1
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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; 52:e58. [PMID: 38864396 PMCID: PMC11260469 DOI: 10.1093/nar/gkae491] [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: 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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2
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McCarthy J. Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae. Integr Biol (Camb) 2021; 13:210-220. [PMID: 34270725 DOI: 10.1093/intbio/zyab013] [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: 04/27/2021] [Revised: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 11/14/2022]
Abstract
This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.
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Affiliation(s)
- John McCarthy
- Warwick Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
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3
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Abstract
One of the fundamental properties of engineered large-scale complex systems is modularity. In synthetic biology, genetic parts exhibit context-dependent behavior. Here, we describe and quantify a major source of such behavior: retroactivity. In particular, we provide a step-by-step guide for characterizing retroactivity to restore the modular description of genetic modules. Additionally, we also discuss how retroactivity can be leveraged to quantify and maximize robustness to perturbations due to interconnection of genetic modules.
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Affiliation(s)
- Andras Gyorgy
- New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
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4
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Guinn MT, Wan Y, Levovitz S, Yang D, Rosner MR, Balázsi G. Observation and Control of Gene Expression Noise: Barrier Crossing Analogies Between Drug Resistance and Metastasis. Front Genet 2020; 11:586726. [PMID: 33193723 PMCID: PMC7662081 DOI: 10.3389/fgene.2020.586726] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Michael Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Stony Brook Medical Scientist Training Program, Stony Brook, NY, United States
| | - Yiming Wan
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Sarah Levovitz
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Dongbo Yang
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Marsha R Rosner
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
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5
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Moriya T, Yamaoka T, Wakayama Y, Ayukawa S, Zhang Z, Yamamura M, Wakao S, Kiga D. Comparison between Effects of Retroactivity and Resource Competition upon Change in Downstream Reporter Genes of Synthetic Genetic Circuits. Life (Basel) 2019; 9:life9010030. [PMID: 30917535 PMCID: PMC6463139 DOI: 10.3390/life9010030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/20/2019] [Accepted: 03/22/2019] [Indexed: 11/16/2022] Open
Abstract
Reporter genes have contributed to advancements in molecular biology. Binding of an upstream regulatory protein to a downstream reporter promoter allows quantification of the activity of the upstream protein produced from the corresponding gene. In studies of synthetic biology, analyses of reporter gene activities ensure control of the cell with synthetic genetic circuits, as achieved using a combination of in silico and in vivo experiments. However, unexpected effects of downstream reporter genes on upstream regulatory genes may interfere with in vivo observations. This phenomenon is termed as retroactivity. Using in silico and in vivo experiments, we found that a different copy number of regulatory protein-binding sites in a downstream gene altered the upstream dynamics, suggesting retroactivity of reporters in this synthetic genetic oscillator. Furthermore, by separating the two sources of retroactivity (titration of the component and competition for degradation), we showed that, in the dual-feedback oscillator, the level of the fluorescent protein reporter competing for degradation with the circuits' components is important for the stability of the oscillations. Altogether, our results indicate that the selection of reporter promoters using a combination of in silico and in vivo experiments is essential for the advanced design of genetic circuits.
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Affiliation(s)
- Takefumi Moriya
- Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan.
| | - Tomohiro Yamaoka
- Department of Electrical Engineering and Bioscience, Waseda University, Shinjuku, Tokyo 169-8050, Japan.
| | - Yuki Wakayama
- Department of Electrical Engineering and Bioscience, Waseda University, Shinjuku, Tokyo 169-8050, Japan.
| | - Shotaro Ayukawa
- Waseda Research Institute for Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8050, Japan.
| | - Zicong Zhang
- Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan.
| | - Masayuki Yamamura
- Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan.
| | - Shinji Wakao
- Department of Electrical Engineering and Bioscience, Waseda University, Shinjuku, Tokyo 169-8050, Japan.
| | - Daisuke Kiga
- Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan.
- Department of Electrical Engineering and Bioscience, Waseda University, Shinjuku, Tokyo 169-8050, Japan.
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6
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Zong DM, Cinar S, Shis DL, Josić K, Ott W, Bennett MR. Predicting Transcriptional Output of Synthetic Multi-input Promoters. ACS Synth Biol 2018; 7:1834-1843. [PMID: 30040895 DOI: 10.1021/acssynbio.8b00165] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Recent advances in synthetic biology have led to a wealth of well-characterized genetic parts. As parts libraries grow, so too does the potential to create novel multi-input promoters that integrate disparate signals to determine transcriptional output. Our ability to construct such promoters will outpace our ability to characterize promoter performance, due to the vast number of input combinations. In this study, we examine the input-output relations of recently developed synthetic multi-input promoters and describe two methods for predicting their behavior. The first method uses 1-dimensional induction data obtained from experiments on single-input systems to predict the n-dimensional induction responses of systems with n inputs. We demonstrate that this approach accurately predicts Boolean (on/off) responses of multi-input systems consisting of novel chimeric transcription factors and hybrid promoters in Escherichia coli. The second method uses only a small amount of multi-input response data to accurately predict analog system response over the entire landscape of input combinations. Taken together, these methods facilitate the design of synthetic circuits that utilize multi-input promoters.
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Affiliation(s)
| | - Selahittin Cinar
- Department of Mathematics, ⊥Department of Biology and Biochemistry, University of Houston, Houston, Texas 77004, United States
| | | | - Krešimir Josić
- Department of Mathematics, ⊥Department of Biology and Biochemistry, University of Houston, Houston, Texas 77004, United States
| | - William Ott
- Department of Mathematics, ⊥Department of Biology and Biochemistry, University of Houston, Houston, Texas 77004, United States
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7
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Abstract
The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.
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Affiliation(s)
- David L Shis
- Department of Biosciences, Rice University, Houston, Texas 77005, USA;
| | - Matthew R Bennett
- Department of Biosciences, Rice University, Houston, Texas 77005, USA; .,Department of Bioengineering, Rice University, Houston, Texas 77005, USA
| | - Oleg A Igoshin
- Department of Biosciences, Rice University, Houston, Texas 77005, USA; .,Department of Bioengineering, Rice University, Houston, Texas 77005, USA.,Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA
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8
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Pantoja-Hernández L, Martínez-García JC. Retroactivity in the Context of Modularly Structured Biomolecular Systems. Front Bioeng Biotechnol 2015; 3:85. [PMID: 26137457 PMCID: PMC4470261 DOI: 10.3389/fbioe.2015.00085] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 05/24/2015] [Indexed: 11/13/2022] Open
Abstract
Synthetic biology has intensively promoted the technical implementation of modular strategies in the fabrication of biological devices. Modules are considered as networks of reactions. The behavior displayed by biomolecular systems results from the information processes carried out by the interconnection of the involved modules. However, in natural systems, module wiring is not a free-of-charge process; as a consequence of interconnection, a reactive phenomenon called retroactivity emerges. This phenomenon is characterized by signals that propagate from downstream modules (the modules that receive the incoming signals upon interconnection) to upstream ones (the modules that send the signals upon interconnection). Such retroactivity signals, depending of their strength, may change and sometimes even disrupt the behavior of modular biomolecular systems. Thus, analysis of retroactivity effects in natural biological and biosynthetic systems is crucial to achieve a deeper understanding of how this interconnection between functionally characterized modules takes place and how it impacts the overall behavior of the involved cell. By discussing the modules interconnection in natural and synthetic biomolecular systems, we propose that such systems should be considered as quasi-modular.
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Affiliation(s)
- Libertad Pantoja-Hernández
- Laboratorio de Genética Molecular, Desarrollo y Evolución de Plantas, Instituto de Ecología, Universidad Nacional Autónoma de México , Mexico City , Mexico ; Centro de Ciencias de Complejidad (C3), Universidad Nacional Autónoma de México , Mexico City , Mexico
| | - Juan Carlos Martínez-García
- Departamento de Control Automático, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN) , Mexico City , Mexico
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9
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Kim KH, Sauro HM. Stochastic modular analysis for gene circuits: interplay among retroactivity, nonlinearity, and stochasticity. Methods Mol Biol 2015; 1244:287-297. [PMID: 25487103 DOI: 10.1007/978-1-4939-1878-2_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This chapter introduces a computational analysis method for analyzing gene circuit dynamics in terms of modules while taking into account stochasticity, system nonlinearity, and retroactivity. (1) ANALOG ELECTRICAL CIRCUIT REPRESENTATION FOR GENE CIRCUITS: A connection between two gene circuit components is often mediated by a transcription factor (TF) and the connection signal is described by the TF concentration. The TF is sequestered to its specific binding site (promoter region) and regulates downstream transcription. This sequestration has been known to affect the dynamics of the TF by increasing its response time. The downstream effect-retroactivity-has been shown to be explicitly described in an electrical circuit representation, as an input capacitance increase. We provide a brief review on this topic. (2) MODULAR DESCRIPTION OF NOISE PROPAGATION: Gene circuit signals are noisy due to the random nature of biological reactions. The noisy fluctuations in TF concentrations affect downstream regulation. Thus, noise can propagate throughout the connected system components. This can cause different circuit components to behave in a statistically dependent manner, hampering a modular analysis. Here, we show that the modular analysis is still possible at the linear noise approximation level. (3) NOISE EFFECT ON MODULE INPUT-OUTPUT RESPONSE: We investigate how to deal with a module input-output response and its noise dependency. Noise-induced phenotypes are described as an interplay between system nonlinearity and signal noise. Lastly, we provide the comprehensive approach incorporating the above three analysis methods, which we call "stochastic modular analysis." This method can provide an analysis framework for gene circuit dynamics when the nontrivial effects of retroactivity, stochasticity, and nonlinearity need to be taken into account.
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Affiliation(s)
- Kyung Hyuk Kim
- Department of Bioengineering, University of Washington, William H. Foege Building, 355061, Seattle, WA, 98195, USA,
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10
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Loads bias genetic and signaling switches in synthetic and natural systems. PLoS Comput Biol 2014; 10:e1003533. [PMID: 24676102 PMCID: PMC3967935 DOI: 10.1371/journal.pcbi.1003533] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 01/29/2014] [Indexed: 01/05/2023] Open
Abstract
Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms. Cells rely on complex networks of protein-protein interactions in order to carry out life functions. Scientists believe that these networks are organized in a modular fashion; that is they are made up of functionally distinct parts like an electronic circuit. Modularity implies that just as we put together electronic parts to make an amplifier that we can use in many different circuits, we can put together biochemical reactions to make an amplifier, or a switch or an oscillator, which perform the same function in different organisms. This assumption is important in synthetic biology, where we engineer and assemble synthetic genetic circuits in living organisms in a modular fashion. We show that for important modules like genetic and signaling switches, the assumption of modularity has a crucial limitation. We show that if one simply connects a biological switch to another downstream circuit, the presence of the connection changes the operation of the switch, which in some cases may stop behaving like a switch. Our work underscores the importance of taking into account these downstream connections and suggests that natural systems may be balancing some of these components in order to ensure that despite diversity, modules continue to work in different systems with fidelity.
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11
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Gyorgy A, Del Vecchio D. Modular composition of gene transcription networks. PLoS Comput Biol 2014; 10:e1003486. [PMID: 24626132 PMCID: PMC3952816 DOI: 10.1371/journal.pcbi.1003486] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Accepted: 01/10/2014] [Indexed: 12/15/2022] Open
Abstract
Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems. Biological modules are inherently context-dependent as the input/output behavior of a module often changes upon connection with other modules. One source of context-dependence is retroactivity, a loading phenomenon by which a downstream system affects the behavior of an upstream system upon interconnection. This fact renders it difficult to predict how modules will behave once connected to each other. In this paper, we propose a general modeling framework for gene transcription networks to accurately predict how retroactivity affects the dynamic behavior of interconnected modules, based on salient physical properties of the same modules in isolation. We illustrate how our framework predicts surprising and counter-intuitive dynamic properties of naturally occurring network structures, which cannot be captured by existing models of the same dimension. We describe implications of our findings on the bottom-up approach to designing synthetic circuits, and on the top-down approach to identifying functional modules in natural networks, revealing trade-offs between robustness to interconnection and dynamic performance. Our framework carries substantial conceptual analogies with electrical network theory based on equivalent representations. We believe that the framework we have proposed, also based on equivalent network representations, can be similarly useful for the analysis and design of biological networks.
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Affiliation(s)
- Andras Gyorgy
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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12
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Marchisio MA. In silico design and in vivo implementation of yeast gene Boolean gates. J Biol Eng 2014; 8:6. [PMID: 24485181 PMCID: PMC3926364 DOI: 10.1186/1754-1611-8-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 01/25/2014] [Indexed: 12/05/2022] Open
Abstract
In our previous computational work, we showed that gene digital circuits can be automatically designed in an electronic fashion. This demands, first, a conversion of the truth table into Boolean formulas with the Karnaugh map method and, then, the translation of the Boolean formulas into circuit schemes organized into layers of Boolean gates and Pools of signal carriers. In our framework, gene digital circuits that take up to three different input signals (chemicals) arise from the composition of three kinds of basic Boolean gates, namely YES, NOT, and AND. Here we present a library of YES, NOT, and AND gates realized via plasmidic DNA integration into the yeast genome. Boolean behavior is reproduced via the transcriptional control of a synthetic bipartite promoter that contains sequences of the yeast VPH1 and minimal CYC1 promoters together with operator binding sites for bacterial (i.e. orthogonal) repressor proteins. Moreover, model-driven considerations permitted us to pinpoint a strategy for re-designing gates when a better digital performance is required. Our library of well-characterized Boolean gates is the basis for the assembly of more complex gene digital circuits. As a proof of concepts, we engineered two 2-input OR gates, designed by our software, by combining YES and NOT gates present in our library.
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Affiliation(s)
- Mario A Marchisio
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, Basel 4058, Switzerland.
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13
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Abstract
Noise permeates biology on all levels, from the most basic molecular, sub-cellular processes to the dynamics of tissues, organs, organisms and populations. The functional roles of noise in biological processes can vary greatly. Along with standard, entropy-increasing effects of producing random mutations, diversifying phenotypes in isogenic populations, limiting information capacity of signaling relays, it occasionally plays more surprising constructive roles by accelerating the pace of evolution, providing selective advantage in dynamic environments, enhancing intracellular transport of biomolecules and increasing information capacity of signaling pathways. This short review covers the recent progress in understanding mechanisms and effects of fluctuations in biological systems of different scales and the basic approaches to their mathematical modeling.
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Affiliation(s)
- Lev S. Tsimring
- BioCircuits Institute, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0328, USA
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14
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Mather WH, Hasty J, Tsimring LS, Williams RJ. Translational cross talk in gene networks. Biophys J 2014; 104:2564-72. [PMID: 23746529 DOI: 10.1016/j.bpj.2013.04.049] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2012] [Revised: 04/03/2013] [Accepted: 04/11/2013] [Indexed: 01/25/2023] Open
Abstract
It has been shown experimentally that competition for limited translational resources by upstream mRNAs can lead to an anticorrelation between protein counts. Here, we investigate a stochastic model for this phenomenon, in which gene transcripts of different types compete for a finite pool of ribosomes. Throughout, we utilize concepts from the theory of multiclass queues to describe a qualitative shift in protein count statistics as the system transitions from being underloaded (ribosomes exceed transcripts in number) to being overloaded (transcripts exceed ribosomes in number). The exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNAs and ribosomes are fixed, exhibits weak positive correlations between steady-state protein counts when total transcript count slightly exceeds ribosome count, whereas the solution can exhibit strong negative correlations when total transcript count significantly exceeds ribosome count. Extending this analysis, we find approximate but reasonably accurate solutions for a more realistic model, in which abundances of mRNAs and ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations contribute positively and mRNA fluctuations contribute negatively to correlations, and when mRNA fluctuations dominate ribosomal fluctuations, a strong anticorrelation extremum reliably occurs near the transition from the underloaded to the overloaded regime.
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Affiliation(s)
- William H Mather
- Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
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15
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Trosset JY, Carbonell P. Synergistic Synthetic Biology: Units in Concert. Front Bioeng Biotechnol 2013; 1:11. [PMID: 25022769 PMCID: PMC4090895 DOI: 10.3389/fbioe.2013.00011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Accepted: 10/01/2013] [Indexed: 01/31/2023] Open
Abstract
Synthetic biology aims at translating the methods and strategies from engineering into biology in order to streamline the design and construction of biological devices through standardized parts. Modular synthetic biology devices are designed by means of an adequate elimination of cross-talk that makes circuits orthogonal and specific. To that end, synthetic constructs need to be adequately optimized through in silico modeling by choosing the right complement of genetic parts and by experimental tuning through directed evolution and craftsmanship. In this review, we consider an additional and complementary tool available to the synthetic biologist for innovative design and successful construction of desired circuit functionalities: biological synergies. Synergy is a prevalent emergent property in biological systems that arises from the concerted action of multiple factors producing an amplification or cancelation effect compared with individual actions alone. Synergies appear in domains as diverse as those involved in chemical and protein activity, polypharmacology, and metabolic pathway complementarity. In conventional synthetic biology designs, synergistic cross-talk between parts and modules is generally attenuated in order to verify their orthogonality. Synergistic interactions, however, can induce emergent behavior that might prove useful for synthetic biology applications, like in functional circuit design, multi-drug treatment, or in sensing and delivery devices. Synergistic design principles are therefore complementary to those coming from orthogonal design and may provide added value to synthetic biology applications. The appropriate modeling, characterization, and design of synergies between biological parts and units will allow the discovery of yet unforeseeable, novel synthetic biology applications.
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Affiliation(s)
| | - Pablo Carbonell
- BioRetroSynth Laboratory, Institute of Systems and Synthetic Biology, University of Evry-Val d'Essonne , Evry , France ; BioRetroSynth Laboratory, Institute of Systems and Synthetic Biology, CNRS , Evry , France
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16
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Jayanthi S, Nilgiriwala KS, Del Vecchio D. Retroactivity controls the temporal dynamics of gene transcription. ACS Synth Biol 2013; 2:431-41. [PMID: 23654274 DOI: 10.1021/sb300098w] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Just like in many engineering systems, impedance-like effects, called retroactivity, arise at the interconnection of biomolecular circuits, leading to unexpected changes in a circuit's behavior. In this paper, we provide a combined experimental and theoretical study to characterize the effects of retroactivity on the temporal dynamics of a gene transcription module in vivo. The response of the module to an inducer was measured both in isolation and when the module was connected to downstream clients. The connected module, when compared to the isolated module, responded selectively to the introduction of the inducer versus its withdrawal. Specifically, a "sign-sensitive delay" appeared, in which the connected module displayed a time delay in the response to induction and anticipation in the response to de-induction. The extent of these effects can be made larger by increasing the amounts of downstream clients and/or their binding affinity to the output protein of the module. Our experimental results and mathematical formulas make it possible to predict the extent of the change in the dynamic behavior of a module after interconnection. They can be employed to both recover the predictive power of a modular approach to understand systems or as an additional design tool to shape the temporal behavior of gene transcription.
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Affiliation(s)
- Shridhar Jayanthi
- Electrical Engineering, University of Michigan, Ann Arbor, Michigan, United
States
- Mechanical Engineering
Department, Massachusetts Institute of Technology, Cambridge, Massachusetts,
United States
| | - Kayzad Soli Nilgiriwala
- Mechanical Engineering
Department, Massachusetts Institute of Technology, Cambridge, Massachusetts,
United States
| | - Domitilla Del Vecchio
- Mechanical Engineering
Department, Massachusetts Institute of Technology, Cambridge, Massachusetts,
United States
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17
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The energy costs of insulators in biochemical networks. Biophys J 2013; 104:1380-90. [PMID: 23528097 DOI: 10.1016/j.bpj.2013.01.056] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2012] [Revised: 01/24/2013] [Accepted: 01/28/2013] [Indexed: 11/23/2022] Open
Abstract
Complex networks of biochemical reactions, such as intracellular protein signaling pathways and genetic networks, are often conceptualized in terms of modules--semiindependent collections of components that perform a well-defined function and which may be incorporated in multiple pathways. However, due to sequestration of molecular messengers during interactions and other effects, collectively referred to as retroactivity, real biochemical systems do not exhibit perfect modularity. Biochemical signaling pathways can be insulated from impedance and competition effects, which inhibit modularity, through enzymatic futile cycles that consume energy, typically in the form of ATP. We hypothesize that better insulation necessarily requires higher energy consumption. We test this hypothesis through a combined theoretical and computational analysis of a simplified physical model of covalent cycles, using two innovative measures of insulation, as well as a possible new way to characterize optimal insulation through the balancing of these two measures in a Pareto sense. Our results indicate that indeed better insulation requires more energy. While insulation may facilitate evolution by enabling a modular plug-and-play interconnection architecture, allowing for the creation of new behaviors by adding targets to existing pathways, our work suggests that this potential benefit must be balanced against the metabolic costs of insulation necessarily incurred in not affecting the behavior of existing processes.
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Blank LM, Ebert BE. From measurement to implementation of metabolic fluxes. Curr Opin Biotechnol 2012; 24:13-21. [PMID: 23219184 DOI: 10.1016/j.copbio.2012.10.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Revised: 10/26/2012] [Accepted: 10/29/2012] [Indexed: 10/27/2022]
Abstract
The intracellular reaction rates (fluxes) are the ultimate outcome of the activities of the complete inventory (from DNA to metabolite) and in their sum determine the cellular phenotype. The genotype-phenotype relationship is fundamental in such different fields as cancer research and biotechnology. Here, we summarize the developments in determining metabolic fluxes, inferring major pathways from the DNA-sequence, estimating optimal flux distributions, and how these flux distributions can be achieved in vivo. The technical advances to intervene with the many levels of the cellular architecture allow the implementation of new strategies in for example Metabolic Engineering.
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Affiliation(s)
- Lars M Blank
- iAMB - Institute of Applied Microbiology, AABt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany.
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Pasotti L, Politi N, Zucca S, Cusella De Angelis MG, Magni P. Bottom-up engineering of biological systems through standard bricks: a modularity study on basic parts and devices. PLoS One 2012; 7:e39407. [PMID: 22911685 PMCID: PMC3401228 DOI: 10.1371/journal.pone.0039407] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Accepted: 05/24/2012] [Indexed: 11/24/2022] Open
Abstract
Background Modularity is a crucial issue in the engineering world, as it enables engineers to achieve predictable outcomes when different components are interconnected. Synthetic Biology aims to apply key concepts of engineering to design and construct new biological systems that exhibit a predictable behaviour. Even if physical and measurement standards have been recently proposed to facilitate the assembly and characterization of biological components, real modularity is still a major research issue. The success of the bottom-up approach strictly depends on the clear definition of the limits in which biological functions can be predictable. Results The modularity of transcription-based biological components has been investigated in several conditions. First, the activity of a set of promoters was quantified in Escherichia coli via different measurement systems (i.e., different plasmids, reporter genes, ribosome binding sites) relative to an in vivo reference promoter. Second, promoter activity variation was measured when two independent gene expression cassettes were assembled in the same system. Third, the interchangeability of input modules (a set of constitutive promoters and two regulated promoters) connected to a fixed output device (a logic inverter) expressing GFP was evaluated. The three input modules provide tunable transcriptional signals that drive the output device. If modularity persists, identical transcriptional signals trigger identical GFP outputs. To verify this, all the input devices were individually characterized and then the input-output characteristic of the logic inverter was derived in the different configurations. Conclusions Promoters activities (referred to a standard promoter) can vary when they are measured via different reporter devices (up to 22%), when they are used within a two-expression-cassette system (up to 35%) and when they drive another device in a functionally interconnected circuit (up to 44%). This paper provides a significant contribution to the study of modularity limitations in building biological systems by providing useful data on context-dependent variability of biological components.
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Affiliation(s)
- Lorenzo Pasotti
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy
- Centro di Ingegneria Tissutale, Università degli Studi di Pavia, Pavia, Italy
| | - Nicolò Politi
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy
- Centro di Ingegneria Tissutale, Università degli Studi di Pavia, Pavia, Italy
| | - Susanna Zucca
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy
- Centro di Ingegneria Tissutale, Università degli Studi di Pavia, Pavia, Italy
| | | | - Paolo Magni
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy
- Centro di Ingegneria Tissutale, Università degli Studi di Pavia, Pavia, Italy
- * E-mail:
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Cardinale S, Arkin AP. Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems. Biotechnol J 2012; 7:856-66. [PMID: 22649052 PMCID: PMC3440575 DOI: 10.1002/biot.201200085] [Citation(s) in RCA: 251] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Revised: 04/12/2012] [Accepted: 05/04/2012] [Indexed: 12/19/2022]
Abstract
Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multi-cellular organisms find themselves shapes the way they interact, respond to the environment and process external information. Since the birth of the field, synthetic biologists have had to grapple with contextual issues, particularly when the molecular and genetic devices inexplicably fail to function as designed when tested in vivo. In this review, we set out to identify and classify the sources of the unexpected divergences between design and actual function of synthetic systems and analyze possible methodologies aimed at controlling, if not preventing, unwanted contextual issues.
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Affiliation(s)
- Stefano Cardinale
- Physical Biosciences Division, LBNL, Department of Bioengineering, University of California, Berkeley, CA, USA
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Abstract
Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks. Stochastic gene expression at the single cell level can lead to significant phenotypic variation at the population level. To obtain a desired phenotype, the noise levels of intracellular protein concentrations may need to be tuned and controlled. Noise levels often decrease in relative amount as the mean values increase. This implies that the noise levels can be passively controlled by changing the mean values. In an engineering perspective, the noise levels can be further controlled while the mean values can be simultaneously adjusted to desired values. Here, systematic schemes for such simultaneous control are described by identifying where and by how much the system needs to be perturbed. The schemes can be applied to the design process of a potential therapeutic HIV-drug that targets a certain set of reactions that are identified by the proposed analysis, to prevent stochastic transition to the lytic state. In some cases, the simultaneous control cannot be performed efficiently, when the noise levels strongly change with the mean values. This problem is shown to be resolved by applying extra noise and feedback.
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Queueing up for enzymatic processing: correlated signaling through coupled degradation. Mol Syst Biol 2011; 7:561. [PMID: 22186735 PMCID: PMC3737734 DOI: 10.1038/msb.2011.94] [Citation(s) in RCA: 108] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2011] [Accepted: 11/11/2011] [Indexed: 12/13/2022] Open
Abstract
High-throughput technologies have led to the generation of complex wiring diagrams as a post-sequencing paradigm for depicting the interactions between vast and diverse cellular species. While these diagrams are useful for analyzing biological systems on a large scale, a detailed understanding of the molecular mechanisms that underlie the observed network connections is critical for the further development of systems and synthetic biology. Here, we use queueing theory to investigate how 'waiting lines' can lead to correlations between protein 'customers' that are coupled solely through a downstream set of enzymatic 'servers'. Using the E. coli ClpXP degradation machine as a model processing system, we observe significant cross-talk between two networks that are indirectly coupled through a common set of processors. We further illustrate the implications of enzymatic queueing using a synthetic biology application, in which two independent synthetic networks demonstrate synchronized behavior when common ClpXP machinery is overburdened. Our results demonstrate that such post-translational processes can lead to dynamic connections in cellular networks and may provide a mechanistic understanding of existing but currently inexplicable links.
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Jiang P, Ventura AC, Sontag ED, Merajver SD, Ninfa AJ, Vecchio DD. Load-induced modulation of signal transduction networks. Sci Signal 2011; 4:ra67. [PMID: 21990429 PMCID: PMC8760836 DOI: 10.1126/scisignal.2002152] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Biological signal transduction networks are commonly viewed as circuits that pass along information--in the process amplifying signals, enhancing sensitivity, or performing other signal-processing tasks--to transcriptional and other components. Here, we report on a "reverse-causality" phenomenon, which we call load-induced modulation. Through a combination of analytical and experimental tools, we discovered that signaling was modulated, in a surprising way, by downstream targets that receive the signal and, in doing so, apply what in physics is called a load. Specifically, we found that non-intuitive changes in response dynamics occurred for a covalent modification cycle when load was present. Loading altered the response time of a system, depending on whether the activity of one of the enzymes was maximal and the other was operating at its minimal rate or whether both enzymes were operating at submaximal rates. These two conditions, which we call "limit regime" and "intermediate regime," were associated with increased or decreased response times, respectively. The bandwidth, the range of frequency in which the system can process information, decreased in the presence of load, suggesting that downstream targets participate in establishing a balance between noise-filtering capabilities and a circuit's ability to process high-frequency stimulation. Nodes in a signaling network are not independent relay devices, but rather are modulated by their downstream targets.
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Affiliation(s)
- Peng. Jiang
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI
| | - Alejandra C. Ventura
- Institute for Physiology, Molecular Biology, and Neuroscience, Department of Biology/Laboratorio de Fisiología y Biología Molecular, Departamento de Fisiología, Biología Molecular y Celular, IFIBYNE-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires, Argentina
| | | | - Sofia D. Merajver
- Department of Internal Medicine, Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI
| | - Alexander J. Ninfa
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA
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