Artículos de revistas: "Synthetic gene circuits"

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Santos-Moreno, Javier y Yolanda Schaerli. "CRISPR-based gene expression control for synthetic gene circuits". Biochemical Society Transactions 48, n.º 5 (23 de septiembre de 2020): 1979–93. http://dx.doi.org/10.1042/bst20200020.

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Synthetic gene circuits allow us to govern cell behavior in a programmable manner, which is central to almost any application aiming to harness engineered living cells for user-defined tasks. Transcription factors (TFs) constitute the ‘classic’ tool for synthetic circuit construction but some of their inherent constraints, such as insufficient modularity, orthogonality and programmability, limit progress in such forward-engineering endeavors. Here we review how CRISPR (clustered regularly interspaced short palindromic repeats) technology offers new and powerful possibilities for synthetic circuit design. CRISPR systems offer superior characteristics over TFs in many aspects relevant to a modular, predictable and standardized circuit design. Thus, the choice of CRISPR technology as a framework for synthetic circuit design constitutes a valid alternative to complement or replace TFs in synthetic circuits and promises the realization of more ambitious designs.

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Ray, L. Bryan. "Stabilizing synthetic gene circuits". Science 365, n.º 6457 (5 de septiembre de 2019): 995.15–997. http://dx.doi.org/10.1126/science.365.6457.995-o.

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3

Zhang, Yongpeng, Yuhua Feng, Yuan Liang, Jing Yang y Cheng Zhang. "Development of Synthetic DNA Circuit and Networks for Molecular Information Processing". Nanomaterials 11, n.º 11 (4 de noviembre de 2021): 2955. http://dx.doi.org/10.3390/nano11112955.

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Deoxyribonucleic acid (DNA), a genetic material, encodes all living information and living characteristics, e.g., in cell, DNA signaling circuits control the transcription activities of specific genes. In recent years, various DNA circuits have been developed to implement a wide range of signaling and for regulating gene network functions. In particular, a synthetic DNA circuit, with a programmable design and easy construction, has become a crucial method through which to simulate and regulate DNA signaling networks. Importantly, the construction of a hierarchical DNA circuit provides a useful tool for regulating gene networks and for processing molecular information. Moreover, via their robust and modular properties, DNA circuits can amplify weak signals and establish programmable cascade systems, which are particularly suitable for the applications of biosensing and detecting. Furthermore, a biological enzyme can also be used to provide diverse circuit regulation elements. Currently, studies regarding the mechanisms and applications of synthetic DNA circuit are important for the establishment of more advanced artificial gene regulation systems and intelligent molecular sensing tools. We therefore summarize recent relevant research progress, contributing to the development of nanotechnology-based synthetic DNA circuits. By summarizing the current highlights and the development of synthetic DNA circuits, this paper provides additional insights for future DNA circuit development and provides a foundation for the construction of more advanced DNA circuits.

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4

Nandagopal, Nagarajan y Michael B. Elowitz. "Synthetic Biology: Integrated Gene Circuits". Science 333, n.º 6047 (1 de septiembre de 2011): 1244–48. http://dx.doi.org/10.1126/science.1207084.

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A major goal of synthetic biology is to develop a deeper understanding of biological design principles from the bottom up, by building circuits and studying their behavior in cells. Investigators initially sought to design circuits “from scratch” that functioned as independently as possible from the underlying cellular system. More recently, researchers have begun to develop a new generation of synthetic circuits that integrate more closely with endogenous cellular processes. These approaches are providing fundamental insights into the regulatory architecture, dynamics, and evolution of genetic circuits and enabling new levels of control across diverse biological systems.

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5

Alamos, Simon y Patrick M. Shih. "Synthetic gene circuits take root". Science 377, n.º 6607 (12 de agosto de 2022): 711–12. http://dx.doi.org/10.1126/science.add6805.

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6

Chalancon, Guilhem y M. Madan Babu. "Scaling up synthetic gene circuits". Nature Nanotechnology 5, n.º 9 (septiembre de 2010): 631–33. http://dx.doi.org/10.1038/nnano.2010.178.

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7

Ray, L. Bryan. "Cooperativity in synthetic gene circuits". Science 364, n.º 6440 (9 de mayo de 2019): 542.10–544. http://dx.doi.org/10.1126/science.364.6440.542-j.

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8

Cloney, Ross. "Cooperating on synthetic gene circuits". Nature Biotechnology 37, n.º 7 (julio de 2019): 729. http://dx.doi.org/10.1038/s41587-019-0182-3.

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9

Gardner, Laura y Alexander Deiters. "Light-controlled synthetic gene circuits". Current Opinion in Chemical Biology 16, n.º 3-4 (agosto de 2012): 292–99. http://dx.doi.org/10.1016/j.cbpa.2012.04.010.

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10

Bashor, Caleb J., Nikit Patel, Sandeep Choubey, Ali Beyzavi, Jané Kondev, James J. Collins y Ahmad S. Khalil. "Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies". Science 364, n.º 6440 (18 de abril de 2019): 593–97. http://dx.doi.org/10.1126/science.aau8287.

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Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic circuits.

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11

English, Max A., Raphaël V. Gayet y James J. Collins. "Designing Biological Circuits: Synthetic Biology Within the Operon Model and Beyond". Annual Review of Biochemistry 90, n.º 1 (20 de junio de 2021): 221–44. http://dx.doi.org/10.1146/annurev-biochem-013118-111914.

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In 1961, Jacob and Monod proposed the operon model of gene regulation. At the model's core was the modular assembly of regulators, operators, and structural genes. To illustrate the composability of these elements, Jacob and Monod linked phenotypic diversity to the architectures of regulatory circuits. In this review, we examine how the circuit blueprints imagined by Jacob and Monod laid the foundation for the first synthetic gene networks that launched the field of synthetic biology in 2000. We discuss the influences of the operon model and its broader theoretical framework on the first generation of synthetic biological circuits, which were predominantly transcriptional and posttranscriptional circuits. We also describe how recent advances in molecular biology beyond the operon model—namely, programmable DNA- and RNA-binding molecules as well as models of epigenetic and posttranslational regulation—are expanding the synthetic biology toolkit and enabling the design of more complex biological circuits.

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12

Din, M. Omar, Aida Martin, Ivan Razinkov, Nicholas Csicsery y Jeff Hasty. "Interfacing gene circuits with microelectronics through engineered population dynamics". Science Advances 6, n.º 21 (mayo de 2020): eaaz8344. http://dx.doi.org/10.1126/sciadv.aaz8344.

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While there has been impressive progress connecting bacterial behavior with electrodes, an attractive observation to facilitate advances in synthetic biology is that the growth of a bacterial colony can be determined from impedance changes over time. Here, we interface synthetic biology with microelectronics through engineered population dynamics that regulate the accumulation of charged metabolites. We demonstrate electrical detection of the bacterial response to heavy metals via a population control circuit. We then implement this approach to a synchronized genetic oscillator where we obtain an oscillatory impedance profile from engineered bacteria. We lastly miniaturize an array of electrodes to form “bacterial integrated circuits” and demonstrate its applicability as an interface with genetic circuits. This approach paves the way for new advances in synthetic biology, analytical chemistry, and microelectronic technologies.

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13

Swank, Zoe y Sebastian J. Maerkl. "CFPU: A Cell-Free Processing Unit for High-Throughput, Automated In Vitro Circuit Characterization in Steady-State Conditions". BioDesign Research 2021 (17 de marzo de 2021): 1–11. http://dx.doi.org/10.34133/2021/2968181.

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Forward engineering synthetic circuits are at the core of synthetic biology. Automated solutions will be required to facilitate circuit design and implementation. Circuit design is increasingly being automated with design software, but innovations in experimental automation are lagging behind. Microfluidic technologies made it possible to perform in vitro transcription-translation (tx-tl) reactions with increasing throughput and sophistication, enabling screening and characterization of individual circuit elements and complete circuit designs. Here, we developed an automated microfluidic cell-free processing unit (CFPU) that extends high-throughput screening capabilities to a steady-state reaction environment, which is essential for the implementation and analysis of more complex and dynamic circuits. The CFPU contains 280 chemostats that can be individually programmed with DNA circuits. Each chemostat is periodically supplied with tx-tl reagents, giving rise to sustained, long-term steady-state conditions. Using microfluidic pulse width modulation (PWM), the device is able to generate tx-tl reagent compositions in real time. The device has higher throughput, lower reagent consumption, and overall higher functionality than current chemostat devices. We applied this technology to map transcription factor-based repression under equilibrium conditions and implemented dynamic gene circuits switchable by small molecules. We expect the CFPU to help bridge the gap between circuit design and experimental automation for in vitro development of synthetic gene circuits.

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14

Melendez-Alvarez, Juan Ramon y Xiao-Jun Tian. "Emergence of qualitative states in synthetic circuits driven by ultrasensitive growth feedback". PLOS Computational Biology 18, n.º 9 (16 de septiembre de 2022): e1010518. http://dx.doi.org/10.1371/journal.pcbi.1010518.

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The mutual interactions between the synthetic gene circuits and the host growth could cause unexpected outcomes in the dynamical behaviors of the circuits. However, how the steady states and the stabilities of the gene circuits are affected by host cell growth is not fully understood. Here, we developed a mathematical model for nonlinear growth feedback based on published experimental data. The model analysis predicts that growth feedback could significantly change the qualitative states of the system. Bistability could emerge in a circuit without positive feedback, and high-order multistability (three or more steady states) arises in the self-activation and toggle switch circuits. Our results provide insight into the potential effects of ultrasensitive growth feedback on the emergence of qualitative states in synthetic circuits and the corresponding underlying mechanism.

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15

Racovita, Adrian y Alfonso Jaramillo. "Reinforcement learning in synthetic gene circuits". Biochemical Society Transactions 48, n.º 4 (5 de agosto de 2020): 1637–43. http://dx.doi.org/10.1042/bst20200008.

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Synthetic gene circuits allow programming in DNA the expression of a phenotype at a given environmental condition. The recent integration of memory systems with gene circuits opens the door to their adaptation to new conditions and their re-programming. This lays the foundation to emulate neuromorphic behaviour and solve complex problems similarly to artificial neural networks. Cellular products such as DNA or proteins can be used to store memory in both digital and analog formats, allowing cells to be turned into living computing devices able to record information regarding their previous states. In particular, synthetic gene circuits with memory can be engineered into living systems to allow their adaptation through reinforcement learning. The development of gene circuits able to adapt through reinforcement learning moves Sciences towards the ambitious goal: the bottom-up creation of a fully fledged living artificial intelligence.

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16

Didovyk, Andriy y Lev S. Tsimring. "Synthetic Gene Circuits Learn to Classify". Cell Systems 4, n.º 2 (febrero de 2017): 151–53. http://dx.doi.org/10.1016/j.cels.2017.02.001.

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17

Stelling, J. "Computational Engineering of Synthetic Gene Circuits". Chemie Ingenieur Technik 82, n.º 9 (27 de agosto de 2010): 1493. http://dx.doi.org/10.1002/cite.201050633.

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18

Kis, Zoltán, Hugo Sant'Ana Pereira, Takayuki Homma, Ryan M. Pedrigi y Rob Krams. "Mammalian synthetic biology: emerging medical applications". Journal of The Royal Society Interface 12, n.º 106 (mayo de 2015): 20141000. http://dx.doi.org/10.1098/rsif.2014.1000.

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In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON–OFF switches, categorized into transcriptional, post-transcriptional, translational and post-translational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes.

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19

Ge, Huanhuan y Mario Andrea Marchisio. "Aptamers, Riboswitches, and Ribozymes in S. cerevisiae Synthetic Biology". Life 11, n.º 3 (17 de marzo de 2021): 248. http://dx.doi.org/10.3390/life11030248.

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Among noncoding RNA sequences, riboswitches and ribozymes have attracted the attention of the synthetic biology community as circuit components for translation regulation. When fused to aptamer sequences, ribozymes and riboswitches are enabled to interact with chemicals. Therefore, protein synthesis can be controlled at the mRNA level without the need for transcription factors. Potentially, the use of chemical-responsive ribozymes/riboswitches would drastically simplify the design of genetic circuits. In this review, we describe synthetic RNA structures that have been used so far in the yeast Saccharomyces cerevisiae. We present their interaction mode with different chemicals (e.g., theophylline and antibiotics) or proteins (such as the RNase III) and their recent employment into clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 9 (CRISPR-Cas) systems. Particular attention is paid, throughout the whole paper, to their usage and performance into synthetic gene circuits.

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20

Szenk, Mariola, Terrence Yim y Gábor Balázsi. "Multiplexed Gene Expression Tuning with Orthogonal Synthetic Gene Circuits". ACS Synthetic Biology 9, n.º 4 (13 de marzo de 2020): 930–39. http://dx.doi.org/10.1021/acssynbio.9b00534.

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21

Chen, Bor-Sen, Chih-Yuan Hsu y Jing-Jia Liou. "Robust Design of Biological Circuits: Evolutionary Systems Biology Approach". Journal of Biomedicine and Biotechnology 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/304236.

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Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are givenin silicowith the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.

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22

Li, Hui-Shan, Divya V. Israni, Keith A. Gagnon, Kok Ann Gan, Michael H. Raymond, Jeffry D. Sander, Kole T. Roybal, J. Keith Joung, Wilson W. Wong y Ahmad S. Khalil. "Multidimensional control of therapeutic human cell function with synthetic gene circuits". Science 378, n.º 6625 (16 de diciembre de 2022): 1227–34. http://dx.doi.org/10.1126/science.ade0156.

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Synthetic gene circuits that precisely control human cell function could expand the capabilities of gene- and cell-based therapies. However, platforms for developing circuits in primary human cells that drive robust functional changes in vivo and have compositions suitable for clinical use are lacking. Here, we developed synthetic zinc finger transcription regulators (synZiFTRs), which are compact and based largely on human-derived proteins. As a proof of principle, we engineered gene switches and circuits that allow precise, user-defined control over therapeutically relevant genes in primary T cells using orthogonal, US Food and Drug Administration–approved small-molecule inducers. Our circuits can instruct T cells to sequentially activate multiple cellular programs such as proliferation and antitumor activity to drive synergistic therapeutic responses. This platform should accelerate the development and clinical translation of synthetic gene circuits in diverse human cell types and contexts.

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23

Peng, Shuguang, Huiya Huang, Ping Wei y Zhen Xie. "Synthetic gene circuits moving into the clinic". Quantitative Biology 9, n.º 1 (2021): 100. http://dx.doi.org/10.15302/j-qb-021-0234.

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24

Haseltine, Eric L. y Frances H. Arnold. "Synthetic Gene Circuits: Design with Directed Evolution". Annual Review of Biophysics and Biomolecular Structure 36, n.º 1 (junio de 2007): 1–19. http://dx.doi.org/10.1146/annurev.biophys.36.040306.132600.

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25

Ye, Haifeng, Dominique Aubel y Martin Fussenegger. "Synthetic mammalian gene circuits for biomedical applications". Current Opinion in Chemical Biology 17, n.º 6 (diciembre de 2013): 910–17. http://dx.doi.org/10.1016/j.cbpa.2013.10.006.

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26

Marchisio, Mario A. y Jörg Stelling. "Automatic Design of Digital Synthetic Gene Circuits". PLoS Computational Biology 7, n.º 2 (17 de febrero de 2011): e1001083. http://dx.doi.org/10.1371/journal.pcbi.1001083.

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27

Higashikuni, Yasutomi, William CW Chen y Timothy K. Lu. "Advancing therapeutic applications of synthetic gene circuits". Current Opinion in Biotechnology 47 (octubre de 2017): 133–41. http://dx.doi.org/10.1016/j.copbio.2017.06.011.

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28

Ye, Haifeng y Martin Fussenegger. "Synthetic therapeutic gene circuits in mammalian cells". FEBS Letters 588, n.º 15 (17 de mayo de 2014): 2537–44. http://dx.doi.org/10.1016/j.febslet.2014.05.003.

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29

Deans, Tara L., Anirudha Singh, Matthew Gibson y Jennifer H. Elisseeff. "Regulating synthetic gene networks in 3D materials". Proceedings of the National Academy of Sciences 109, n.º 38 (27 de agosto de 2012): 15217–22. http://dx.doi.org/10.1073/pnas.1204705109.

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Combining synthetic biology and materials science will enable more advanced studies of cellular regulatory processes, in addition to facilitating therapeutic applications of engineered gene networks. One approach is to couple genetic inducers into biomaterials, thereby generating 3D microenvironments that are capable of controlling intrinsic and extrinsic cellular events. Here, we have engineered biomaterials to present the genetic inducer, IPTG, with different modes of activating genetic circuits in vitro and in vivo. Gene circuits were activated in materials with IPTG embedded within the scaffold walls or chemically linked to the matrix. In addition, systemic applications of IPTG were used to induce genetic circuits in cells encapsulated into materials and implanted in vivo. The flexibility of modifying biomaterials with genetic inducers allows for patterned placement of these inducers that can be used to generate distinct patterns of gene expression. Together, these genetically interactive materials can be used to characterize genetic circuits in environments that more closely mimic cells’ natural 3D settings, to better explore complex cell–matrix and cell–cell interactions, and to facilitate therapeutic applications of synthetic biology.

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Bashor, Caleb J. y James J. Collins. "Understanding Biological Regulation Through Synthetic Biology". Annual Review of Biophysics 47, n.º 1 (20 de mayo de 2018): 399–423. http://dx.doi.org/10.1146/annurev-biophys-070816-033903.

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Engineering synthetic gene regulatory circuits proceeds through iterative cycles of design, building, and testing. Initial circuit designs must rely on often-incomplete models of regulation established by fields of reductive inquiry—biochemistry and molecular and systems biology. As differences in designed and experimentally observed circuit behavior are inevitably encountered, investigated, and resolved, each turn of the engineering cycle can force a resynthesis in understanding of natural network function. Here, we outline research that uses the process of gene circuit engineering to advance biological discovery. Synthetic gene circuit engineering research has not only refined our understanding of cellular regulation but furnished biologists with a toolkit that can be directed at natural systems to exact precision manipulation of network structure. As we discuss, using circuit engineering to predictively reorganize, rewire, and reconstruct cellular regulation serves as the ultimate means of testing and understanding how cellular phenotype emerges from systems-level network function.

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31

Hong, Seongho, Dohyun Jeong, Jordan Ryan, Mathias Foo, Xun Tang y Jongmin Kim. "Design and Evaluation of Synthetic RNA-Based Incoherent Feed-Forward Loop Circuits". Biomolecules 11, n.º 8 (10 de agosto de 2021): 1182. http://dx.doi.org/10.3390/biom11081182.

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RNA-based regulators are promising tools for building synthetic biological systems that provide a powerful platform for achieving a complex regulation of transcription and translation. Recently, de novo-designed synthetic RNA regulators, such as the small transcriptional activating RNA (STAR), toehold switch (THS), and three-way junction (3WJ) repressor, have been utilized to construct RNA-based synthetic gene circuits in living cells. In this work, we utilized these regulators to construct type 1 incoherent feed-forward loop (IFFL) circuits in vivo and explored their dynamic behaviors. A combination of a STAR and 3WJ repressor was used to construct an RNA-only IFFL circuit. However, due to the fast kinetics of RNA–RNA interactions, there was no significant timescale difference between the direct activation and the indirect inhibition, that no pulse was observed in the experiments. These findings were confirmed with mechanistic modeling and simulation results for a wider range of conditions. To increase delay in the inhibition pathway, we introduced a protein synthesis process to the circuit and designed an RNA–protein hybrid IFFL circuit using THS and TetR protein. Simulation results indicated that pulse generation could be achieved with this RNA–protein hybrid model, and this was further verified with experimental realization in E. coli. Our findings demonstrate that while RNA-based regulators excel in speed as compared to protein-based regulators, the fast reaction kinetics of RNA-based regulators could also undermine the functionality of a circuit (e.g., lack of significant timescale difference). The agreement between experiments and simulations suggests that the mechanistic modeling can help debug issues and validate the hypothesis in designing a new circuit. Moreover, the applicability of the kinetic parameters extracted from the RNA-only circuit to the RNA–protein hybrid circuit also indicates the modularity of RNA-based regulators when used in a different context. We anticipate the findings of this work to guide the future design of gene circuits that rely heavily on the dynamics of RNA-based regulators, in terms of both modeling and experimental realization.

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Jeong, Dohyun, Melissa Klocke, Siddharth Agarwal, Jeongwon Kim, Seungdo Choi, Elisa Franco y Jongmin Kim. "Cell-Free Synthetic Biology Platform for Engineering Synthetic Biological Circuits and Systems". Methods and Protocols 2, n.º 2 (14 de mayo de 2019): 39. http://dx.doi.org/10.3390/mps2020039.

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Synthetic biology integrates diverse engineering disciplines to create novel biological systems for biomedical and technological applications. The substantial growth of the synthetic biology field in the past decade is poised to transform biotechnology and medicine. To streamline design processes and facilitate debugging of complex synthetic circuits, cell-free synthetic biology approaches has reached broad research communities both in academia and industry. By recapitulating gene expression systems in vitro, cell-free expression systems offer flexibility to explore beyond the confines of living cells and allow networking of synthetic and natural systems. Here, we review the capabilities of the current cell-free platforms, focusing on nucleic acid-based molecular programs and circuit construction. We survey the recent developments including cell-free transcription–translation platforms, DNA nanostructures and circuits, and novel classes of riboregulators. The links to mathematical models and the prospects of cell-free synthetic biology platforms will also be discussed.

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Azizoglu, Asli y Jörg Stelling. "Controlling cell-to-cell variability with synthetic gene circuits". Biochemical Society Transactions 47, n.º 6 (5 de diciembre de 2019): 1795–804. http://dx.doi.org/10.1042/bst20190295.

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Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.

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Stelzer, Christoph y Yaakov Benenson. "Precise determination of input-output mapping for multimodal gene circuits using data from transient transfection". PLOS Computational Biology 16, n.º 11 (30 de noviembre de 2020): e1008389. http://dx.doi.org/10.1371/journal.pcbi.1008389.

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The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.

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35

Feng, Xiaofan y Mario Andrea Marchisio. "Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era". Biology 10, n.º 6 (6 de junio de 2021): 504. http://dx.doi.org/10.3390/biology10060504.

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Synthetic gene circuits are made of DNA sequences, referred to as transcription units, that communicate by exchanging proteins or RNA molecules. Proteins are, mostly, transcription factors that bind promoter sequences to modulate the expression of other molecules. Promoters are, therefore, key components in genetic circuits. In this review, we focus our attention on the construction of artificial promoters for the yeast S. cerevisiae, a popular chassis for gene circuits. We describe the initial techniques and achievements in promoter engineering that predated the start of the Synthetic Biology epoch of about 20 years. We present the main applications of synthetic promoters built via different methods and discuss the latest innovations in the wet-lab engineering of novel promoter sequences.

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Oyarzún, Diego A. y Guy-Bart V. Stan. "Synthetic gene circuits for metabolic control: design trade-offs and constraints". Journal of The Royal Society Interface 10, n.º 78 (6 de enero de 2013): 20120671. http://dx.doi.org/10.1098/rsif.2012.0671.

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A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations.

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37

Li, Zonglun, Alya Fattah, Peter Timashev y Alexey Zaikin. "An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation". Sensors 22, n.º 15 (7 de agosto de 2022): 5907. http://dx.doi.org/10.3390/s22155907.

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The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando’s model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando’s model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.

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Saxena, Pratik, Ghislaine Charpin-El Hamri, Marc Folcher, Henryk Zulewski y Martin Fussenegger. "Synthetic gene network restoring endogenous pituitary–thyroid feedback control in experimental Graves’ disease". Proceedings of the National Academy of Sciences 113, n.º 5 (19 de enero de 2016): 1244–49. http://dx.doi.org/10.1073/pnas.1514383113.

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Graves’ disease is an autoimmune disorder that causes hyperthyroidism because of autoantibodies that bind to the thyroid-stimulating hormone receptor (TSHR) on the thyroid gland, triggering thyroid hormone release. The physiological control of thyroid hormone homeostasis by the feedback loops involving the hypothalamus–pituitary–thyroid axis is disrupted by these stimulating autoantibodies. To reset the endogenous thyrotrophic feedback control, we designed a synthetic mammalian gene circuit that maintains thyroid hormone homeostasis by monitoring thyroid hormone levels and coordinating the expression of a thyroid-stimulating hormone receptor antagonist (TSHAntag), which competitively inhibits the binding of thyroid-stimulating hormone or the human autoantibody to TSHR. This synthetic control device consists of a synthetic thyroid-sensing receptor (TSR), a yeast Gal4 protein/human thyroid receptor-α fusion, which reversibly triggers expression of the TSHAntag gene from TSR-dependent promoters. In hyperthyroid mice, this synthetic circuit sensed pathological thyroid hormone levels and restored the thyrotrophic feedback control of the hypothalamus–pituitary–thyroid axis to euthyroid hormone levels. Therapeutic plug and play gene circuits that restore physiological feedback control in metabolic disorders foster advanced gene- and cell-based therapies.

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39

Brophy, Jennifer A. N., Katie J. Magallon, Lina Duan, Vivian Zhong, Prashanth Ramachandran, Kiril Kniazev y José R. Dinneny. "Synthetic genetic circuits as a means of reprogramming plant roots". Science 377, n.º 6607 (12 de agosto de 2022): 747–51. http://dx.doi.org/10.1126/science.abo4326.

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The shape of a plant’s root system influences its ability to reach essential nutrients in the soil and to acquire water during drought. Progress in engineering plant roots to optimize water and nutrient acquisition has been limited by our capacity to design and build genetic programs that alter root growth in a predictable manner. We developed a collection of synthetic transcriptional regulators for plants that can be compiled to create genetic circuits. These circuits control gene expression by performing Boolean logic operations and can be used to predictably alter root structure. This work demonstrates the potential of synthetic genetic circuits to control gene expression across tissues and reprogram plant growth.

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40

Morel, Mathieu, Roman Shtrahman, Varda Rotter, Lior Nissim y Roy H. Bar-Ziv. "Cellular heterogeneity mediates inherent sensitivity–specificity tradeoff in cancer targeting by synthetic circuits". Proceedings of the National Academy of Sciences 113, n.º 29 (6 de julio de 2016): 8133–38. http://dx.doi.org/10.1073/pnas.1604391113.

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Synthetic gene circuits are emerging as a versatile means to target cancer with enhanced specificity by combinatorial integration of multiple expression markers. Such circuits must also be tuned to be highly sensitive because escape of even a few cells might be detrimental. However, the error rates of decision-making circuits in light of cellular variability in gene expression have so far remained unexplored. Here, we measure the single-cell response function of a tunable logic AND gate acting on two promoters in heterogeneous cell populations. Our analysis reveals an inherent tradeoff between specificity and sensitivity that is controlled by the AND gate amplification gain and activation threshold. We implement a tumor-mimicking cell-culture model of cancer cells emerging in a background of normal ones, and show that molecular parameters of the synthetic circuits control specificity and sensitivity in a killing assay. This suggests that, beyond the inherent tradeoff, synthetic circuits operating in a heterogeneous environment could be optimized to efficiently target malignant state with minimal loss of specificity.

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41

Zhang, Fengyu, Yanhong Sun y Chunxiong Luo. "Microfluidic approaches for synthetic gene circuits’ construction and analysis". Quantitative Biology 9, n.º 1 (2021): 47. http://dx.doi.org/10.15302/j-qb-021-0235.

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42

Oyarzún, Diego A., Jean-Baptiste Lugagne y Guy-Bart V. Stan. "Noise Propagation in Synthetic Gene Circuits for Metabolic Control". ACS Synthetic Biology 4, n.º 2 (mayo de 2014): 116–25. http://dx.doi.org/10.1021/sb400126a.

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43

Pearce, Stephanie C., Ralph L. McWhinnie y Francis E. Nano. "Synthetic temperature-inducible lethal gene circuits in Escherichia coli". Microbiology 163, n.º 4 (1 de abril de 2017): 462–71. http://dx.doi.org/10.1099/mic.0.000446.

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44

Cheng, Yu-Yu, Andrew J. Hirning, Krešimir Josić y Matthew R. Bennett. "The Timing of Transcriptional Regulation in Synthetic Gene Circuits". ACS Synthetic Biology 6, n.º 11 (5 de septiembre de 2017): 1996–2002. http://dx.doi.org/10.1021/acssynbio.7b00118.

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45

Nissim, Lior, Ming-Ru Wu, Erez Pery, Adina Binder-Nissim, Hiroshi I. Suzuki, Doron Stupp, Claudia Wehrspaun, Yuval Tabach, Phillip A. Sharp y Timothy K. Lu. "Synthetic RNA-Based Immunomodulatory Gene Circuits for Cancer Immunotherapy". Cell 171, n.º 5 (noviembre de 2017): 1138–50. http://dx.doi.org/10.1016/j.cell.2017.09.049.

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46

Marchisio, M. A. y J. Stelling. "Computational design of synthetic gene circuits with composable parts". Bioinformatics 24, n.º 17 (25 de junio de 2008): 1903–10. http://dx.doi.org/10.1093/bioinformatics/btn330.

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47

May, Tobias, Milada Butueva, Sara Bantner, David Markusic, Jurgen Seppen, Roderick A. F. MacLeod, Herbert Weich, Hansjörg Hauser y Dagmar Wirth. "Synthetic Gene Regulation Circuits for Control of Cell Expansion". Tissue Engineering Part A 16, n.º 2 (febrero de 2010): 441–52. http://dx.doi.org/10.1089/ten.tea.2009.0184.

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48

Sarpeshkar, R. "Analog synthetic biology". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, n.º 2012 (28 de marzo de 2014): 20130110. http://dx.doi.org/10.1098/rsta.2013.0110.

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We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog–digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA–protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.

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Guinn, Michael Tyler y Gábor Balázsi. "Noise-reducing optogenetic negative-feedback gene circuits in human cells". Nucleic Acids Research 47, n.º 14 (3 de julio de 2019): 7703–14. http://dx.doi.org/10.1093/nar/gkz556.

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Abstract Gene autorepression is widely present in nature and is also employed in synthetic biology, partly to reduce gene expression noise in cells. Optogenetic systems have recently been developed for controlling gene expression levels in mammalian cells, but most have utilized activator-based proteins, neglecting negative feedback except for in silico control. Here, we engineer optogenetic gene circuits into mammalian cells to achieve noise-reduction for precise gene expression control by genetic, in vitro negative feedback. We build a toolset of these noise-reducing Light-Inducible Tuner (LITer) gene circuits using the TetR repressor fused with a Tet-inhibiting peptide (TIP) or a degradation tag through the light-sensitive LOV2 protein domain. These LITers provide a range of nearly 4-fold gene expression control and up to 5-fold noise reduction from existing optogenetic systems. Moreover, we use the LITer gene circuit architecture to control gene expression of the cancer oncogene KRAS(G12V) and study its downstream effects through phospho-ERK levels and cellular proliferation. Overall, these novel LITer optogenetic platforms should enable precise spatiotemporal perturbations for studying multicellular phenotypes in developmental biology, oncology and other biomedical fields of research.

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BULDÚ, JAVIER M., JORDI GARCÍA-OJALVO, ALEXANDRE WAGEMAKERS y MIGUEL A. F. SANJUÁN. "ELECTRONIC DESIGN OF SYNTHETIC GENETIC NETWORKS". International Journal of Bifurcation and Chaos 17, n.º 10 (octubre de 2007): 3507–11. http://dx.doi.org/10.1142/s0218127407019275.

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We propose the use of nonlinear electronic circuits to study synthetic gene regulation networks. Specifically, we have designed two electronic versions of a synthetic genetic clock, known as the "repressilator," making use of appropriate electronic elements linked in the same way as the original biochemical system. We study the effects of coupling in a population of electronic repressilators, with the aim of observing coherent oscillations of the whole population. With these results, we show that this kind of nonlinear circuits can be helpful in the design and understanding of synthetic genetic networks.

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