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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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2
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Yang S, Bögels BWA, Wang F, Xu C, Dou H, Mann S, Fan C, de Greef TFA. DNA as a universal chemical substrate for computing and data storage. Nat Rev Chem 2024; 8:179-194. [PMID: 38337008 DOI: 10.1038/s41570-024-00576-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/12/2024]
Abstract
DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.
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Affiliation(s)
- Shuo Yang
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China
| | - Bas W A Bögels
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Fei Wang
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Can Xu
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China
| | - Hongjing Dou
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China
| | - Stephen Mann
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China.
- Centre for Protolife Research and Centre for Organized Matter Chemistry, School of Chemistry, University of Bristol, Bristol, UK.
- Max Planck-Bristol Centre for Minimal Biology, School of Chemistry, University of Bristol, Bristol, UK.
| | - Chunhai Fan
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands.
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, Utrecht, The Netherlands.
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Halužan Vasle A, Moškon M. Synthetic biological neural networks: From current implementations to future perspectives. Biosystems 2024; 237:105164. [PMID: 38402944 DOI: 10.1016/j.biosystems.2024.105164] [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: 06/14/2023] [Revised: 01/03/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.
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Affiliation(s)
- Ana Halužan Vasle
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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Stano P. Chemical Systems for Wetware Artificial Life: Selected Perspectives in Synthetic Cell Research. Int J Mol Sci 2023; 24:14138. [PMID: 37762444 PMCID: PMC10532297 DOI: 10.3390/ijms241814138] [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: 08/11/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called "synthetic cells" (SCs) (or "artificial cells", or "protocells"), lead us to consider the role of wetware technologies in the "Sciences of Artificial", where they constitute the third pillar, alongside the more well-known pillars hardware (robotics) and software (Artificial Intelligence, AI). In this article, it will be highlighted how wetware approaches can help to model life and cognition from a unique perspective, complementary to robotics and AI. It is suggested that, through SB, it is possible to explore novel forms of bio-inspired technologies and systems, in particular chemical AI. Furthermore, attention is paid to the concept of semantic information and its quantification, following the strategy recently introduced by Kolchinsky and Wolpert. Semantic information, in turn, is linked to the processes of generation of "meaning", interpreted here through the lens of autonomy and cognition in artificial systems, emphasizing its role in chemical ones.
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Affiliation(s)
- Pasquale Stano
- Department of Biological and Environmental Sciences and Technologies (DiSTeBA), University of Salento, 73100 Lecce, Italy
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Kieffer C, Genot AJ, Rondelez Y, Gines G. Molecular Computation for Molecular Classification. Adv Biol (Weinh) 2023; 7:e2200203. [PMID: 36709492 DOI: 10.1002/adbi.202200203] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/28/2022] [Indexed: 01/30/2023]
Abstract
DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.
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Affiliation(s)
- Coline Kieffer
- Laboratoire Gulliver, UMR 7083, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin, Paris, 75005, France
| | - Anthony J Genot
- LIMMS, CNRS-Institute of Industrial Science, IRL 2820, University of Tokyo, Tokyo, 153-8505, Japan
| | - Yannick Rondelez
- Laboratoire Gulliver, UMR 7083, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin, Paris, 75005, France
| | - Guillaume Gines
- Laboratoire Gulliver, UMR 7083, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin, Paris, 75005, France
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Moškon M, Pušnik Ž, Stanovnik L, Zimic N, Mraz M. A computational design of a programmable biological processor. Biosystems 2022; 221:104778. [PMID: 36099979 DOI: 10.1016/j.biosystems.2022.104778] [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: 06/02/2022] [Revised: 08/02/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.
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Affiliation(s)
- Miha Moškon
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia.
| | - Žiga Pušnik
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Lidija Stanovnik
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Nikolaj Zimic
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Mraz
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
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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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Moškon M, Mraz M. Programmable evolution of computing circuits in cellular populations. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07532-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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