Curated Papers > Highly Cited Authoritative Literature on "Synthetic Biology"

Tuning response curves for synthetic biology.

Literature Information

DOI	10.1021/sb4000564
PMID	23905721
Journal	ACS synthetic biology
Impact Factor	3.9
JCR Quartile	Q1
Publication Year	2013
Times Cited	63
Keywords	Synthetic Biology, Response Curve, System Design, Regulatory Mechanism, Dynamic Properties
Literature Type	Journal Article
ISSN	2161-5063
Pages	547-67
Issue	2(10)
Authors	Jordan Ang, Edouard Harris, Brendan J Hussey, Richard Kil, David R McMillen

TL;DR

This study explores the tuning of response rates in synthetic biology systems, emphasizing the importance of adjusting response curve shapes to optimize system dynamics and steady-state properties. By reviewing mathematical formulations and categorizing experimental approaches across various regulatory levels, the research provides a framework that enhances the engineering of complex biological systems.

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Synthetic Biology · Response Curve · System Design · Regulatory Mechanism · Dynamic Properties

Abstract

Synthetic biology may be viewed as an effort to establish, formalize, and develop an engineering discipline in the context of biological systems. The ability to tune the properties of individual components is central to the process of system design in all fields of engineering, and synthetic biology is no exception. A large and growing number of approaches have been developed for tuning the responses of cellular systems, and here we address specifically the issue of tuning the rate of response of a system: given a system where an input affects the rate of change of an output, how can the shape of the response curve be altered experimentally? This affects a system's dynamics as well as its steady-state properties, both of which are critical in the design of systems in synthetic biology, particularly those with multiple components. We begin by reviewing a mathematical formulation that captures a broad class of biological response curves and use this to define a standard set of varieties of tuning: vertical shifting, horizontal scaling, and the like. We then survey the experimental literature, classifying the results into our defined categories, and organizing them by regulatory level: transcriptional, post-transcriptional, and post-translational.

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Primary Questions Addressed

1. What are the specific techniques used for vertical shifting and horizontal scaling of response curves in synthetic biology?
2. How do different regulatory levels (transcriptional, post-transcriptional, post-translational) impact the tuning of response curves?
3. What are the implications of altered response curves on the overall dynamics and steady-state properties of synthetic biological systems?
4. Can you provide examples of successful applications where tuning response curves significantly improved the performance of synthetic biological systems?
5. How does the mathematical formulation of biological response curves facilitate experimental design in synthetic biology research?

Key Findings

Research Background and Objectives

Synthetic biology aims to formalize and develop an engineering discipline focused on biological systems. A crucial aspect of this field is the ability to tune the properties of individual components, which is essential for designing complex biological systems. This review addresses the tuning of response curves in cellular systems, specifically how to experimentally alter the shape of these curves in response to various inputs, impacting both the dynamics and steady-state properties of the systems.

Main Methods/Materials/Experimental Design

The review begins with a mathematical formulation to represent biological response curves and categorizes tuning mechanisms into vertical shifting, horizontal scaling, and others. The authors then survey experimental literature, classifying results by regulatory levels: transcriptional, post-transcriptional, and post-translational.

Experimental Design Flowchart

Key Results and Findings

1. Mathematical Modeling: The authors utilize a first-order differential equation to model response curves, which can be approximated by the Hill function, describing the relationship between input concentrations and output response rates.
2. Tuning Mechanisms: Various experimental techniques have been identified for tuning response curves, including:
   • Vertical Scaling: Achieved by altering gene copy numbers or translational efficiency.
   • Vertical Shifting: Introduced by adding constitutive expression sources.
   • Horizontal Scaling: Related to changes in binding affinities of transcription factors.
   • Steepness Modulation: Adjusted by varying the Hill coefficient through cooperative binding effects.
3. Case Studies: Numerous experimental examples illustrate the effectiveness of these tuning mechanisms, demonstrating how different approaches can yield significant variations in gene expression levels.

Main Conclusions/Significance/Innovation

The review emphasizes the importance of tuning response curves in synthetic biology for building complex, reliable systems. The variety of methods and examples discussed highlight the growing toolkit available to synthetic biologists, facilitating the design of biological systems with desired characteristics. The systematic approach to classifying tuning methods provides a framework for future research and application in the field.

Research Limitations and Future Directions

The review acknowledges several limitations, including:

• The complexity and noise inherent in biological systems, which can complicate the predictability of tuning outcomes.
• The need for a deeper understanding of the context-dependence of tuning mechanisms across different organisms and cellular environments.

Future research directions include:

• Developing more robust models to predict the effects of tuning in diverse biological contexts.
• Expanding libraries of genetic parts and tuning methods to enhance modularity and consistency in synthetic biology applications.

References

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2. The second wave of synthetic biology: from modules to systems. - Priscilla E M Purnick;Ron Weiss - Nature reviews. Molecular cell biology (2009)
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6. Logic integration of mRNA signals by an RNAi-based molecular computer. - Zhen Xie;Siyuan John Liu;Leonidas Bleris;Yaakov Benenson - Nucleic acids research (2010)
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Literatures Citing This Work

1. Principles of genetic circuit design. - Jennifer A N Brophy;Christopher A Voigt - Nature methods (2014)
2. Accurate, model-based tuning of synthetic gene expression using introns in S. cerevisiae. - Ido Yofe;Zohar Zafrir;Rachel Blau;Maya Schuldiner;Tamir Tuller;Ehud Shapiro;Tuval Ben-Yehezkel - PLoS genetics (2014)
3. Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. - Peng Xu;Lingyun Li;Fuming Zhang;Gregory Stephanopoulos;Mattheos Koffas - Proceedings of the National Academy of Sciences of the United States of America (2014)
4. Design and characterization of a dual-mode promoter with activation and repression capability for tuning gene expression in yeast. - Mostafizur Mazumder;David R McMillen - Nucleic acids research (2014)
5. Advances and computational tools towards predictable design in biological engineering. - Lorenzo Pasotti;Susanna Zucca - Computational and mathematical methods in medicine (2014)
6. Impact of upstream and downstream constraints on a signaling module's ultrasensitivity. - Edgar Altszyler;Alejandra Ventura;Alejandro Colman-Lerner;Ariel Chernomoretz - Physical biology (2014)
7. Synthetic biology outside the cell: linking computational tools to cell-free systems. - Daniel D Lewis;Fernando D Villarreal;Fan Wu;Cheemeng Tan - Frontiers in bioengineering and biotechnology (2014)
8. Amplification of small molecule-inducible gene expression via tuning of intracellular receptor densities. - Baojun Wang;Mauricio Barahona;Martin Buck - Nucleic acids research (2015)
9. Mechanistic links between cellular trade-offs, gene expression, and growth. - Andrea Y Weiße;Diego A Oyarzún;Vincent Danos;Peter S Swain - Proceedings of the National Academy of Sciences of the United States of America (2015)
10. Modelling the effects of cell-to-cell variability on the output of interconnected gene networks in bacterial populations. - Nicolò Politi;Lorenzo Pasotti;Susanna Zucca;Paolo Magni - BMC systems biology (2015)

... (53 more literatures)

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