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Alexis E, Schulte CCM, Cardelli L, Papachristodoulou A. Regulation strategies for two-output biomolecular networks. J R Soc Interface 2023; 20:20230174. [PMID: 37528680 PMCID: PMC10394417 DOI: 10.1098/rsif.2023.0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023] Open
Abstract
Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.
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Affiliation(s)
- Emmanouil Alexis
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Carolin C. M. Schulte
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Luca Cardelli
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
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2
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Sharon JA, Dasrath C, Fujiwara A, Snyder A, Blank M, O'Brien S, Aufdembrink LM, Engelhart AE, Adamala KP. Trumpet is an operating system for simple and robust cell-free biocomputing. Nat Commun 2023; 14:2257. [PMID: 37080970 PMCID: PMC10119096 DOI: 10.1038/s41467-023-37752-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/30/2023] [Indexed: 04/22/2023] Open
Abstract
Biological computation is becoming a viable and fast-growing alternative to traditional electronic computing. Here we present a biocomputing technology called Trumpet: Transcriptional RNA Universal Multi-Purpose GatE PlaTform. Trumpet combines the simplicity and robustness of the simplest in vitro biocomputing methods, adding signal amplification and programmability, while avoiding common shortcomings of live cell-based biocomputing solutions. We have demonstrated the use of Trumpet to build all universal Boolean logic gates. We have also built a web-based platform for designing Trumpet gates and created a primitive processor by networking several gates as a proof-of-principle for future development. The Trumpet offers a change of paradigm in biocomputing, providing an efficient and easily programmable biological logic gate operating system.
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Affiliation(s)
- Judee A Sharon
- Department of Genetics, Cellular Biology, and Development, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Chelsea Dasrath
- Department of Genetics, Cellular Biology, and Development, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Aiden Fujiwara
- Department of Computer Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Alessandro Snyder
- Department of Computer Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Mace Blank
- Department of Genetics, Cellular Biology, and Development, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Sam O'Brien
- Department of Computer Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Lauren M Aufdembrink
- Department of Genetics, Cellular Biology, and Development, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Aaron E Engelhart
- Department of Genetics, Cellular Biology, and Development, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Katarzyna P Adamala
- Department of Genetics, Cellular Biology, and Development, University of Minnesota, Twin Cities, Minneapolis, MN, USA.
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Srivastava R, Bagh S. A Logically Reversible Double Feynman Gate with Molecular Engineered Bacteria Arranged in an Artificial Neural Network-Type Architecture. ACS Synth Biol 2023; 12:51-60. [PMID: 36384003 DOI: 10.1021/acssynbio.2c00520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Reversible logic gates are the key components of reversible computing that map inputs and outputs in a certain one-to-one pattern so that the output signals can reveal the pattern of the input signals. One of the main research foci of reversible computing is the implementation of basic reversible gates by various modalities. Though true thermodynamic reversibility cannot be attained within living cells, the high energy efficiency of biological reactions inspires the implementation of reversible computation in living cells. The implementation of synthetic genetic circuits is mostly based on conventional irreversible computing, and the implementation of logical reversibility in living cells is rare. Here, we constructed a 3-input-3-output synthetic genetic reversible double Feynman logic gate with a population of genetically engineered E. coli cells. Instead of following hierarchical electronic design principles, we adapted the concept of artificial neural networks (ANN) and built a single-layer artificial network-type architecture with five different engineered bacteria, named bactoneurons. We used three extracellular chemicals as input signals and the expression of three fluorescence proteins as the output signals. The cellular devices, which combine the input chemical signals linearly and pass them through a nonlinear activation function and represent specific bactoneurons, were built by designing and creating small synthetic genetic networks inside E. coli. The weights of each of the inputs and biases of individual bactoneurons in the bacterial ANN were adjusted by optimizing the synthetic genetic networks. When arranging the five bactoneurons through an ANN-type architecture, the system generated a double Feynman gate function at the population level. To our knowledge, this is the first reversible double Feynman gate realization with living cells. This work may have significance in development of biocomputer technology, reversible computation, ANN wetware, and synthetic biology.
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Affiliation(s)
- Rajkamal Srivastava
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Block A/F, Sector-I, Bidhannagar, Kolkata700064, India.,Homi Bhabha National Institute, Training School Complex, Anushaktinagar, Mumbai400094, India
| | - Sangram Bagh
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Block A/F, Sector-I, Bidhannagar, Kolkata700064, India.,Homi Bhabha National Institute, Training School Complex, Anushaktinagar, Mumbai400094, India
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Reineke TM. Bioconjugate Chemistry: Enabling Innovation and Fostering Community at the Nexus of Synthetic and Biological Research. Bioconjug Chem 2023; 34:1-2. [PMID: 36563340 DOI: 10.1021/acs.bioconjchem.2c00591] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Theresa M Reineke
- Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, United States
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5
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Chakraborty D, Rengaswamy R, Raman K. Designing Biological Circuits: From Principles to Applications. ACS Synth Biol 2022; 11:1377-1388. [PMID: 35320676 DOI: 10.1021/acssynbio.1c00557] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.
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Affiliation(s)
- Debomita Chakraborty
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
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Srivastava R, Sarkar K, Bonnerjee D, Bagh S. Synthetic Genetic Reversible Feynman Gate in a Single E. coli Cell and Its Application in Bacterial to Mammalian Cell Information Transfer. ACS Synth Biol 2022; 11:1040-1048. [PMID: 35179369 DOI: 10.1021/acssynbio.1c00392] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Reversible computing is a nonconventional form of computing where the inputs and outputs are mapped in a unique one-to-one fashion. Reversible logic gates in single living cells have not been demonstrated. Here, we constructed a synthetic genetic reversible Feynman gate in single E. coli cells, and the input-output relations were measured in a clonal population. The inputs were extracellular chemicals, isopropyl β-d-1-thiogalactopyranoside (IPTG), and anhydrotetracycline (aTc), and the outputs were two fluorescence proteins. We developed a simple mathematical model and simulation to capture the essential features of the circuit and experimentally demonstrated that the behavior of the circuit was ultrasensitive and predictive. We showed an application by creating an intercellular Feynman gate, where input information from bacteria was computed and transferred to HeLa cells through shRNAs delivery and the output signals were observed as silencing of native AKT1 and CTNNB1 genes. The introduction of reversible logics in synthetic biology is new, and given that one-to-one input-output mapping, such reversible genetic systems might have applications in sensing, diagnostics, cellular computing, and synthetic biology.
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Affiliation(s)
- Rajkamal Srivastava
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
| | - Kathakali Sarkar
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
| | - Deepro Bonnerjee
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
| | - Sangram Bagh
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
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Mısırlı G, Yang B, James K, Wipat A. Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits. ACS Synth Biol 2021; 10:3304-3315. [PMID: 34762797 DOI: 10.1021/acssynbio.1c00157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, ST5 5BG, U.K
| | - Bill Yang
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
| | - Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, U.K
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
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Sarkar K, Chakraborty S, Bonnerjee D, Bagh S. Distributed Computing with Engineered Bacteria and Its Application in Solving Chemically Generated 2 × 2 Maze Problems. ACS Synth Biol 2021; 10:2456-2464. [PMID: 34543017 DOI: 10.1021/acssynbio.1c00279] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This work presented an application of genetic distributed computing, where an abstract computational problem was mapped on a complex truth table and solved using simple genetic circuits distributed among various cell populations. Maze generating and solving are challenging problems in mathematics and computing. Here, we mapped all the input-output matrices of a 2 × 2 mathematical maze on a 4-input-4-output truth table. The logic values of four chemical inputs determined the 16 different 2 × 2 maze problems on a chemical space. We created six multi-input synthetic genetic AND gates, which distributed among six cell populations and organized in a single layer. Those cell populations in a mixed culture worked as a computational solver, which solved the chemically generated maze problems by expressing or not expressing four different fluorescent proteins. The three available "solutions" were visualized by glowing bacteria, and for the 13 "no solution" cases, no bacteria glowed. Thus, our system not only solved the maze problems but also showed the number of solvable and unsolvable problems. This work may have significance in cellular computation and synthetic biology.
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Affiliation(s)
- Kathakali Sarkar
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
| | - Saswata Chakraborty
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
| | - Deepro Bonnerjee
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
| | - Sangram Bagh
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI), Block A/F, Sector-I, Bidhannagar, Kolkata 700064, India
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