SEARCH · bioRxiv Science

Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

bioRxiv2018

High throughput gene expression profiling of yeast colonies with microgel-culture Drop-seq

Yeasts can be engineered into \"living foundries\" for non-natural chemical production by reprogramming their genome using a synthetic biology \"design-build-test\" cycle. While methods for \"design\" and \"build\" are scalable and efficient, \"test\" remains a labor-intensive bottleneck, limiting the effectiveness of the genetic reprogramming results. Here we describe Isogenic Colony Sequencing (ICO-seq), a massively-parallel strategy to assess the gene expression, and thus engineered pathway efficacy, of large numbers of genetically distinct yeast colonies. We use the approach to characterize opaque-white switching in 658 C. albicans colonies. By profiling transcriptomes of 1642 engineered S. cerevisiae strains, we use it to assess gene expression heterogeneity in a protein mutagenesis library. Our approach will accelerate synthetic biology by allowing facile and cost-effective transcriptional profiling of large numbers of genetically distinct yeast strains.

bioengineering↗

bioRxiv2017

Enhanced Guide-RNA Design And Targeting Analysis For Precise CRISPR Genome Editing Of Single And Consortia Of Industrially Relevant And Non-Model Organisms

MotivationGenetic diversity of non-model organisms offers a repertoire of unique phenotypic features for exploration and cultivation for synthetic biology and metabolic engineering applications. To realize this enormous potential, it is critical to have an efficient genome editing tool for rapid strain engineering of these organisms to perform novel programmed functions.\n\nResultsTo accommodate the use of CRISPR/Cas systems for genome editing across organisms, we have developed a novel method, named CASPER (CRISPR Associated Software for Pathway Engineering and Research), for identifying on- and off-targets with enhanced predictability coupled with an analysis of non-unique (repeated) targets to assist in editing any organism with various endonucleases. Utilizing CASPER, we demonstrated a modest 2.4% and significant 30.2% improvement (F-test, p<0.05) over the conventional methods for predicting on- and off-target activities, respectively. Further we used CASPER to develop novel applications in genome editing: multitargeting analysis (i.e. simultaneous multiple-site modification on a target genome with a sole guide-RNA (gRNA) requirement) and multispecies population analysis (i.e. gRNA design for genome editing across a consortium of organisms). Our analysis on a selection of industrially relevant organisms revealed a number of non-unique target sites associated with genes and transposable elements that can be used as potential sites for multitargeting. The analysis also identified shared and unshared targets that enable genome editing of single or multiple genomes in a consortium of interest. We envision CASPER as a useful platform to enhance the precise CRISPR genome editing for metabolic engineering and synthetic biology applications.

bioinformatics↗

bioRxiv2015

Characterizing and Prototyping Genetic Networks with Cell-Free Transcription-Translation Reactions

A central goal of synthetic biology is to engineer cellular behavior by engineering synthetic gene networks for a variety of biotechnology and medical applications. The process of engineering gene networks often involves an iterative design-build-test cycle, whereby the parts and connections that make up the network are built, characterized and varied until the desired network function is reached. Many advances have been made in the design and build portions of this cycle. However, the slow process of in vivo characterization of network function often limits the timescale of the testing step. Cell-free transcription-translation (TX-TL) systems offer a simple and fast alternative to performing these characterizations in cells. Here we provide an overview of a cell-free TX-TL system that utilizes the native Escherichia coli TX-TL machinery, thereby allowing a large repertoire of parts and networks to be characterized. As a way to demonstrate the utility of cell-free TX-TL, we illustrate the characterization of two genetic networks: an RNA transcriptional cascade and a protein regulated incoherent feed-forward loop. We also provide guidelines for designing TX-TL experiments to characterize new genetic networks. We end with a discussion of current and emerging applications of cell free systems.\n\nAbbreviations

Synthetic Biology↗

bioRxiv2017

Engineering a Functional small RNA Negative Autoregulation Network with Model-guided Design

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 noise. 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 in vivo and show that our sRNA transcriptional network reduces both network response time and noise in steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

synthetic biology↗

bioRxiv2018

Establishing a cell-free Vibrio natriegens expression system

The fast growing bacterium Vibrio natriegens is an emerging microbial host for biotechnology. Harnessing its productive cellular components may offer a compelling platform for rapid protein production and prototyping of metabolic pathways or genetic circuits. Here, we report the development of a V. natriegens cell-free expression system. We devised a simplified crude extract preparation protocol and achieved >260 g/mL of super-folder GFP in a small-scale batch reaction after three hours. Culturing conditions, including growth media and cell density, significantly affect translation kinetics and protein yield of extracts. We observed maximal protein yield at incubation temperatures of 26{degrees}C or 30{degrees}C, and show improved yield by tuning ions crucial for ribosomal stability. This work establishes an initial V. natriegens cell-free expression system, enables probing of V. natriegens biology, and will serve as a platform to accelerate metabolic engineering and synthetic biology applications.

synthetic biology↗

bioRxiv2018

Tuning gene expression variability and multi-gene regulation by dynamic transcription factor control

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Using a fast-acting, light-responsive transcription factor in Saccharomyces cerevisiae, we show that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be precisely and independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to highly beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or complex construction of synthetic gene networks.

synthetic biology↗

bioRxiv2018

Role of communication network topology in controlling microbial population in consortia

Engineering microbial consortia is an important new frontier for synthetic biology given its efficiency in performing complex tasks and endurance to environmental uncertainty. Most synthetic circuits regulate populational behaviors via cell-to-cell communications, which are affected by spatially heterogenous environments. Therefore, it is important to understand the limits on controlling system dynamics that are determined by interconnections among cell agents and provide a control strategy for engineering consortia. Here, we build a network model for a fractional population control circuit in two-strain consortia, and characterize the cell-to-cell communication network by topological properties, such as symmetry, locality and connectivity. Using linear network control theory, we relate the network topology to system outputs tracking performance. We analytically and numerically demonstrate that the minimum network control energy for accurate tracking depends on locality difference between two cell populations spatial distributions and how strongly the controller node contributes to communication strength. To realize a robust consortia, we can manipulate the communication network topology and construct strongly connected consortia by altering chemicals in environments. Our results ground the expected cell population dynamics in its spatially organized communication network, and inspire directions in cooperative control in microbial consortia.

synthetic biology↗

bioRxiv2014

Regulatory RNA design through evolutionary computation and strand displacement

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

Synthetic Biology↗

bioRxiv2016

Regulation of cancer epigenomes with a histone-binding synthetic transcription factor

Chromatin proteins have expanded the mammalian synthetic biology toolbox by enabling control of active and silenced states at endogenous genes. Others have reported synthetic proteins that bind DNA and regulate genes by altering chromatin marks, such as histone modifications. Previously we reported the first synthetic transcriptional activator, the \"Polycomb-based transcription factor\" (PcTF), that reads histone modifications through a protein-protein interaction between the PCD motif and trimethylated lysine 27 of histone H3 (H3K27me3). Here, we describe the genome-wide behavior of PcTF. Transcriptome and chromatin profiling revealed PcTF-sensitive promoter regions marked by proximal PcTF and distal H3K27me3 binding. These results illuminate a mechanism in which PcTF interactions bridge epigenetic marks with the transcription initiation complex. In three cancer-derived human cell lines tested here, many PcTF-sensitive genes encode developmental regulators and tumor suppressors. Thus, PcTF represents a powerful new fusion-protein-based method for cancer research and treatment where silencing marks are translated into direct gene activation.

Synthetic Biology↗

bioRxiv2017

An automated model test system for systematic development and improvement of gene expression models

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 is a significant challenge, even though they are essential to engineering complex genetic systems. 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 that enables correct model comparisons across datasets. 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 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.

synthetic biology↗

bioRxiv2016

Analytic framework for stochastic binary biological switches

We propose an analytic solution for the stochastic dynamics of a binary biological switch, defined as a DNA unit with two mutually exclusive configurations, each one triggering the expression of a different gene. Such a device could be used as a memory unit for biological computing systems designed to operate in noisy environments. We discuss a recent implementation of an exclusive switch in living cells, the recombinase addressable data (RAD) module. In order to understand the behavior of a RAD module we compute the exact time dependent distributions of the two expressed genes starting in one state and evolving to another asymptotic state. We consider two operating regimes of the RAD module: fast and slow stochastic switching. The fast regime is \"aggregative\" and produces unimodal distributions, whereas the slow regime is \"separative\" and produces bimodal distributions. Both regimes can serve to prepare pure memory states when all cells are expressing the same gene. The slow regime can also separate mixed states by producing two sub-populations each one expressing a different gene. Our model provides a simplified, general phenomenological framework for studying biological memory devices and our analytic solution can be further used to clarify theoretical concepts in bio-computation and for optimal design in synthetic biology.

Synthetic Biology↗

bioRxiv2014

Engineering a circular riboregulator in Escherichia coli

Circular RNAs have recently been shown to be important gene expression regulators in mammalian cells. However, their role in prokaryotes remains elusive. Here, we engineered a synthetic riboregulator that self-splice to produce a circular molecule, exploiting group I permuted intron-exon (PIE) sequences. We demonstrated that the resulting circular riboregulator can activate gene expression, showing increased dynamic range compared to the linear form. We characterized the system with a fluorescent reporter and with an antibiotic resistance marker. Thanks to the increased regulatory activity by higher stability, isolation due to self-splicing, and modularity of PIE, we envisage engineered circular riboregulators in further synthetic biology applications.

Synthetic Biology↗

bioRxiv2015

A statistical approach reveals designs for the most robust stochastic gene oscillators

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative structural robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of auto-regulation, and degradation of mRNA and protein affects the frequency, amplitude and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modelling approaches to systems and synthetic biology.

Synthetic Biology↗

bioRxiv2018

Investigating the unknown functions in the minimal bacterial genome reveals many transporter proteins

The recent identification of the minimal bacterial genome revealed that nearly one third (149) of the 473 encoded genes were of unknown function, demonstrating our limited understanding of the essential functions of life. Application of state of the art in silico methods for functional annotation demonstrated that these proteins of unknown function lack orthologs, known protein domains, and templates to model their structure. Combination of the results from different complementary approaches enabled functions to be assigned to 94 of the 149 proteins, although often with general terms such as transporter or DNA binding. 22 likely transporter proteins were identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell, where many metabolic enzymes have been removed. These results advance our understanding of the minimal bacterial genome and therefore aid synthetic biology and its application to biotechnology.

synthetic biology↗

bioRxiv2015

Deconvolution of gene expression noise into physical dynamics of cognate promoters

When facing recalcitrant pollutants, soil bacteria exploit noise of catabolic promoters for deploying environmentally beneficial phenotypes such as metabolic bet-hedging an/or division of biochemical labor. While the origin of such noise in terms of upstream promoter-regulator interplay is hardly understood, its dynamics has to be somehow encrypted in the patterns of flow-cytometry data delivered by transcriptional reporter fusions. On this background, we have examined the behaviour of the Pm promoter of the environmental bacterium Pseudomonas putida and its cognate 3-methylbenzoate-responsive regulator XylS under different conditions by following expression of Pm-GFP fusions in single cells. Using mathematical modeling and computational simulations we elucidated the kinetic properties of the system and use them as a baseline code to interpret the observed fluorescence output in terms of upstream regulator variability. Transcriptional noise was predicted to depend on the intracellular physical distance between the regulator source (where the e.g. XylS is being produced in the cells) and the target promoter. Experiments with engineered bacteria where this distance is either minimized or enlarged proved the effects of proximity on noise patterns as predicted by the model. This approach not only allowed deconvolution of cytometry data into mechanistic information on the gene expression flow. But it also provided a mechanistic basis for selecting a given level of noise in engineered regulatory nodes e.g. in Synthetic Biology constructs.

Synthetic Biology↗

bioRxiv2016

Self-destructive altruism in a synthetic developmental program enables complex feedstock utilization

Stochastic differentiation and programmed cell death are common developmental processes in microbes, driving diverse altruistic behaviors that promote cooperation. Utilizing cell death in developmental programs requires control over the rate of differentiation to balance cell proliferation against the utility of sacrifice. However, the regulatory networks that control these behaviors are often complex and have yet to be successfully harnessed as biotechnology. Here, we engineered a synthetic developmental gene network that couples stochastic differentiation with programmed cell death to implement a two-member division of labor. Progenitor cellobiose consumer cells were engineered to grow on cellobiose and differentiate at a controlled rate into self-destructive altruists that release an otherwise sequestered cellulase enzyme payload through autolysis to form a developmental Escherichia coli consortium that utilizes cellulose for growth. We used an experimentally parameterized model of task switching, payload delivery and cellulose conversion to nutrients to set key parameters to achieve overall population growth supported by cellulase release, liberating 14-23% of the available carbon. An inevitable consequence of engineering altruistic developmental behaviors is the emergence of cheaters that undermine cooperation. We observed cheater phenotypes for consumers and altruists, identified mutational hotspots and constructed a predictive model of circuit longeivity based on mutation rate estimates for each mode of evolutionary escape. This work introduces the altruistic developmental program as a new tool for synthetic biology, demonstrates the utility of population dynamics models to engineer complex phenotypes and provides a testbed for probing the evolutionary biology of self-destructive altruism.

synthetic biology↗

bioRxiv2014

Cooperative riboregulation in living cells through allosteric programming of toehold activation

Living cells rely on small non-coding RNAs (sRNAs) to regulate gene expression at the post-transcriptional level. Contrary to most protein-based activators of transcription, all known riboregulators do not exploit cooperative binding mechanisms to activate gene expression. We conceived a general strategy to design cooperative riboregulation by programming a hierarchical toehold activation cascade, which we implemented into a de novo sequence design algorithm. We engineered different riboregulatory systems relying on the conditional formation of a heterotrimeric species. We characterized the specificity of each RNA-RNA interaction in vitro and the cooperative activation of gene expression in Escherichia coli. As we only rely on a biochemical model to compute allosteric regulation, our strategy could be applied to reach more complex RNA-based nanostructures regulating gene expression for synthetic biology applications.

Synthetic Biology↗

bioRxiv2017

Tn-Core: context-specific reconstruction of core metabolic models using Tn-seq data

MotivationTn-seq (transposon mutagenesis and sequencing) and constraint-based metabolic modelling represent highly complementary approaches. They can be used to probe the core genetic and metabolic networks underlying a biological process, revealing invaluable information for synthetic biology engineering of microbial cell factories. However, while algorithms exist for integration of -omics data sets with metabolic models, no method has been explicitly developed for integration of Tn-seq data with metabolic reconstructions.\n\nResultsWe report the development of Tn-Core, a Matlab toolbox designed to generate gene-centric, context-specific core reconstructions consistent with experimental Tn-seq data. Extensions of this algorithm allow: i) the generation of context-specific functional models through integration of both Tn-seq and RNA-seq data; ii) to visualize redundancy in core metabolic processes; and iii) to assist in curation of de novo draft metabolic models. The utility of Tn-Core is demonstrated primarily using a Sinorhizobium meliloti model as a case study.\n\nAvailability and implementationThe software can be downloaded from https://github.com/diCenzo-GC/Tn-Core. All results presented in this work have been obtained with Tn-Core v. 1.0.\n\nContactgeorgecolin.dicenzo@unifi.it, marco.fondi@unifi.it\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗