1
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Riley AT, Robson JM, Green AA. Generative and predictive neural networks for the design of functional RNA molecules. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.549043. [PMID: 37503279 PMCID: PMC10370010 DOI: 10.1101/2023.07.14.549043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.
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Affiliation(s)
- Aidan T. Riley
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
| | - James M. Robson
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Alexander A. Green
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
- Molecular Biology, Cell Biology & Biochemistry Program, Graduate School of Arts and Sciences, Boston University, Boston, MA 02215, USA
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2
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Layered feedback control overcomes performance trade-off in synthetic biomolecular networks. Nat Commun 2022; 13:5393. [PMID: 36104365 PMCID: PMC9474519 DOI: 10.1038/s41467-022-33058-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 08/31/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractLayered feedback is an optimization strategy in feedback control designs widely used in engineering. Control theory suggests that layering multiple feedbacks could overcome the robustness-speed performance trade-off limit. In natural biological networks, genes are often regulated in layers to adapt to environmental perturbations. It is hypothesized layering architecture could also overcome the robustness-speed performance trade-off in genetic networks. In this work, we validate this hypothesis with a synthetic biomolecular network in living E. coli cells. We start with system dynamics analysis using models of various complexities to guide the design of a layered control architecture in living cells. Experimentally, we interrogate system dynamics under three groups of perturbations. We consistently observe that the layered control improves system performance in the robustness-speed domain. This work confirms that layered control could be adopted in synthetic biomolecular networks for performance optimization. It also provides insights into understanding genetic feedback control architectures in nature.
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3
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Romantseva E, Alperovich N, Ross D, Lund SP, Strychalski EA. Effects of DNA template preparation on variability in cell-free protein production. Synth Biol (Oxf) 2022; 7:ysac015. [PMID: 36046152 PMCID: PMC9425043 DOI: 10.1093/synbio/ysac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 07/01/2022] [Accepted: 08/12/2022] [Indexed: 08/08/2023] Open
Abstract
DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.
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Affiliation(s)
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD USA
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Steven P Lund
- National Institute of Standards and Technology, Gaithersburg, MD USA
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4
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Poole W, Pandey A, Shur A, Tuza ZA, Murray RM. BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts. PLoS Comput Biol 2022; 18:e1009987. [PMID: 35442944 PMCID: PMC9060376 DOI: 10.1371/journal.pcbi.1009987] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 05/02/2022] [Accepted: 03/03/2022] [Indexed: 11/23/2022] Open
Abstract
Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions. This paper describes a new software package BioCRNpyler (pronounced “Biocompiler”) designed to support rapid development and exploration of mathematical models of biochemical networks and circuits by computational biologists, systems biologists, and synthetic biologists. BioCRNpyler allows its users to generate large complex models using very few lines of code in a way that is modular. To do this, BioCRNpyler uses a powerful new representation of biochemical circuits which defines their parts, underlying biochemical mechanisms, and chemical context independently. BioCRNpyler was developed as a Python scripting language designed to be accessible to beginning users as well as easily extendable and customizable for advanced users. Ultimately, we see Biocrnpyler being used to accelerate computer automated design of biochemical circuits and model driven hypothesis generation in biology.
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Affiliation(s)
- William Poole
- Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
| | - Ayush Pandey
- Control and Dynamical Systems, California Institute of Technology, Pasadena, California, United States of America
| | - Andrey Shur
- Bioengineering, California Institute of Technology, Pasadena, California, United States of America
| | - Zoltan A. Tuza
- Bioengineering, Imperial College London, London, England
| | - Richard M. Murray
- Control and Dynamical Systems, California Institute of Technology, Pasadena, California, United States of America
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5
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Romantseva EF, Tack DS, Alperovich N, Ross D, Strychalski EA. Best Practices for DNA Template Preparation Toward Improved Reproducibility in Cell-Free Protein Production. Methods Mol Biol 2022; 2433:3-50. [PMID: 34985735 DOI: 10.1007/978-1-0716-1998-8_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Performance variability is a common challenge in cell-free protein production and hinders a wider adoption of these systems for both research and biomanufacturing. While the inherent stochasticity and complexity of biology likely contributes to variability, other systematic factors may also play a role, including the source and preparation of the cell extract, the composition of the supplemental reaction buffer, the facility at which experiments are conducted, and the human operator (Cole et al. ACS Synth Biol 8:2080-2091, 2019). Variability in protein production could also arise from differences in the DNA template-specifically the amount of functional DNA added to a cell-free reaction and the quality of the DNA preparation in terms of contaminants and strand breakage. Here, we present protocols and suggest best practices optimized for DNA template preparation and quantitation for cell-free systems toward reducing variability in cell-free protein production.
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Affiliation(s)
| | - Drew S Tack
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, USA
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6
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Lehr FX, Kuzembayeva A, Bailey ME, Kleindienst W, Kabisch J, Koeppl H. Functionalizing Cell-Free Systems with CRISPR-Associated Proteins: Application to RNA-Based Circuit Engineering. ACS Synth Biol 2021; 10:2138-2150. [PMID: 34383464 DOI: 10.1021/acssynbio.0c00386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Cell-free systems have become a compelling choice for the prototyping of synthetic circuits. Many robust protocols for preparing cell-free systems are now available along with toolboxes designed for a variety of applications. Thus far, the production of cell-free extracts has often been decoupled from the production of functionalized proteins. Here, we leveraged a recent protocol for producing an E. coli-based cell-free expression system with two CRISPR-associated proteins, Csy4 and dCas9, expressed prior to harvest. We found that pre-expression did not affect the resulting extract performance, and the final concentrations of the endonucleases matched the level required for synthetic circuit prototyping. We demonstrated the benefits and versatility of dCas9 and Csy4 through the use of RNA circuitry based on a combination of single guide RNAs, small transcriptional activator RNAs, and toehold switches. For instance, we show that Csy4 processing increased 4-fold the dynamic range of a previously published AND-logic gate. Additionally, blending the CRISPR-enhanced extracts enabled us to reduce leakage in a multiple inputs gate, and to extend the type of Boolean functions available for RNA-based circuits, such as NAND-logic. Finally, we reported the use of simultaneous transcriptional and translational reporters in our RNA-based circuits. In particular, the AND-gate mRNA and protein levels were able to be independently monitored in response to transcriptional and translational activators. We hope this work will facilitate the adoption of advanced processing tools for RNA-based circuit prototyping in a cell-free environment.
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Affiliation(s)
- François-Xavier Lehr
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt 64287, Germany
- Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt 64283, Germany
| | - Alina Kuzembayeva
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt 64287, Germany
- Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt 64283, Germany
| | - Megan E Bailey
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt 64287, Germany
- Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt 64283, Germany
| | - Werner Kleindienst
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt 64287, Germany
| | - Johannes Kabisch
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt 64287, Germany
| | - Heinz Koeppl
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt 64287, Germany
- Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt 64283, Germany
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7
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Adhikari A, Vilkhovoy M, Vadhin S, Lim HE, Varner JD. Effective Biophysical Modeling of Cell Free Transcription and Translation Processes. Front Bioeng Biotechnol 2020; 8:539081. [PMID: 33324619 PMCID: PMC7726328 DOI: 10.3389/fbioe.2020.539081] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 11/02/2020] [Indexed: 12/18/2022] Open
Abstract
Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.
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Affiliation(s)
- Abhinav Adhikari
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
| | - Michael Vilkhovoy
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
| | - Sandra Vadhin
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
| | - Ha Eun Lim
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
| | - Jeffrey D Varner
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
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8
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Reis AC, Salis HM. An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models. ACS Synth Biol 2020; 9:3145-3156. [PMID: 33054181 DOI: 10.1021/acssynbio.0c00394] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions remains a significant challenge. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce model capacity, a new information theoretic metric for correct cross-data-set comparisons. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We then applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.
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9
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Laohakunakorn N. Cell-Free Systems: A Proving Ground for Rational Biodesign. Front Bioeng Biotechnol 2020; 8:788. [PMID: 32793570 PMCID: PMC7393481 DOI: 10.3389/fbioe.2020.00788] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/22/2020] [Indexed: 11/13/2022] Open
Abstract
Cell-free gene expression systems present an alternative approach to synthetic biology, where biological gene expression is harnessed inside non-living, in vitro biochemical reactions. Taking advantage of a plethora of recent experimental innovations, they easily overcome certain challenges for computer-aided biological design. For instance, their open nature renders all their components directly accessible, greatly facilitating model construction and validation. At the same time, these systems present their own unique difficulties, such as limited reaction lifetimes and lack of homeostasis. In this Perspective, I propose that cell-free systems are an ideal proving ground to test rational biodesign strategies, as demonstrated by a small but growing number of examples of model-guided, forward engineered cell-free biosystems. It is likely that advances gained from this approach will contribute to our efforts to more reliably and systematically engineer both cell-free as well as living cellular systems for useful applications.
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Affiliation(s)
- Nadanai Laohakunakorn
- School of Biological Sciences, Institute of Quantitative Biology, Biochemistry, and Biotechnology, University of Edinburgh, Edinburgh, United Kingdom
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10
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Abstract
Cell-free systems are a widely used research tool in systems and synthetic biology and a promising platform for manufacturing of proteins and chemicals. In the past, cell-free biology was primarily used to better understand fundamental biochemical processes. Notably, E. coli cell-free extracts were used in the 1960s to decipher the sequencing of the genetic code. Since then, the transcription and translation capabilities of cell-free systems have been repeatedly optimized to improve energy efficiency and product yield. Today, cell-free systems, in combination with the rise of synthetic biology, have taken on a new role as a promising technology for just-in-time manufacturing of therapeutically important biologics and high-value small molecules. They have also been implemented at an industrial scale for the production of antibodies and cytokines. In this review, we discuss the evolution of cell-free technologies, in particular advancements in extract preparation, cell-free protein synthesis, and cell-free metabolic engineering applications. We then conclude with a discussion of the mathematical modeling of cell-free systems. Mathematical modeling of cell-free processes could be critical to addressing performance bottlenecks and estimating the costs of cell-free manufactured products.
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11
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Agrawal DK, Marshall R, Noireaux V, Sontag ED. In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller. Nat Commun 2019; 10:5760. [PMID: 31848346 PMCID: PMC6917713 DOI: 10.1038/s41467-019-13626-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
Feedback mechanisms play a critical role in the maintenance of cell homeostasis in the presence of disturbances and uncertainties. Motivated by the need to tune the dynamics and improve the robustness of gene circuits, biological engineers have proposed various designs that mimic natural molecular feedback control mechanisms. However, practical and predictable implementations have proved challenging because of the complexity of synthesis and analysis of complex biomolecular networks. Here, we analyze and experimentally validate a synthetic biomolecular controller executed in vitro. The controller ensures that gene expression rate tracks an externally imposed reference level, and achieves this goal even in the presence of certain kinds of disturbances. Our design relies upon an analog of the well-known principle of integral feedback in control theory. We implement the controller in an Escherichia coli cell-free transcription-translation system, which allows rapid prototyping and implementation. Modeling and theory guide experimental implementation with well-defined operational predictability.
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Affiliation(s)
- Deepak K Agrawal
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Ryan Marshall
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Vincent Noireaux
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Eduardo D Sontag
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston MA, USA.
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12
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Horvath N, Vilkhovoy M, Wayman JA, Calhoun K, Swartz J, Varner JD. Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli. Metab Eng Commun 2019; 10:e00113. [PMID: 32280586 PMCID: PMC7136494 DOI: 10.1016/j.mec.2019.e00113] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 10/15/2019] [Accepted: 11/19/2019] [Indexed: 11/09/2022] Open
Abstract
In this study, we developed a dynamic mathematical model of E. coli cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and could provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis. Protein production is biphasic, powered initially by glucose and later by pyruvate. Protein is produced with an energy efficiency of only 12%. Protein productivity is most sensitive to oxidative phosphorylation and glycolysis. Protein production is robust to allosteric control.
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Affiliation(s)
- Nicholas Horvath
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA
| | - Michael Vilkhovoy
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA
| | - Joseph A Wayman
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY, 14853, USA
| | - Kara Calhoun
- School of Chemical Engineering, Stanford University, Stanford, CA, 94395, USA
| | - James Swartz
- School of Chemical Engineering, Stanford University, Stanford, CA, 94395, USA
| | - Jeffrey D Varner
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA
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13
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Lehr FX, Hanst M, Vogel M, Kremer J, Göringer HU, Suess B, Koeppl H. Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators. ACS Synth Biol 2019; 8:2163-2173. [PMID: 31393707 DOI: 10.1021/acssynbio.9b00238] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages such as short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multilevel regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterize the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analyzed a prototype gate in vitro as well as in silico, employing parametrized ordinary differential equations (ODEs), for which parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. On the basis of this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.
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Affiliation(s)
- François-Xavier Lehr
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Maleen Hanst
- Department of Electrical Engineering, Technische Universität Darmstadt, 64283 Darmstadt, Germany
| | - Marc Vogel
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Jennifer Kremer
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - H. Ulrich Göringer
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Beatrix Suess
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Heinz Koeppl
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
- Department of Electrical Engineering, Technische Universität Darmstadt, 64283 Darmstadt, Germany
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14
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McNerney MP, Zhang Y, Steppe P, Silverman AD, Jewett MC, Styczynski MP. Point-of-care biomarker quantification enabled by sample-specific calibration. SCIENCE ADVANCES 2019; 5:eaax4473. [PMID: 31579825 PMCID: PMC6760921 DOI: 10.1126/sciadv.aax4473] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 08/27/2019] [Indexed: 05/22/2023]
Abstract
Easy-to-perform, relatively inexpensive blood diagnostics have transformed at-home healthcare for some patients, but they require analytical equipment and are not easily adapted to measuring other biomarkers. The requirement for reliable quantification in complex sample types (such as blood) has been a critical roadblock in developing and deploying inexpensive, minimal-equipment diagnostics. Here, we developed a platform for inexpensive, easy-to-use diagnostics that uses cell-free expression to generate colored readouts that are visible to the naked eye, yet quantitative and robust to the interference effects seen in complex samples. We achieved this via a parallelized calibration scheme that uses the patient sample to generate custom reference curves. We used this approach to quantify a clinically relevant micronutrient and to quantify nucleic acids, demonstrating a generalizable platform for low-cost quantitative diagnostics.
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Affiliation(s)
- Monica P. McNerney
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA
| | - Yan Zhang
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA
| | - Paige Steppe
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA
| | - Adam D. Silverman
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
| | - Michael C. Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
- Simpson Querrey Institute, Northwestern University, Chicago, IL 60611, USA
| | - Mark P. Styczynski
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA
- Corresponding author.
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15
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Garenne D, Noireaux V. Cell-free transcription–translation: engineering biology from the nanometer to the millimeter scale. Curr Opin Biotechnol 2019; 58:19-27. [DOI: 10.1016/j.copbio.2018.10.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 10/14/2018] [Indexed: 01/01/2023]
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16
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Jeong D, Klocke M, Agarwal S, Kim J, Choi S, Franco E, Kim J. Cell-Free Synthetic Biology Platform for Engineering Synthetic Biological Circuits and Systems. Methods Protoc 2019; 2:E39. [PMID: 31164618 PMCID: PMC6632179 DOI: 10.3390/mps2020039] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/12/2019] [Accepted: 05/08/2019] [Indexed: 01/07/2023] Open
Abstract
Synthetic biology brings 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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Affiliation(s)
- Dohyun Jeong
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
| | - Melissa Klocke
- Department of Mechanical Engineering, University of California at Riverside, 900 University Ave, Riverside, CA 92521, USA.
| | - Siddharth Agarwal
- Department of Mechanical Engineering, University of California at Riverside, 900 University Ave, Riverside, CA 92521, USA.
| | - Jeongwon Kim
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
| | - Seungdo Choi
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
| | - Elisa Franco
- Department of Mechanical and Aerospace Engineering, University of California at Los Angeles, 420 Westwood Plaza, Los Angeles, CA 90095, USA.
| | - Jongmin Kim
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Gyeongbuk 37673, Korea.
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17
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Evolving methods for rational de novo design of functional RNA molecules. Methods 2019; 161:54-63. [PMID: 31059832 DOI: 10.1016/j.ymeth.2019.04.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/26/2019] [Accepted: 04/29/2019] [Indexed: 12/16/2022] Open
Abstract
Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.
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18
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Westbrook A, Tang X, Marshall R, Maxwell CS, Chappell J, Agrawal DK, Dunlop MJ, Noireaux V, Beisel CL, Lucks J, Franco E. Distinct timescales of RNA regulators enable the construction of a genetic pulse generator. Biotechnol Bioeng 2019; 116:1139-1151. [DOI: 10.1002/bit.26918] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/25/2018] [Accepted: 01/06/2019] [Indexed: 01/01/2023]
Affiliation(s)
- Alexandra Westbrook
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University Ithaca New York
| | - Xun Tang
- Department of Mechanical Engineering University of California at Riverside Riverside California
| | - Ryan Marshall
- School of Physics and Astronomy, University of Minnesota Minneapolis Minnesota
| | - Colin S. Maxwell
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh North Carolina
| | | | - Deepak K. Agrawal
- Biomedical Engineering Department Boston University Boston Massachusetts
| | - Mary J. Dunlop
- Biomedical Engineering Department Boston University Boston Massachusetts
| | - Vincent Noireaux
- School of Physics and Astronomy, University of Minnesota Minneapolis Minnesota
| | - Chase L. Beisel
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh North Carolina
- Helmholtz Institute for RNA‐based Infection Research (HIRI) Helmholtz‐Centre for Infection Research (HZI), Würzburg Germany
- Faculty of Medicine, University of Würzburg Würzburg Germany
| | - Julius Lucks
- Department of Chemical and Biological Engineering Northwestern University Evanston Illinois
| | - Elisa Franco
- Department of Mechanical Engineering University of California at Riverside Riverside California
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19
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Vilkhovoy M, Horvath N, Shih CH, Wayman JA, Calhoun K, Swartz J, Varner JD. Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis. ACS Synth Biol 2018; 7:1844-1857. [PMID: 29944340 DOI: 10.1021/acssynbio.7b00465] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis, and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature; thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of carbon number and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multiprotein synthetic circuits, RNA circuits, or the cell-free production of small molecule products.
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Affiliation(s)
- Michael Vilkhovoy
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Nicholas Horvath
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Che-Hsiao Shih
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Joseph A. Wayman
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Kara Calhoun
- School of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - James Swartz
- School of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Jeffrey D. Varner
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
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20
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Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design. ACS Synth Biol 2018; 7:1507-1518. [PMID: 29733627 DOI: 10.1021/acssynbio.7b00440] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.
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21
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Senoussi A, Lee Tin Wah J, Shimizu Y, Robert J, Jaramillo A, Findeiss S, Axmann IM, Estevez-Torres A. Quantitative Characterization of Translational Riboregulators Using an in Vitro Transcription-Translation System. ACS Synth Biol 2018; 7:1269-1278. [PMID: 29617125 DOI: 10.1021/acssynbio.7b00387] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Riboregulators are short RNA sequences that, upon binding to a ligand, change their secondary structure and influence the expression rate of a downstream gene. They constitute an attractive alternative to transcription factors for building synthetic gene regulatory networks because they can be engineered de novo. However, riboregulators are generally designed in silico and tested in vivo, which provides little quantitative information about their performances, thus hindering the improvement of design algorithms. Here we show that a cell-free transcription-translation (TX-TL) system provides valuable information about the performances of in silico designed riboregulators. We first propose a simple model that provides a quantitative definition of the dynamic range of a riboregulator. We further characterize two types of translational riboregulators composed of a cis-repressed (cr) and a trans-activating (ta) strand. At the DNA level we demonstrate that high concentrations of taDNA poisoned the activator until total shut off, in agreement with our model, and that relative dynamic ranges of riboregulators determined in vitro are in agreement with published in vivo data. At the RNA level, we show that this approach provides a fast and simple way to measure dissociation constants of functional riboregulators, in contrast to standard mobility-shift assays. Our method opens the route for using cell-free TX-TL systems for the quantitative characterization of functional riboregulators in order to improve their design in silico.
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Affiliation(s)
- Anis Senoussi
- Sorbonne Université and CNRS, Laboratoire Jean Perrin, F-75005, Paris, France
| | | | - Yoshihiro Shimizu
- Laboratory for Cell-Free Protein Synthesis, RIKEN Quantitative Biology Center, Osaka 565-0874, Japan
| | - Jérôme Robert
- Sorbonne Université and CNRS, Laboratoire Jean Perrin, F-75005, Paris, France
| | - Alfonso Jaramillo
- Warwick Integrative Synthetic Biology Centre and School of Life Sciences, University of Warwick, CV4 7AL, Coventry, U.K
- CNRS Laboratoire iSSB, Université Paris-Saclay, Université d’ Évry and CEA, DRF, IG, Genoscope, F-91000 Évry, France
- Institute for Integrative Systems Biology, University of Valencia-CSIC, 46980 Paterna, Spain
| | - Sven Findeiss
- Dept. Computer Science and ICB, University Leipzig, D-04107 Leipzig, Germany
- University of Vienna, Faculties of Computer Science and Chemistry, Dept. of Theoretical Chemistry, A-1090 Vienna, Austria
| | - Ilka M. Axmann
- Institute for Synthetic Microbiology and CEPLAS, Heinrich Heine University Düsseldorf, D-40225 Düsseldorf, Germany
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22
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Agrawal DK, Tang X, Westbrook A, Marshall R, Maxwell CS, Lucks J, Noireaux V, Beisel CL, Dunlop MJ, Franco E. Mathematical Modeling of RNA-Based Architectures for Closed Loop Control of Gene Expression. ACS Synth Biol 2018; 7:1219-1228. [PMID: 29709170 DOI: 10.1021/acssynbio.8b00040] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Feedback allows biological systems to control gene expression precisely and reliably, even in the presence of uncertainty, by sensing and processing environmental changes. Taking inspiration from natural architectures, synthetic biologists have engineered feedback loops to tune the dynamics and improve the robustness and predictability of gene expression. However, experimental implementations of biomolecular control systems are still far from satisfying performance specifications typically achieved by electrical or mechanical control systems. To address this gap, we present mathematical models of biomolecular controllers that enable reference tracking, disturbance rejection, and tuning of the temporal response of gene expression. These controllers employ RNA transcriptional regulators to achieve closed loop control where feedback is introduced via molecular sequestration. Sensitivity analysis of the models allows us to identify which parameters influence the transient and steady state response of a target gene expression process, as well as which biologically plausible parameter values enable perfect reference tracking. We quantify performance using typical control theory metrics to characterize response properties and provide clear selection guidelines for practical applications. Our results indicate that RNA regulators are well-suited for building robust and precise feedback controllers for gene expression. Additionally, our approach illustrates several quantitative methods useful for assessing the performance of biomolecular feedback control systems.
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Affiliation(s)
- Deepak K. Agrawal
- Biomedical Engineering Department, Boston University, Boston, Massachusetts 02215, United States
| | - Xun Tang
- Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States
| | - Alexandra Westbrook
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Ryan Marshall
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Colin S. Maxwell
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Julius Lucks
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Vincent Noireaux
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Chase L. Beisel
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
- Helmholtz Institute for RNA-Based Infection Research (HIRI), Josef-Schneider-Str. 2/D15, D-97080 Würzburg, Germany
| | - Mary J. Dunlop
- Biomedical Engineering Department, Boston University, Boston, Massachusetts 02215, United States
| | - Elisa Franco
- Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States
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23
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Jayaraman P, Yeoh JW, Jayaraman S, Teh AY, Zhang J, Poh CL. Cell-Free Optogenetic Gene Expression System. ACS Synth Biol 2018; 7:986-994. [PMID: 29596741 DOI: 10.1021/acssynbio.7b00422] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Optogenetic tools provide a new and efficient way to dynamically program gene expression with unmatched spatiotemporal precision. To date, their vast potential remains untapped in the field of cell-free synthetic biology, largely due to the lack of simple and efficient light-switchable systems. Here, to bridge the gap between cell-free systems and optogenetics, we studied our previously engineered one component-based blue light-inducible Escherichia coli promoter in a cell-free environment through experimental characterization and mathematical modeling. We achieved >10-fold dynamic expression and demonstrated rapid and reversible activation of the target gene to generate oscillatory response. The deterministic model developed was able to recapitulate the system behavior and helped to provide quantitative insights to optimize dynamic response. This in vitro optogenetic approach could be a powerful new high-throughput screening technology for rapid prototyping of complex biological networks in both space and time without the need for chemical induction.
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24
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Westbrook AM, Lucks JB. Achieving large dynamic range control of gene expression with a compact RNA transcription-translation regulator. Nucleic Acids Res 2017; 45:5614-5624. [PMID: 28387839 PMCID: PMC5435934 DOI: 10.1093/nar/gkx215] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 03/23/2017] [Indexed: 12/14/2022] Open
Abstract
RNA transcriptional regulators are emerging as versatile components for genetic network construction. However, these regulators suffer from incomplete repression in their OFF state, making their dynamic range less than that of their protein counterparts. This incomplete repression causes expression leak, which impedes the construction of larger synthetic regulatory networks as leak propagation can interfere with desired network function. To address this, we demonstrate how naturally derived antisense RNA-mediated transcriptional regulators can be configured to regulate both transcription and translation in a single compact RNA mechanism that functions in Escherichia coli. Using in vivo gene expression assays, we show that a combination of transcriptional termination and ribosome binding site sequestration increases repression from 85% to 98%, or activation from 10-fold to over 900-fold, in response to cognate antisense RNAs. We also show that orthogonal repressive versions of this mechanism can be created through engineering minimal antisense RNAs. Finally, to demonstrate the utility of this mechanism, we use it to reduce network leak in an RNA-only cascade. We anticipate these regulators will find broad use as synthetic biology moves beyond parts engineering to the design and construction of more sophisticated regulatory networks.
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Affiliation(s)
- Alexandra M Westbrook
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Julius B Lucks
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
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25
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Tsigkinopoulou A, Baker SM, Breitling R. Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology. Trends Biotechnol 2017; 35:518-529. [PMID: 28094080 DOI: 10.1016/j.tibtech.2016.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 12/05/2016] [Accepted: 12/15/2016] [Indexed: 10/20/2022]
Abstract
Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.
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Affiliation(s)
- Areti Tsigkinopoulou
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - Syed Murtuza Baker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
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26
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Gould R, Bassen DM, Chakrabarti A, Varner JD, Butcher J. Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1. PLoS Comput Biol 2016; 12:e1005251. [PMID: 28027307 PMCID: PMC5189931 DOI: 10.1371/journal.pcbi.1005251] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Accepted: 11/17/2016] [Indexed: 12/22/2022] Open
Abstract
Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor β (TGF-β) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-β induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-β exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-β and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-β and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-β treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.
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Affiliation(s)
- Russell Gould
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America
| | - David M. Bassen
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America
| | - Anirikh Chakrabarti
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Jeffrey D. Varner
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Jonathan Butcher
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America
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27
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Kushwaha M, Rostain W, Prakash S, Duncan JN, Jaramillo A. Using RNA as Molecular Code for Programming Cellular Function. ACS Synth Biol 2016; 5:795-809. [PMID: 26999422 DOI: 10.1021/acssynbio.5b00297] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
RNA is involved in a wide-range of important molecular processes in the cell, serving diverse functions: regulatory, enzymatic, and structural. Together with its ease and predictability of design, these properties can lead RNA to become a useful handle for biological engineers with which to control the cellular machinery. By modifying the many RNA links in cellular processes, it is possible to reprogram cells toward specific design goals. We propose that RNA can be viewed as a molecular programming language that, together with protein-based execution platforms, can be used to rewrite wide ranging aspects of cellular function. In this review, we catalogue developments in the use of RNA parts, methods, and associated computational models that have contributed to the programmability of biology. We discuss how RNA part repertoires have been combined to build complex genetic circuits, and review recent applications of RNA-based parts and circuitry. We explore the future potential of RNA engineering and posit that RNA programmability is an important resource for firmly establishing an era of rationally designed synthetic biology.
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Affiliation(s)
- Manish Kushwaha
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
| | - William Rostain
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
- iSSB, Genopole,
CNRS, UEVE, Université Paris-Saclay, Évry, France
| | - Satya Prakash
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
| | - John N. Duncan
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
| | - Alfonso Jaramillo
- Warwick
Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry, CV4 7AL, U.K
- iSSB, Genopole,
CNRS, UEVE, Université Paris-Saclay, Évry, France
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