Engineering artificial gene networks from modular components is a major goal of synthetic biology. However, the construction of gene networks with predictable functions remains hampered by a lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. Here we present an approach that couples libraries of diversified components (synthesized with randomized nonessential sequence) with in silico modeling to guide predictable gene network construction without the need for post hoc tweaking. We demonstrate our approach in Saccharomyces cerevisiae by synthesizing regulatory promoter libraries and using them to construct feed-forward loop networks with different predicted input-output characteristics. We then expand our method to produce a synthetic gene network acting as a predictable timer, modifiable by component choice. We use this network to control the timing of yeast sedimentation, illustrating how the plug-and-play nature of our design can be readily applied to biotechnology.

/pdf/diversity-based-model-guided-construction-of-synthetic-gene-3n46lxkxpk.pdf

Diversity-based, model-guided construction of synthetic gene networks with predicted functions

Although there has been considerable progress in the development of engineering principles for synthetic biology, a substantial challenge is the construction of robust circuits in a noisy cellular environment. Such an environment leads to considerable intercellular variability in circuit behaviour, which can hinder functionality at the colony level. Here we engineer the synchronization of thousands of oscillating colony 'biopixels' over centimetre-length scales through the use of synergistic intercellular coupling involving quorum sensing within a colony and gas-phase redox signalling between colonies. We use this platform to construct a liquid crystal display (LCD)-like macroscopic clock that can be used to sense arsenic via modulation of the oscillatory period. Given the repertoire of sensing capabilities of bacteria such as Escherichia coli, the ability to coordinate their behaviour over large length scales sets the stage for the construction of low cost genetic biosensors that are capable of detecting heavy metals and pathogens in the field.

/pdf/a-sensing-array-of-radically-coupled-genetic-biopixels-10ut4innxe.pdf

A sensing array of radically coupled genetic ‘biopixels’

This Review introduces the core concepts of using biological building blocks to carry out computation. The author explains models of computation, experimental examples — sometimes inspired by the complex computation that is part of natural biological systems — and potential applications.

Biomolecular computing systems: principles, progress and potential

Many bacterial systems rely on dynamic genetic circuits to control crucial biological processes. A major goal of systems biology is to understand these behaviours in terms of individual genes and their interactions. However, traditional techniques based on population averages 'wash out' crucial dynamics that are either unsynchronized between cells or are driven by fluctuations, or 'noise', in cellular components. Recently, the combination of time-lapse microscopy, quantitative image analysis and fluorescent protein reporters has enabled direct observation of multiple cellular components over time in individual cells. In conjunction with mathematical modelling, these techniques are now providing powerful insights into genetic circuit behaviour in diverse microbial systems.

/pdf/using-movies-to-analyse-gene-circuit-dynamics-in-single-13rtfl5kha.pdf

Using movies to analyse gene circuit dynamics in single cells

Secondary metabolites are an important source of high-value chemicals, many of which exhibit important pharmacological properties. These valuable natural products are often difficult to synthesize chemically and are commonly isolated through inefficient extractions from natural biological sources. As such, they are increasingly targeted for production by biosynthesis from engineered microorganisms. The budding yeast species Saccharomyces cerevisiae has proven to be a powerful microorganism for heterologous expression of biosynthetic pathways. S. cerevisiae's usefulness as a host organism is owed in large part to the wealth of knowledge accumulated over more than a century of intense scientific study. Yet many challenges are currently faced in engineering yeast strains for the biosynthesis of complex secondary metabolite production. However, synthetic biology is advancing the development of new tools for constructing, controlling, and optimizing complex metabolic pathways in yeast. Here, we review how the coupling between yeast biology and synthetic biology is advancing the use of S. cerevisiae as a microbial host for the construction of secondary metabolic pathways.

Advancing secondary metabolite biosynthesis in yeast with synthetic biology tools.

Protein decay rates are regulated by degradation machinery that clears unnecessary housekeeping proteins and maintains appropriate dynamic resolution for transcriptional regulators. Turnover rates are also crucial for fluorescence reporters that must strike a balance between sufficient fluorescence for signal detection and temporal resolution for tracking dynamic responses. Here, we use components of the Escherichia coli degradation machinery to construct a Saccharomyces cerevisiae strain that allows for tunable degradation of a tagged protein. Using a microfluidic platform tailored for single-cell fluorescence measurements, we monitor protein decay rates after repression using an ssrA-tagged fluorescent reporter. We observe a half-life ranging from 91 to 22 min, depending on the level of activation of the degradation genes. Computational modeling of the underlying set of enzymatic reactions leads to GFP decay curves that are in excellent agreement with the observations, implying that degradation is governed by Michaelis–Menten-type interactions. In addition to providing a reporter with tunable dynamic resolution, our findings set the stage for explorations of the effect of protein degradation on gene regulatory and signalling pathways.

A synthetic gene network for tuning protein degradation in Saccharomyces cerevisiae.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chris Grilly

Author Tools

Chat about Author

Papers

A synthetic gene network for tuning protein degradation in Saccharomyces cerevisiae.