chain could be large, rates are determined by estimating the mean first passage time from sampled trajectories. As a result, the cost of kinetic predictions becomes prohibitively expensive for rare events with extremely long trajectories. Also problematic are scenarios where multiple predictions are needed for the same reaction, e.g., under different environmental conditions, or when calibrating model parameters, because a new set of trajectories is needed multiple times. We propose a new method, called pathway elaboration, to handle these scenarios. Pathway elaboration builds a truncated continuous-time Markov chain through both biased and unbiased sampling. The resulting Markov chain has moderate state space size, so matrix methods can efficiently compute reaction rates, even for rare events. Also the transition rates of the truncated Markov chain can easily be adapted when model or environmental parameters are perturbed, making model calibration feasible. We illustrate the utility of pathway elaboration on toehold-mediated strand displacement reactions, show that it well-approximates trajectory-based predictions of unbiased elementary step models on a wide range of reaction types for which such predictions are feasible, and demonstrate that it performs better than alternative truncation-based approaches that are applicable for mean first passage time estimation. Finally, in a small study, we use pathway elaboration to optimize the Metropolis kinetic model of Multistrand, an elementary step simulator, showing that the optimized parameters greatly improve reaction rate predictions. Our framework and dataset are available at https://github.com/DNA-and-Natural-Algorithms-Group/PathwayElaboration.
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Affiliation(s)
| | | | - Mark Schmidt
- University of British Columbia, Canada; Canada CIFAR AI Chair (Amii), Canada.
| | | | - Erik Winfree
- California Institute of Technology, United States of America.
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23
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Hughes AC, Pollard BG, Xu B, Gammons JW, Chapman P, Bikoff JB, Schwarz LA. A Novel Single Vector Intersectional AAV Strategy for Interrogating Cellular Diversity and Brain Function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.07.527312. [PMID: 36798174 PMCID: PMC9934562 DOI: 10.1101/2023.02.07.527312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
As the discovery of cellular diversity in the brain accelerates, so does the need for functional tools that target cells based on multiple features, such as gene expression and projection target. By selectively driving recombinase expression in a feature-specific manner, one can utilize intersectional strategies to conditionally promote payload expression only where multiple features overlap. We developed Conditional Viral Expression by Ribozyme Guided Degradation (ConVERGD), a single-construct intersectional targeting strategy that combines a self-cleaving ribozyme with traditional FLEx switches. ConVERGD offers benefits over existing platforms, such as expanded intersectionality, the ability to accommodate larger and more complex payloads, and a vector design that is easily modified to better facilitate rapid toolkit expansion. To demonstrate its utility for interrogating neural circuitry, we employed ConVERGD to target an unexplored subpopulation of norepinephrine (NE)-producing neurons within the rodent locus coeruleus (LC) identified via single-cell transcriptomic profiling to co-express the stress-related endogenous opioid gene prodynorphin (Pdyn). These studies showcase ConVERGD as a versatile tool for targeting diverse cell types and reveal Pdyn-expressing NE+ LC neurons as a small neuronal subpopulation capable of driving anxiogenic behavioral responses in rodents.
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Affiliation(s)
- Alex C. Hughes
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, 38105
| | - Brittany G. Pollard
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, 38105
| | - Beisi Xu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, 38105
| | - Jesse W. Gammons
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, 38105
- Present address: Department of Pediatrics, Stanford University, Stanford, CA, 94305
| | - Phillip Chapman
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, 38105
| | - Jay B. Bikoff
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, 38105
| | - Lindsay A. Schwarz
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, 38105
- Lead contact
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24
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Wang T, Hellmer H, Simmel FC. Genetic switches based on nucleic acid strand displacement. Curr Opin Biotechnol 2023; 79:102867. [PMID: 36535150 DOI: 10.1016/j.copbio.2022.102867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/15/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
Toehold-mediated strand displacement (TMSD) is an isothermal switching process that enables the sequence-programmable and reversible conversion of DNA or RNA strands between single- and double-stranded conformations or other secondary structures. TMSD processes have already found widespread application in DNA nanotechnology, where they are used to drive DNA-based molecular devices or for the realization of synthetic biochemical computing circuits. Recently, researchers have started to employ TMSD also for the control of RNA-based gene regulatory processes in vivo, in particular in the context of synthetic riboregulators and conditional guide RNAs for CRISPR/Cas. Here, we provide a review over recent developments in this emerging field and discuss the opportunities and challenges for such systems in in vivo applications.
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Affiliation(s)
- Tianhe Wang
- Physics of Synthetic Biological Systems - E14, Physics Department and ZNN, Technische Universität München, Am Coulombwall 4a, 85748 Garching, Germany
| | - Henning Hellmer
- Physics of Synthetic Biological Systems - E14, Physics Department and ZNN, Technische Universität München, Am Coulombwall 4a, 85748 Garching, Germany
| | - Friedrich C Simmel
- Physics of Synthetic Biological Systems - E14, Physics Department and ZNN, Technische Universität München, Am Coulombwall 4a, 85748 Garching, Germany.
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25
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Updated toolkits for nucleic acid-based biosensors. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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26
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Tack DS, Tonner PD, Pressman A, Olson ND, Levy SF, Romantseva EF, Alperovich N, Vasilyeva O, Ross D. Precision engineering of biological function with large-scale measurements and machine learning. PLoS One 2023; 18:e0283548. [PMID: 36989327 PMCID: PMC10057847 DOI: 10.1371/journal.pone.0283548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/11/2023] [Indexed: 03/30/2023] Open
Abstract
As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.
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Affiliation(s)
- Drew S Tack
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Peter D Tonner
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Abe Pressman
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nathan D Olson
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, United States of America
- Joint Initiative for Metrology in Biology, Stanford, CA, United States of America
| | - Eugenia F Romantseva
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Olga Vasilyeva
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
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27
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Simmel FC. Nucleic acid strand displacement - from DNA nanotechnology to translational regulation. RNA Biol 2023; 20:154-163. [PMID: 37095744 PMCID: PMC10132225 DOI: 10.1080/15476286.2023.2204565] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Nucleic acid strand displacement reactions involve the competition of two or more DNA or RNA strands of similar sequence for binding to a complementary strand, and facilitate the isothermal replacement of an incumbent strand by an invader. The process can be biased by augmenting the duplex comprising the incumbent with a single-stranded extension, which can act as a toehold for a complementary invader. The toehold gives the invader a thermodynamic advantage over the incumbent, and can be programmed as a unique label to activate a specific strand displacement process. Toehold-mediated strand displacement processes have been extensively utilized for the operation of DNA-based molecular machines and devices as well as for the design of DNA-based chemical reaction networks. More recently, principles developed initially in the context of DNA nanotechnology have been applied for the de novo design of gene regulatory switches that can operate inside living cells. The article specifically focuses on the design of RNA-based translational regulators termed toehold switches. Toehold switches utilize toehold-mediated strand invasion to either activate or repress translation of an mRNA in response to the binding of a trigger RNA molecule. The basic operation principles of toehold switches will be discussed as well as their applications in sensing and biocomputing. Finally, strategies for their optimization will be described as well as challenges for their operation in vivo.
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Affiliation(s)
- Friedrich C Simmel
- TU Munich, School of Natural Sciences, Department of Bioscience, Garching, Germany
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28
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Gilliot PA, Gorochowski TE. Design and Analysis of Massively Parallel Reporter Assays Using FORECAST. Methods Mol Biol 2023; 2553:41-56. [PMID: 36227538 DOI: 10.1007/978-1-0716-2617-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Machine learning is revolutionizing molecular biology and bioengineering by providing powerful insights and predictions. Massively parallel reporter assays (MPRAs) have emerged as a particularly valuable class of high-throughput technique to support such algorithms. MPRAs enable the simultaneous characterization of thousands or even millions of genetic constructs and provide the large amounts of data needed to train models. However, while the scale of this approach is impressive, the design of effective MPRA experiments is challenging due to the many factors that can be varied and the difficulty in predicting how these will impact the quality and quantity of data obtained. Here, we present a computational tool called FORECAST, which can simulate MPRA experiments based on fluorescence-activated cell sorting and subsequent sequencing (commonly referred to as Flow-seq or Sort-seq experiments), as well as carry out rigorous statistical estimation of construct performance from this type of experimental data. FORECAST can be used to develop workflows to aid the design of MPRA experiments and reanalyze existing MPRA data sets.
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29
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Yu W, Xu X, Jin K, Liu Y, Li J, Du G, Lv X, Liu L. Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications. Biotechnol Adv 2023; 62:108077. [PMID: 36502964 DOI: 10.1016/j.biotechadv.2022.108077] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.
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Affiliation(s)
- Wenwen Yu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xianhao Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Ke Jin
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
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30
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Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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31
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Accuracy and data efficiency in deep learning models of protein expression. Nat Commun 2022; 13:7755. [PMID: 36517468 PMCID: PMC9751117 DOI: 10.1038/s41467-022-34902-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.
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32
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Minkner R, Boonyakida J, Park EY, Wätzig H. Oligonucleotide separation techniques for purification and analysis: What can we learn for today's tasks? Electrophoresis 2022; 43:2402-2427. [PMID: 36285667 DOI: 10.1002/elps.202200079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 09/09/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022]
Abstract
Nucleic acids are the blueprint of life. They are not only the construction plan of the single cell or higher associations of them, but also necessary for function, communication and regulation. Due to the pandemic, the attention shifted in particular to their therapeutic potential as a vaccine. As pharmaceutical oligonucleotides are unique in terms of their stability and application, special delivery systems were also considered. Oligonucleotide production systems can vary and depend on the feasibility, availability, price and intended application. To achieve good purity, reliable results and match the strict specifications in the pharmaceutical industry, the separation of oligonucleotides is always essential. Besides the separation required for production, additional and specifically different separation techniques are needed for analysis to determine if the product complies with the designated specifications. After a short introduction to ribonucleic acids (RNAs), messenger RNA vaccines, and their production and delivery systems, an overview regarding separation techniques will be provided. This not only emphasises electrophoretic separations but also includes spin columns, extractions, precipitations, magnetic nanoparticles and several chromatographic separation principles, such as ion exchange chromatography, ion-pair reversed-phase, size exclusion and affinity.
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Affiliation(s)
- Robert Minkner
- Institute of Medicinal and Pharmaceutical Chemistry, Technische Universität Braunschweig, Braunschweig, Germany
| | - Jirayu Boonyakida
- Department of Bioscience, Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan.,Laboratory of Biotechnology, Green Chemistry Research Division, Research Institute of Green Science and Technology, Shizuoka University, Shizuoka, Japan
| | - Enoch Y Park
- Department of Bioscience, Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan.,Laboratory of Biotechnology, Green Chemistry Research Division, Research Institute of Green Science and Technology, Shizuoka University, Shizuoka, Japan
| | - Hermann Wätzig
- Institute of Medicinal and Pharmaceutical Chemistry, Technische Universität Braunschweig, Braunschweig, Germany
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Chandler M, Jain S, Halman J, Hong E, Dobrovolskaia MA, Zakharov AV, Afonin KA. Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204941. [PMID: 36216772 PMCID: PMC9671856 DOI: 10.1002/smll.202204941] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.
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Affiliation(s)
- Morgan Chandler
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Sankalp Jain
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Justin Halman
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Enping Hong
- Nanotechnology Characterization Lab, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Marina A. Dobrovolskaia
- Nanotechnology Characterization Lab, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Kirill A. Afonin
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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34
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Toehold-mediated biosensors: Types, mechanisms and biosensing strategies. Biosens Bioelectron 2022; 220:114922. [DOI: 10.1016/j.bios.2022.114922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022]
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35
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Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S, Bu D, Zhao Y. Multi-modality artificial intelligence in digital pathology. Brief Bioinform 2022; 23:6702380. [PMID: 36124675 PMCID: PMC9677480 DOI: 10.1093/bib/bbac367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/27/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022] Open
Abstract
In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.
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Affiliation(s)
- Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lianhe Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
| | - Chunlong Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shengtong Li
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yi Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
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36
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Wang Y, Liu Y, Li J, Chen Y, Liu S, Zhong C. Engineered living materials (ELMs) design: From function allocation to dynamic behavior modulation. Curr Opin Chem Biol 2022; 70:102188. [PMID: 35970133 DOI: 10.1016/j.cbpa.2022.102188] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/14/2022] [Accepted: 07/05/2022] [Indexed: 11/17/2022]
Abstract
Natural materials possess many distinctive "living" attributes, such as self-growth, self-healing, environmental responsiveness, and evolvability, that are beyond the reach of many existing synthetic materials. The emerging field of engineered living materials (ELMs) takes inspiration from nature and harnesses engineered living systems to produce dynamic and responsive materials with genetically programmable functionalities. Here, we identify and review two main directions for the rational design of ELMs: first, engineering of living materials with enhanced performances by incorporating functional material modules, including engineered biological building blocks (proteins, polysaccharides, and nucleic acids) or well-defined artificial materials; second, engineering of smart ELMs that can sense and respond to their surroundings by programming dynamic cellular behaviors regulated via cell-cell or cell-environment interactions. We next discuss the strengths and challenges of current ELMs and conclude by providing a perspective of future directions in this promising area.
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Affiliation(s)
- Yanyi Wang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Cas Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yi Liu
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Cas Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Jing Li
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Cas Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yue Chen
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Cas Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Sizhe Liu
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Cas Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China
| | - Chao Zhong
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Cas Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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37
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Beardall WA, Stan GB, Dunlop MJ. Deep Learning Concepts and Applications for Synthetic Biology. GEN BIOTECHNOLOGY 2022; 1:360-371. [PMID: 36061221 PMCID: PMC9428732 DOI: 10.1089/genbio.2022.0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/14/2022] [Indexed: 12/24/2022]
Abstract
Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
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Affiliation(s)
- William A.V. Beardall
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Mary J. Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
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38
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Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. BIOSENSORS 2022; 12:bios12080562. [PMID: 35892459 PMCID: PMC9330886 DOI: 10.3390/bios12080562] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.
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Affiliation(s)
- Pandiaraj Manickam
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Correspondence:
| | - Siva Ananth Mariappan
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
| | - Sindhu Monica Murugesan
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
| | - Shekhar Hansda
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India
| | - Ajeet Kaushik
- School of Engineering, University of Petroleum and Energy Studies (UPES), Dehradun 248001, Uttarakhand, India;
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805-8531, USA
| | - Ravikumar Shinde
- Department of Zoology, Shri Pundlik Maharaj Mahavidyalaya Nandura, Buldana 443404, Maharashtra, India;
| | - S. P. Thipperudraswamy
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Central Instrument Facility, CSIR-Central Electrochemical Research Institute, Karaikudi, Sivagangai 630003, Tamil Nadu, India
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39
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Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power. Proc Natl Acad Sci U S A 2022; 119:e2114021119. [PMID: 35733251 PMCID: PMC9245639 DOI: 10.1073/pnas.2114021119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (landscape interpretable nonparametric model, https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights of the landscape, including its inherent dimensionality, a latent space of additive mutational effects, and metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic space.
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40
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Robson JM, Green AA. Closing the loop on crowdsourced science. Proc Natl Acad Sci U S A 2022; 119:e2205897119. [PMID: 35687665 PMCID: PMC9231617 DOI: 10.1073/pnas.2205897119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- James M. Robson
- Department of Biomedical Engineering, Boston University, Boston, MA 02215
- Biological Design Center, Boston University, Boston, MA 02215
| | - Alexander A. Green
- Department of Biomedical Engineering, Boston University, Boston, MA 02215
- Biological Design Center, Boston University, Boston, MA 02215
- Molecular Biology, Cell Biology & Biochemistry Program, Graduate School of Arts and Sciences, Boston University, Boston, MA 02215
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41
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Engineering Toehold-Mediated Switches for Native RNA Detection and Regulation in Bacteria. J Mol Biol 2022; 434:167689. [PMID: 35717997 DOI: 10.1016/j.jmb.2022.167689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/19/2022] [Accepted: 06/09/2022] [Indexed: 01/24/2023]
Abstract
RNA switches are versatile tools in synthetic biology for sensing and regulation applications. The discoveries of RNA-mediated translational and transcriptional control have facilitated the development of complexde novodesigns of RNA switches. Specifically, RNA toehold-mediated switches, in which binding to the toehold sensing domain controls the transition between switch states via strand displacement, have been extensively adapted for coupling systems responses to specifictrans-RNA inputs. This review highlights some of the challenges associated with applying these switches for native RNA detectionin vivo, including transferability between organisms. The applicability and design considerations of toehold-mediated switches are discussed by highlighting twelve recently developed switch designs. This review finishes with future perspectives to address current gaps in the field, particularly regarding the power of structural prediction algorithms for improved in vivo functionality of RNA switches.
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42
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Soudier P, Rodriguez Pinzon D, Reif-Trauttmansdorff T, Hijazi H, Cherrière M, Goncalves Pereira C, Blaise D, Pispisa M, Saint-Julien A, Hamlet W, Nguevo M, Gomes E, Belkhelfa S, Niarakis A, Kushwaha M, Grigoras I. Toehold switch based biosensors for sensing the highly trafficked rosewood Dalbergia maritima. Synth Syst Biotechnol 2022; 7:791-801. [PMID: 35415278 PMCID: PMC8976095 DOI: 10.1016/j.synbio.2022.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 02/28/2022] [Accepted: 03/14/2022] [Indexed: 01/05/2023] Open
Abstract
Nucleic acid sensing is a 3 decades old but still challenging area of application for different biological sub-domains, from pathogen detection to single cell transcriptomics analysis. The many applications of nucleic acid detection and identification are mostly carried out by PCR techniques, sequencing, and their derivatives used at large scale. However, these methods’ limitations on speed, cost, complexity and specificity have motivated the development of innovative detection methods among which nucleic acid biosensing technologies seem promising. Toehold switches are a particular class of RNA sensing devices relying on a conformational switch of secondary structure induced by the pairing of the detected trigger RNA with a de novo designed synthetic sensing mRNA molecule. Here we describe a streamlined methodology enabling the development of such a sensor for the RNA-mediated detection of an endangered plant species in a cell-free reaction system. We applied this methodology to help identify the rosewood Dalbergia maritima, a highly trafficked wood, whose protection is limited by the capacity of the authorities to distinguish protected logs from other unprotected but related species. The streamlined pipeline presented in this work is a versatile framework enabling cheap and rapid development of new sensors for custom RNA detection.
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43
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Baabu PRS, Srinivasan S, Nagarajan S, Muthamilselvan S, Selvi T, Suresh RR, Palaniappan A. End-to-end computational approach to the design of RNA biosensors for detecting miRNA biomarkers of cervical cancer. Synth Syst Biotechnol 2022; 7:802-814. [PMID: 35475253 PMCID: PMC9014444 DOI: 10.1016/j.synbio.2022.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 02/25/2022] [Accepted: 03/23/2022] [Indexed: 12/18/2022] Open
Abstract
Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Relatively scant awareness and limited access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative unified pipeline for diagnosis has been developed, beginning with identification of optimal biomarkers, concurrent design of toehold switch sensors, and finally simulation of the designed diagnostic circuits to assess performance. Using miRNA expression data in the public domain, we identified miR-21–5p and miR-20a-5p as blood-based miRNA biomarkers specific to early-stage cervical cancer employing a multi-tier algorithmic screening. Synthetic riboregulators called toehold switches specific to the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we used a generic grammar of these switches, and built a neural network model of dynamic range using thermodynamic features derived from mRNA secondary structure and interaction. Second-generation toehold switches were used to overcome the design challenges associated with miRNA biomarkers. The resultant model yielded an adj. R2 ∼0.71, outperforming earlier models of toehold-switch dynamic range. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10 nM and 100 nM before saturation. Our study demonstrates an end-to-end computational workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic/nucleic-acid signatures. The approach has the potential to replace iterative experimental trial and error, and focus time, money, and efforts. All software including the toehold grammar parser, neural network model and reaction kinetics simulation are available as open-source software (https://github.com/SASTRA-iGEM2019) under GNU GPLv3 licence.
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44
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Yu H, Qi Y, Ding Y. Deep Learning in RNA Structure Studies. Front Mol Biosci 2022; 9:869601. [PMID: 35677883 PMCID: PMC9168262 DOI: 10.3389/fmolb.2022.869601] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.
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Affiliation(s)
- Haopeng Yu
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, United Kingdom
| | | | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, United Kingdom
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45
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Ruiz Puentes P, Rueda-Gensini L, Valderrama N, Hernández I, González C, Daza L, Muñoz-Camargo C, Cruz JC, Arbeláez P. Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery. Sci Rep 2022; 12:8434. [PMID: 35589824 PMCID: PMC9119967 DOI: 10.1038/s41598-022-12180-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/05/2022] [Indexed: 02/08/2023] Open
Abstract
Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein–ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target–ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands’ and targets’ most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand–target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.
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Affiliation(s)
- Paola Ruiz Puentes
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Laura Rueda-Gensini
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Natalia Valderrama
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Isabela Hernández
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Cristina González
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Laura Daza
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Carolina Muñoz-Camargo
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Juan C Cruz
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Pablo Arbeláez
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia. .,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia.
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46
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Wang T, Simmel FC. Riboswitch-inspired toehold riboregulators for gene regulation in Escherichia coli. Nucleic Acids Res 2022; 50:4784-4798. [PMID: 35446427 PMCID: PMC9071393 DOI: 10.1093/nar/gkac275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/08/2022] [Indexed: 12/24/2022] Open
Abstract
Regulatory RNA molecules have been widely investigated as components for synthetic gene circuits, complementing the use of protein-based transcription factors. Among the potential advantages of RNA-based gene regulators are their comparatively simple design, sequence-programmability, orthogonality, and their relatively low metabolic burden. In this work, we developed a set of riboswitch-inspired riboregulators in Escherichia coli that combine the concept of toehold-mediated strand displacement (TMSD) with the switching principles of naturally occurring transcriptional and translational riboswitches. Specifically, for translational activation and repression, we sequestered anti-anti-RBS or anti-RBS sequences, respectively, inside the loop of a stable hairpin domain, which is equipped with a single-stranded toehold region at its 5' end and is followed by regulated sequences on its 3' side. A trigger RNA binding to the toehold region can invade the hairpin, inducing a structural rearrangement that results in translational activation or deactivation. We also demonstrate that TMSD can be applied in the context of transcriptional regulation by switching RNA secondary structure involved in Rho-dependent termination. Our designs expand the repertoire of available synthetic riboregulators by a set of RNA switches with no sequence limitation, which should prove useful for the development of robust genetic sensors and circuits.
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Affiliation(s)
- Tianhe Wang
- Physics of Synthetic Biological Systems – E14, Physics Department and ZNN, Technische Universität München, Am Coulombwall 4a, 85748 Garching, Germany
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47
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Batista AC, Levrier A, Soudier P, Voyvodic PL, Achmedov T, Reif-Trauttmansdorff T, DeVisch A, Cohen-Gonsaud M, Faulon JL, Beisel CL, Bonnet J, Kushwaha M. Differentially Optimized Cell-Free Buffer Enables Robust Expression from Unprotected Linear DNA in Exonuclease-Deficient Extracts. ACS Synth Biol 2022; 11:732-746. [PMID: 35034449 DOI: 10.1021/acssynbio.1c00448] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The use of linear DNA templates in cell-free systems promises to accelerate the prototyping and engineering of synthetic gene circuits. A key challenge is that linear templates are rapidly degraded by exonucleases present in cell extracts. Current approaches tackle the problem by adding exonuclease inhibitors and DNA-binding proteins to protect the linear DNA, requiring additional time- and resource-intensive steps. Here, we delete the recBCD exonuclease gene cluster from the Escherichia coli BL21 genome. We show that the resulting cell-free systems, with buffers optimized specifically for linear DNA, enable near-plasmid levels of expression from σ70 promoters in linear DNA templates without employing additional protection strategies. When using linear or plasmid DNA templates at the buffer calibration step, the optimal potassium glutamate concentrations obtained when using linear DNA were consistently lower than those obtained when using plasmid DNA for the same extract. We demonstrate the robustness of the exonuclease deficient extracts across seven different batches and a wide range of experimental conditions across two different laboratories. Finally, we illustrate the use of the ΔrecBCD extracts for two applications: toehold switch characterization and enzyme screening. Our work provides a simple, efficient, and cost-effective solution for using linear DNA templates in cell-free systems and highlights the importance of specifically tailoring buffer composition for the final experimental setup. Our data also suggest that similar exonuclease deletion strategies can be applied to other species suitable for cell-free synthetic biology.
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Affiliation(s)
- Angelo Cardoso Batista
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Antoine Levrier
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Paul Soudier
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Peter L. Voyvodic
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Tatjana Achmedov
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Centre for Infection Research (HZI), 97080 Würzburg, Germany
| | | | - Angelique DeVisch
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Martin Cohen-Gonsaud
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Jean-Loup Faulon
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Chase L. Beisel
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Centre for Infection Research (HZI), 97080 Würzburg, Germany
- Medical Faculty, University of Würzburg, 97080 Würzburg, Germany
| | - Jerome Bonnet
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Manish Kushwaha
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
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48
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Wang C, Zhang W, Tian R, Zhang J, Zhang L, Deng Z, Lv X, Li J, Liu L, Du G, Liu Y. Model‐driven design of synthetic N‐terminal coding sequences for regulating gene expression in yeast and bacteria. Biotechnol J 2022; 17:e2100655. [DOI: 10.1002/biot.202100655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/12/2022]
Affiliation(s)
- Chenyun Wang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Wei Zhang
- School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China
- Jiangsu Key Laboratory of Media Design and Software Technology Wuxi 214122 China
| | - Rongzhen Tian
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Jianing Zhang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Linpei Zhang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
| | - Zhaohong Deng
- School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China
- Jiangsu Key Laboratory of Media Design and Software Technology Wuxi 214122 China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology Jiangnan University Wuxi 214122 China
- Science Center for Future Foods Jiangnan University Wuxi 214122 China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology Jiangnan University Wuxi 214122 China
- Qingdao Special Food Research Institute Wuxi 214122 China
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49
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Detection of pks Island mRNAs Using Toehold Sensors in Escherichia coli. Life (Basel) 2021; 11:life11111280. [PMID: 34833155 PMCID: PMC8625898 DOI: 10.3390/life11111280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/15/2021] [Accepted: 11/18/2021] [Indexed: 12/14/2022] Open
Abstract
Synthetic biologists have applied biomolecular engineering approaches toward the goal of novel biological devices and have shown progress in diverse areas of medicine and biotechnology. Especially promising is the application of synthetic biological devices towards a novel class of molecular diagnostics. As an example, a de-novo-designed riboregulator called toehold switch, with its programmability and compatibility with field-deployable devices showed promising in vitro applications for viral RNA detection such as Zika and Corona viruses. However, the in vivo application of high-performance RNA sensors remains challenging due to the secondary structure of long mRNA species. Here, we introduced ‘Helper RNAs’ that can enhance the functionality of toehold switch sensors by mitigating the effect of secondary structures around a target site. By employing the helper RNAs, previously reported mCherry mRNA sensor showed improved fold-changes in vivo. To further generalize the Helper RNA approaches, we employed automatic design pipeline for toehold sensors that target the essential genes within the pks island, an important target of biomedical research in connection with colorectal cancer. The toehold switch sensors showed fold-changes upon the expression of full-length mRNAs that apparently depended sensitively on the identity of the gene as well as the predicted local structure within the target region of the mRNA. Still, the helper RNAs could improve the performance of toehold switch sensors in many instances, with up to 10-fold improvement over no helper cases. These results suggest that the helper RNA approaches can further assist the design of functional RNA devices in vivo with the aid of the streamlined automatic design software developed here. Further, our solutions for screening and stabilizing single-stranded region of mRNA may find use in other in vivo mRNA-sensing applications such as cas13 crRNA design, transcriptome engineering, and trans-cleaving ribozymes.
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50
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Phan NN, Hsu CY, Huang CC, Tseng LM, Chuang EY. Prediction of Breast Cancer Recurrence Using a Deep Convolutional Neural Network Without Region-of-Interest Labeling. Front Oncol 2021; 11:734015. [PMID: 34745954 PMCID: PMC8567097 DOI: 10.3389/fonc.2021.734015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/29/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose The present study aimed to assign a risk score for breast cancer recurrence based on pathological whole slide images (WSIs) using a deep learning model. Methods A total of 233 WSIs from 138 breast cancer patients were assigned either a low-risk or a high-risk score based on a 70-gene signature. These images were processed into patches of 512x512 pixels by the PyHIST tool and underwent color normalization using the Macenko method. Afterward, out of focus and pixelated patches were removed using the Laplacian algorithm. Finally, the remaining patches (n=294,562) were split into 3 parts for model training (50%), validation (7%) and testing (43%). We used 6 pretrained models for transfer learning and evaluated their performance using accuracy, precision, recall, F1 score, confusion matrix, and AUC. Additionally, to demonstrate the robustness of the final model and its generalization capacity, the testing set was used for model evaluation. Finally, the GRAD-CAM algorithm was used for model visualization. Results Six models, namely VGG16, ResNet50, ResNet101, Inception_ResNet, EfficientB5, and Xception, achieved high performance in the validation set with an overall accuracy of 0.84, 0.85, 0.83, 0.84, 0.87, and 0.91, respectively. We selected Xception for assessment of the testing set, and this model achieved an overall accuracy of 0.87 with a patch-wise approach and 0.90 and 1.00 with a patient-wise approach for high-risk and low-risk groups, respectively. Conclusions Our study demonstrated the feasibility and high performance of artificial intelligence models trained without region-of-interest labeling for predicting cancer recurrence based on a 70-gene signature risk score.
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Affiliation(s)
- Nam Nhut Phan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.,Bioinformatics and Biostatistics Core, Centre of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
| | - Chih-Yi Hsu
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,College of Nursing, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chi-Cheng Huang
- Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan.,Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ling-Ming Tseng
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Eric Y Chuang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.,Bioinformatics and Biostatistics Core, Centre of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan.,Master Program for Biomedical Engineering, China Medical University, Taichung, Taiwan
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