SEARCH · Search bioRxiv

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 19 recordsLinked to original sources

bioRxiv2017

PartsGenie: an integrated tool for optimising and sharing synthetic biology parts

Synthetic biology is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. While repositories of such parts exist to promote their reuse, there is still a need to design novel parts from scratch.\n\nPartsGenie, freely available at http://parts.synbiochem.co.uk, is introduced to facilitate the computational design of such synthetic biology parts. PartsGenie has been designed to bridge the gap between optimisation tools for the design of novel parts, the representation of such parts in community-developed data standards such as Synthetic Biology Open Language (SBOL), and their sharing in journal-recommended data repositories.\n\nConsisting of a drag-and-drop web interface, a number of DNA optimisation algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimisation and dissemination of reusable synthetic biology parts through a single, integrated application. PartsGenie can therefore be used as a single, stand-alone tool, or integrated into larger synthetic biology pipelines that are being developed in the SYNBIOCHEM centre and elsewhere.

synthetic biology↗

bioRxiv2017

Quantitative Modeling of Integrase Dynamics Using a Novel Python Toolbox for Parameter Inference in Synthetic Biology

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, partioning models, and Bayesian inference - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML) or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the packages simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/biocircuits/bioscrape) along with more detailed documentation and examples.

synthetic biology↗

bioRxiv2015

Landauer in the age of synthetic biology: energy consumption and information processing in biochemical networks

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using \"post-translational\" synthetic biology that exploits reversible protein modification.

Synthetic Biology↗

bioRxiv2016

The Software Crisis of Synthetic Biology

In fifteen years, Synthetic Biology (SB) has moved from proof-of-concept designs to several flagship achievements. Standardisation efforts are still under way, basic engineering concepts such as modularity and orthogonality are still controversial in biology, and making predictions from computer models is still unreliable. A deep characterization in the pattern of re-use of biological blocks in SB has not been attempted to date. We have compared the topological organisation of two different technological networks, one associated to a standard, large-scale software repository and the second provided by the Registry of Standard Biological Parts (RSBP). Our results strongly suggest that software engineering, and not industrial engineering, is the closest complex system to SB. In both cases, combining standard or quasi-standard components assembly with tinkering may not be at odds with success.

Systems Biology↗

bioRxiv2017

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory

Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We propose a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we outline this design space in general as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible mathematical framework we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

synthetic biology↗

bioRxiv2016

Development of prokaryotic cell-free systems for synthetic biology

Prokaryotic cell-free systems are currently heavily used for the production of protein that can be otherwise challenging to produce in cells. However, historically cell-free systems were used to explore natural phenomena before the advent of genetic modification and transformation technology. Recently, synthetic biology has seen a resurgence of this historical use of cell-free systems as a prototyping tool of synthetic and natural genetic circuits. For these cell-free systems to be effective prototyping tools, an understanding of cell-free system mechanics must be established that is not purely protein-expression driven. Here we discuss the development of E. coli-based cell-free systems, with an emphasis on documenting published extract and energy preparation methods into a uniform format. We also discuss additional considerations when applying cell-free systems to synthetic biology.

Synthetic Biology↗

bioRxiv2017

BacilloFlex: A modular DNA assembly toolkit for Bacillus subtilis synthetic biology

Bacillus subtilis is a valuable industrial production platform for proteins, a bacterial model for cellular differentiation and its endospores have been proposed as a vehicle for vaccine delivery. As such B. subtilis is a major synthetic biology chassis but, unlike Escherichia coli, lacks a standardized toolbox for genetic manipulation. EcoFlex is a versatile modular DNA assembly toolkit for E. coli synthetic biology based on Golden Gate cloning. Here we introduce BacilloFlex, an extension of the EcoFlex assembly standard to B. subtilis. Transcription units flanked by sequences homologous to loci in the B. subtilis genome were rapidly assembled by the EcoFlex standard and subsequently chromosomally integrated. At present, BacilloFlex includes a range of multi-functional B. subtilis specific parts with applications including metabolic engineering, biosensors and spore surface display. We hope this work will form the foundation of a widely adopted cloning standard for B. subtilis facilitating collaboration and the sharing of parts.

synthetic biology↗

bioRxiv2018

Kluyveromyces marxianus as a robust synthetic biology platform host

Throughout history, the yeast Saccharomyces cerevisiae has played a central role in human society due to its use in food production and more recently as a major industrial and model microorganism, because of the many genetic and genomic tools available to probe its biology. However S. cerevisiae has proven difficult to engineer to expand the carbon sources it can utilize, the products it can make, and the harsh conditions it can tolerate in industrial applications. Other yeasts that could solve many of these problems remain difficult to manipulate genetically. Here, we engineer the thermotolerant yeast Kluyveromyces marxianus to create a new synthetic biology platform. Using CRISPR-Cas9 mediated genome editing, we show that wild isolates of K. marxianus can be made heterothallic for sexual crossing. By breeding two of these mating-type engineered K. marxianus strains, we combined three complex traits- thermotolerance, lipid production, and facile transformation with exogenous DNA-into a single host. The ability to cross K. marxianus strains with relative ease, together with CRISPR-Cas9 genome editing, should enable engineering of K. marxianus isolates with promising lipid production at temperatures far exceeding those of other fungi under development for industrial applications. These results establish K. marxianus as a synthetic biology platform comparable to S. cerevisiae, with naturally more robust traits that hold potential for the industrial production of renewable chemicals.

synthetic biology↗

bioRxiv2017

Characterization of Azotobacter vinelandii and Kits for Its Synthetic Biology Applications

Azotobacter vinelandii, a Gram-negative aerobic bacterium with an intracellular anaerobic environment that maintains the oxygen-sensitive enzymatic cascades for nitrogen fixation, could be used to express oxygen-sensitive proteins. However, little is known about the properties of A. vinelandii for synthetic biology applications. We therefore first characterized and optimized the conditions for growing and screening BioBrick constructs in A. vinelandii in the presence of 2 antibiotics, ampicillin and chloramphenicol, and then developed two sets of BioBricks for regulated protein expression. The first kit used T7 RNA polymerase, whose expression is under the control of a nitrogen-repressible nifH promoter. The commonly used T7-dependent system in Escherichia coli can then be used in A. vinelandii. Because its intracellular anaerobic environment is favorable for processes such as magnetosome biogenesis, we attempted to migrate the biogenesis machineries from the magnetotactic bacterium Magnetospirillum gryphiswaldense to A. vinelandii. During this undertaking, another insertion kit construct was developed to allow protein conjugation onto magnetosomes. The kit consists of mamC, a gene encoding a transmembrane protein on magnetosomes, and multiple restriction sites downstream of mamC for fusing a gene of interest. This insertion kit allows the attachment of any desired protein onto the magnetosome membrane by fusing with the mamC protein. We demonstrated the function of this kit by fusing mamC to a GFP nanobody. This kit will facilitate the conjugation of any target protein onto magnetosomes for downstream applications in the future.\n\nFinancial DisclosureWe received sponsorship from the 2012-15 Teaching Development Grants Triennium, Faculty of Engineering and Biochemistry Program, School of Life Sciences, The Chinese University of Hong Kong. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\nCompeting InterestsThe authors declare that no competing interests exist.\n\nEthics StatementN/A.\n\nData AvailabilityAll data are fully available without restriction.

synthetic biology↗

bioRxiv2016

Establishing elements of a synthetic biology platform for Vaccinia virus production: BioBrickTM design, serum-free virus production and microcarrier-based cultivation of CV-1 cells.

Vaccinia virus (VACV) is an established tool for vaccination and is beginning to prove effective as an oncolytic agent. Industrial production of VACV stands to benefit in future from advances made by synthetic biology in genome engineering and standardisation. As a step toward realising these benefits we analysed the Lister Vaccinia virus genome with respect to refactoring options and propose a VACV genome engineering BioBrick. We then used the CV-1 cell line to produce a conventional recombinant Lister strain VACV, VACVL-15 RFP in a serum-free process. CV-1 cells grown in 5% foetal bovine serum (FBS) Dulbeccos Modified Eagle Medium (DMEM) were adapted to growth in OptiPRO and VP-SFM brands of serum-free media. Specific growth rates of 0.047 h-1 and 0.044 h-1 were observed for cells adapted to OptiPRO and VP-SFM respectively, compared to 0.035 h-1 in 5% FBS DMEM. Cells adapted to OptiPRO and to 5% FBS DMEM achieved recovery ratios of over 96%, an indication of their robustness to cryopreservation. Cells adapted to VP-SFM showed a recovery ratio of 82%. VACV production in static culture, measured as plaque forming units (PFU) per propagator cell, was 75 PFU/cell for cells in 5% FBS DMEM. VP-SFM and OptiPRO adaptation increased VACV production to 150 PFU/cell and 350 PFU/cell respectively. Boosted PFU/cell from OptiPRO-adapted cells persisted when 5% FBS DMEM or OptiPRO medium was present during the infection step and when titre was measured using cells adapted to 5% FBS DMEM or OptiPRO medium. Finally, OptiPRO-adapted CV-1 cells were successfully cultivated using Cytodex-1 microcarriers to inform future scale up studies.

Synthetic Biology↗

bioRxiv2018

A synthetic biology platform for the reconstitution and mechanistic dissection of LINC complex assembly

The linker of nucleoskeleton and cytoskeleton (LINC) is a conserved nuclear envelope-spanning molecular bridge that is responsible for the mechanical integration of the nucleus with the cytoskeleton. LINC complexes are formed by a transluminal interaction between the outer and inner nuclear membrane KASH and SUN proteins, respectively. Despite recent structural insights, our mechanistic understanding of LINC complex assembly remains limited by the lack of an experimental system for its in vitro reconstitution and manipulation. Here, we describe artificial nuclear membranes (ANMs) as a synthetic biology platform based on mammalian cell-free expression for the rapid reconstitution of SUN proteins in supported lipid bilayers. We demonstrate that SUN1 and SUN2 are oriented in ANMs with solvent-exposed C-terminal KASH-binding SUN domains. We also find that SUN2 possesses a single transmembrane domain, while SUN1 possesses three. Finally, SUN protein-containing ANMs bind synthetic KASH peptides, thereby reconstituting the LINC complex core. This work represents the first in vitro reconstitution of KASH-binding SUN proteins in supported lipid bilayers using cell-free expression, which will be invaluable for testing proposed models of LINC complex assembly and its regulation.

synthetic biology↗

bioRxiv2017

Non-biological synthetic spike-in controls and the AMPtk software pipeline improve fungal high throughput amplicon sequencing data

High throughput amplicon sequencing (HTAS) of conserved DNA regions is a powerful technique to characterize microbial communities. Recently, spike-in mock communities have been used to measure accuracy of sequencing platforms and data analysis pipelines. To assess the ability of sequencing platforms and data processing pipelines using fungal ITS amplicons, we created two ITS spike-in control mock communities composed of cloned DNA in plasmids: a biological mock community (BioMock), consisting of ITS sequences from fungal taxa, and a synthetic mock community (SynMock), consisting of non-biological ITS-like sequences. Using these spike-in controls we show that: 1) a non-biological synthetic control (e.g., SynMock) is the best solution for parameterizing bioinformatics pipelines, 2) pre-clustering steps for variable length amplicons are critically important, 3) a major source of bias is attributed to initial PCR reactions and thus HTAS read abundances are typically not representative of starting values. We developed AMPtk, a versatile software solution equipped to deal with variable length amplicons and quality filter HTAS data based on spike-in controls. While we describe herein a non-biological synthetic mock community for ITS sequences, the concept and AMPtk software can be widely applied to any HTAS dataset to improve data quality.\n\nAvailability and Implementation - AMPtk is publically available at https://github.com/nextgenusfs/amptk. All primary data and data analysis done in this manuscript are available via the Open Science Framework (https://osf.io/4xd9r/). The SynMock sequences and the script to produce them are available in the OSF repository ((https://osf.io/4xd9r/) as well as packaged into AMPtk distributions.

bioinformatics↗

bioRxiv2017

Multiplex genome editing for synthetic biology in Vibrio natriegens

Vibrio natriegens has recently emerged as an alternative to Escherichia coli for molecular biology and biotechnology, but low-efficiency genetic tools hamper its development. Here, we uncover how to induce natural competence in V. natriegens and describe methods for multiplex genome editing by natural transformation (MuGENT). MuGENT promotes integration of large genome edits at high-efficiency on unprecedented timescales, which will extend the utility of this species for diverse applications.\n\nV. natriegens is the fastest growing organism known, with a doubling time of <10 min1,2. With broad metabolic capabilities, lack of pathogenicity, and its rapid growth rate, it is an attractive alternative to E. coli for diverse molecular biology and biotechnology applications3. Methods for classical genetic techniques have been developed for V. natriegens, but these are rel ...

synthetic biology↗

bioRxiv2017

A synthetic biology approach to probing nucleosome symmetry

The repeating subunit of chromatin, the nucleosome, includes two copies of each of the four core histones, and several recent studies have reported that asymmetrically-modified nucleosomes occur at regulatory elements in vivo. To probe the mechanisms by which histone modifications are read out, we designed an obligate pair of H3 heterodimers, termed H3X and H3Y, which we extensively validated genetically and biochemically. Comparing the effects of asymmetric histone tail point mutants with those of symmetric double mutants revealed that a single methylated H3K36 per nucleosome was sufficient to silence cryptic transcription in vivo. We also demonstrate the utility of this system for analysis of histone modification crosstalk, using mass spectrometry to separately identify modifications on each H3 molecule within asymmetric nucleosomes. The ability to generate asymmetric nucleosomes in vivo and in vitro provides a powerful and generalizable tool to probe the mechanisms by which H3 tails are read out by effector proteins in the cell.

molecular biology↗

bioRxiv2015

Integrating Gene Synthesis and Microfluidic Protein Analysis for Rapid Protein Engineering

The capability to rapidly design proteins with novel functions will have a significant impact on medicine, biotechnology, and synthetic biology. Synthetic genes are becoming a commodity, but integrated approaches have yet to be developed that take full advantage of gene synthesis. We developed a solid-phase gene synthesis method based on asymmetric primer extension (APE) and coupled this process directly to high-throughput, on-chip protein expression, purification, and characterization (mechanically induced trapping of molecular interactions, MITOMI). By completely circumventing molecular cloning and cell-based steps, APE-MITOMI reduces the time between protein design and quantitative characterization to 3-4 days. With APE-MITOMI we synthesized and characterized over 440 zinc-finger (ZF) transcription factors (TF), showing that although ZF TFs can be readily engineered to recognize a particular DNA sequence, engineering the precise binding energy landscape remains challenging. We also found that it is possible to engineer ZF - DNA affinity precisely and independently of sequence specificity and that in silico modeling can explain some of the observed affinity differences. APE-MITOMI is a generic approach that should facilitate fundamental studies in protein biophysics, and protein design/engineering.

Bioengineering↗

bioRxiv2017

Finding analytic stationary solutions to the chemical master equation by gluing state spaces at one or two states recursively

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model that describes how the probability distribution of a chemically reacting system varies with time. Knowing analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterise state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing stationary distributions that satisfy user-specified constraints. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and chemical reactions with two-dimensional state spaces.\n\nSubject AreasSystems biology, synthetic biology, biomathematics, bioengineering

systems biology↗

bioRxiv2015

GreA and GreB enhance Escherichia coli RNA polymerase transcription rate in a reconstituted transcription-translation system

Cell-free environments are becoming viable alternatives for implementing biological networks in synthetic biology. The reconstituted cell-free expression system (PURE) allows characterization of genetic networks under defined conditions but its applicability to native bacterial promoters and endogenous genetic networks is limited due to the poor transcription rate of Escherichia coli RNA polymerase in this minimal system. We found that addition of transcription elongation factors GreA and GreB to the PURE system increased transcription rates of E. coli RNA polymerase from sigma factor 70 promoters up to 6-fold and enhanced the performance of a genetic network. Furthermore, we reconstituted activation of natural E. coli promoters controlling flagella biosynthesis by the transcriptional activator FlhDC and sigma factor 28. Addition of GreA/GreB to the PURE system allows efficient expression from natural and synthetic E. coli promoters and characterization of their regulation in minimal and defined reaction conditions making the PURE system more broadly applicable to study genetic networks and bottom-up synthetic biology.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC=\"FIGDIR/small/024604_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@53902org.highwire.dtl.DTLVardef@e1ba85org.highwire.dtl.DTLVardef@19c961eorg.highwire.dtl.DTLVardef@143928d_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG

Synthetic Biology↗

bioRxiv2014

In Silico Design of Self-Optimizing Integrated Metabolic and Gene Regulatory Networks

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed in silico evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

Bioengineering↗