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bioRxiv2017

Prototyping And Implementation Of A Novel Feedforward Loop In A Cell-Free Transcription-Translation System And Cells

Building novel synthetic biological devices is a time-consuming task because of the lengthy cell-based testing and optimization processes. Recent progress made in the cell-free field suggests that the utilization of mathematical models and cell-free transcription-translation testing platforms to systematically design and test novel synthetic biocircuits may help streamline some of the processes. Here we present a study of building a novel functional biological network motif from scratch with the aid of the mathematical modeling and the cell-free prototyping. In this work, we demonstrated that we were able to make a 3-promoter feedforward circuit from a concept to a working biocircuit in cells within a month. We started with performing simulations with a cell-free transcription-translation simulation toolbox. After verifying the feasibility of the circuit design, we used a fast assembling method to build the constructs and used the linear DNAs directly in the cell-free system for prototyping. After additional tests and assemblies, we implemented the circuit in plasmid forms in cells and showed that the in vivo results were consistent with the simulations and the outcomes in the cell-free platform. This study showed the usefulness of modeling and prototyping in building synthetic biocircuits and that we can use these tools to help streamline the process of circuit optimizations in future studies.

bioengineering↗

bioRxiv2018

Synthetic genome defenses against selfish DNA elements stabilize engineered bacteria against evolutionary failure

Mobile genetic elements drive evolution by disrupting genes and rearranging genomes. Eukaryotes have evolved epigenetic mechanisms, including DNA methylation and RNA interference, that silence mobile elements and thereby preserve the integrity of their genomes. We created an artificial reprogrammable epigenetic system based on CRISPR interference to give engineered bacteria a similar line of defense against transposons and other selfish elements in their genomes. We demonstrate that this CRISPR interference against mobile elements (CRISPRi-ME) approach can be used to simultaneously repress two different transposon families in Escherichia coli, thereby increasing the evolutionary stability of costly protein expression. We further show that silencing a transposon in Acinetobacter baylyi ADP1 reduces mutation rates by a factor of five, nearly as much as deleting all copies of this element from its genome. By deploying CRISPRi-ME on a broad-host-range vector we have created a generalizable platform for stabilizing the genomes of engineered bacterial cells for applications in metabolic engineering and synthetic biology.\n\nSignificanceEngineered cells often cease to function or lose productivity when mutations arise in their genomes. Mobile DNA elements, such as transposons, are major sources of these inactivating mutations. Eukaryotic genomes have evolved flexible epigenetic defenses against mobile DNA that help them to maintain genome integrity, but bacteria do not possess comparable silencing systems. We developed a synthetic control system based on CRISPR interference that can be used to give bacterial cells a reprogrammable line of defense against selfish DNA elements in their genomes. We show that this system effectively represses multicopy transposons and multiple families of transposons. Limiting selfish DNA proliferation within a genome in this way improves the reliability of genetically engineered functions in replicating bacterial cell populations.

evolutionary biology↗

bioRxiv2017

Adapting machine-learning algorithms to design gene circuits

Biological systems rely on complex networks, such as transcriptional circuits and protein-protein interaction networks, to perform a variety of functions e.g. responding to stimuli, directing cell fate, or patterning an embryo. Mathematical models are often used to ask: given some network, what function does it perform? However, we often want precisely the opposite i.e. given some circuit - either observed in vivo, or desired for some engineering objective - what biological networks could execute this function? Here, we adapt optimization algorithms from machine learning to rapidly screen and design gene circuits capable of performing arbitrary functions. We demonstrate the power of this approach by designing circuits (1) that recapitulate important in vivo phenomena, such as oscillators, and (2) to perform complex tasks for synthetic biology, such as counting noisy biological events. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.

systems biology↗

bioRxiv2017

DeepMetabolism: A Deep Learning System To Predict Phenotype From Genome Sequencing

Life science is entering a new era of petabyte-level sequencing data. Converting such \"big data\" to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

systems biology↗

bioRxiv2016

Pan- and core- network analysis of co-expression genes in a model plant

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of pan-network and core-network representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.\n\nResearch AreaSystems and Synthetic Biology\n\nSummaryBy analyzing 134 microarray datasets for Arabidopsis, we found that gene coexpression networks are highly context-dependent.\n\nFinancial sourceWork at Brookhaven was supported by grants PM-031 from the Office of Biological Research of the U.S. Department of Energy.

Bioinformatics↗

bioRxiv2017

Tellurium Notebooks - An Environment for Dynamical Model Development, Reproducibility, and Reuse

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system that facilitates building reproducible dynamical models and reusing models by 1) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 2) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.\n\nAuthor summaryThere is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human-readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human-readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard-encoded models and simulations, regardless of the users level of technical knowledge of said standards.

systems biology↗

bioRxiv2017

MetQy: an R package to query metabolic functions of genes and genomes

SummaryWith the rapid accumulation of sequencing data from genomic and metagenomic studies, there is an acute need for better tools that facilitate their analyses against biological functions. To this end, we developed MetQy, an open-source R package designed for query-based analysis of functional units in [meta]genomes and/or sets of genes using the The Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Furthermore, MetQy contains visualization and analysis tools and facilitates KEGGs flat file manipulation. Thus, MetQy enables better understanding of metabolic capabilities of known genomes or user-specified [meta]genomes by using the available information and can help guide studies in microbial ecology, metabolic engineering and synthetic biology.\n\nAvailability and ImplementationThe MetQy R package is freely available and can be downloaded from our groups website (http://osslab.lifesci.warwick.ac.uk) or GitHub (https://github.com/OSS-Lab/MetQy).\n\nContactO.Soyer@warwick.ac.uk

microbiology↗

bioRxiv2018

An Open-Source Plate Reader

Microplate readers are foundational instruments in experimental biology and bioengineering that enable multiplexed spectrophotometric measurements. To enhance their accessibility, we here report the design, construction, validation, and benchmarking of an open-source microplate reader. The system features full-spectrum absorbance and fluorescence emission detection, in situ optogenetic stimulation, and stand-alone touch screen programming of automated assay protocols. The total system costs <$3500, a fraction of the cost of commercial plate readers, and can detect the fluorescence of common dyes down to [~]10 nanomolar concentration. Functional capabilities were demonstrated in context of synthetic biology, optogenetics, and photosensory biology: by steady-state measurements of ligand-induced reporter gene expression in a model of bacterial quorum sensing, and by flavin photocycling kinetic measurements of a LOV (light-oxygen-voltage) domain photoreceptor used for optogenetic transcriptional activation. Fully detailed guides for assembling the device and automating it using the custom Python-based API (Application Program Interface) are provided. This work contributes a key technology to the growing community-wide infrastructure of open-source biology-focused hardware, whose creation is facilitated by rapid prototyping capabilities and low-cost electronics, optoelectronics, and microcomputers.\n\nTable of Contents Graphic\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC=\"FIGDIR/small/413781_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (30K):\norg.highwire.dtl.DTLVardef@c26eaborg.highwire.dtl.DTLVardef@efb695org.highwire.dtl.DTLVardef@1bc2f41org.highwire.dtl.DTLVardef@1c25c79_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2017

Signaling architectures that transmit unidirectional information despite retroactivity

A signaling pathway transmits information from an upstream system to downstream systems, ideally in a unidirectional fashion. A key obstacle to unidirectional transmission is retroactivity, the additional reaction flux that affects a system once its species interact with those of downstream systems. This raises the fundamental question of whether signaling pathways have developed specialized architectures that overcome retroactivity and transmit unidirectional signals. Here, we propose a general procedure based on mathematical analysis that provides an answer to this question. Using this procedure, we analyze the ability of a variety of signaling architectures to transmit one-way (from upstream to downstream) signals, as key biological parameters are tuned. We find that single stage phosphorylation and phosphotransfer systems that transmit signals from a kinase show a stringent design trade-off that hampers their ability to overcome retroactivity. Interestingly, cascades of these architectures, which are highly represented in nature, can overcome this trade-off and thus enable unidirectional transmission. By contrast, phosphotransfer systems, and single and double phosphorylation cycles that transmit signals from a substrate are unable to mitigate retroactivity effects, even when cascaded, and hence are not well suited for unidirectional information transmission. Our results identify signaling architectures that, allowing unidirectional transmission of signals, embody modular processes that conserve their input/output behavior across multiple contexts. These findings can be used to decompose natural signal transduction networks into modules, and, at the same time, they establish a library of devices that can be used in synthetic biology to facilitate modular circuit design.

biophysics↗

bioRxiv2026

Autonomous Homeostatic Synthetic Cells via Self-Gating DNA Nanopores

Homeostasis is a fundamental hallmark of living organisms, arising from the complex interplay between biochemical reactions and regulatory feedback systems. Reconstituting such self-regulating behaviour in minimal synthetic cells enables continuous, persistent operation of biochemical reactions for extended amount of time. In this work, we demonstrate a minimal homeostatic synthetic cell capable of autonomous flux regulation using DNA nanotechnology and bottom-up synthetic biology. Our homeostatic architecture consists of Giant Unilamellar Vesicles (GUVs) equipped with gated DNA nanopores, encapsulated in vitro transcription (IVT) machinery, and an RNA degradation system. We achieve homeostasis under varying external chemical stimuli specifically varying concentrations of rNTPs by implementing a negative feedback loop between rNTP influx and RNA production. In our system, DNA nanopores facilitate the influx of rNTPs from the external environment, driving internal transcription. Crucially, the transcription process generates RNA "blockers" designed to bind and gate the DNA nanopores, thereby attenuating further rNTP influx. Our system is dynamic as encapsulated RNases slowly degrade the RNA blockers, allowing the pores to reopen as blocker concentration goes down. We first characterise the functionality and gating efficiency of the DNA nanopores using both pre-synthesised and in situ produced DNA and RNA blockers. We then demonstrate that rNTP flux through these pores is sufficient to drive IVT within the GUVs. Finally, by integrating these modules, we demonstrate robust homeostasis: the system maintains a steady-state level of RNA production for up to 16 hours. By harnessing the controllability of negative feedback loop, we demonstrate thresholding of the homeostasis level using single-stranded regulator DNA. This work establishes a versatile framework for engineering adaptive and self-sustaining responsive nanomaterials and synthetic cell chassis.

biophysics↗

bioRxiv2017

Probabilistic Recovery Of Cryptic Haplotypes From Metagenomic Data

The cryptic diversity of microbial communities represent an untapped biotechnological resource for biomining, biorefining and synthetic biology. Revealing this information requires the recovery of the exact sequence of DNA bases (or \"haplotype\") that constitutes the genes and genomes of every individual present. This is a computationally difficult problem complicated by the requirement for environmental sequencing approaches (metagenomics) due to the resistance of the constituent organisms to culturing in vitro.\n\nHaplotypes are identified by their unique combination of DNA variants. However, standard approaches for working with metagenomic data require simplifications that violate assumptions in the process of identifying such variation. Furthermore, current haplotyping methods lack objective mechanisms for choosing between alternative haplotype reconstructions from microbial communities.\n\nTo address this, we have developed a novel probabilistic approach for reconstructing haplotypes from complex microbial communities and propose the \"metahaplome\" as a definition for the set of haplotypes for any particular genomic region of interest within a metagenomic dataset. Implemented in the twin software tools Hansel and Gretel, the algorithm performs incremental probabilistic haplotype recovery using Naive Bayes -- an efficient and effective technique.\n\nOur approach is capable of reconstructing the haplotypes with the highest likelihoods from metagenomic datasets without a priori knowledge or making assumptions of the distribution or number of variants. Additionally, the algorithm is robust to sequencing and alignment error without altering or discarding observed variation and uses all available evidence from aligned reads. We validate our approach using synthetic metahaplomes constructed from sets of real genes, and demonstrate its capability using metagenomic data from a complex HIV-1 strain mix. The results show that the likelihood framework can allow recovery from microbial communities of cryptic functional isoforms of genes with 100% accuracy.

bioinformatics↗

bioRxiv2018

Model Classification for Uncertainty Reduction in Biochemical Kinetic Models

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.\n\nAuthor SummaryKinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.

systems biology↗

bioRxiv2014

A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors

Clostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. We present iMLTC804cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC804cdf contains 804 genes, 705 metabolites and 766 metabolic, 145 exchange and 118 transport reactions. iMLTC804cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 88.8% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs. We predict the existence of 83 essential genes and 68 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 19 of these potential therapeutic targets, we find 72 inhibitors of homologous proteins that could serve as starting points in the development of new antibiotics, including approved drugs with the potential for drug repositioning.\n\nSystems & Synthetic Biology Subject Category: Genome Scale & Integrative Biology\n\nMolecular & Cell Biology Subject Category: Pharmacology & Drug Discovery

Systems Biology↗

bioRxiv2014

A simple method for constructing magnetic Escherichia coli

Magnetic force can serve as an ideal way to control the spatial behavior of microorganisms, because of its flexibility and penetrability. By incubation with the biocompatible compound, ammonium ferric citrate, as an iron source, we magnetized Escherichia coli, the most programmable chassis in synthetic biology. To enhance the magnetization efficiency, the ferritin protein, FtnA, from E. coli was cloned and overexpressed in strain BL21(DE3). The magnetization effect was observed within 30 min after harvest of bacteria, and the concentration of ammonium ferric acid used could be as low as 0.5 mM. Using different shapes of magnetic fields, different patterns could be generated easily. Our method may set up the foundation for a rational design of spatial structure of cell communities, which is important for their actual application.

Microbiology↗

bioRxiv2014

Multicellularity makes somatic differentiation evolutionarily stable

Many multicellular organisms produce two cell lineages: germ cells, whose descendants form the next generation, and somatic cells which support, protect, and disperse the germ cells. This distinction has evolved independently in dozens of multicellular taxa but is absent in unicellular species. We propose that unicellular, soma-producing populations are intrinsically susceptible to invasion by non-differentiating mutants which ultimately eradicate the differentiating lineage. We argue that multicellularity can prevent the victory of such mutants. To test this hypothesis, we engineer strains of the budding yeast Saccharomyces cerevisiae that differ only in the presence or absence of multicellularity and somatic differentiation, permitting direct comparisons between organisms with different lifestyles. We find that non-differentiating mutants overtake unicellular populations but are outcompeted by multicellular differentiating strains, suggesting that multicellularity confers evolutionary stability to somatic differentiation.\n\nOne Sentence SummaryUsing a synthetic biological approach, we show that multicellularity protects species that produce somatic cells from exploitation by common mutants.

Evolutionary Biology↗

bioRxiv2015

Design and strain selection criteria for bacterial communication networks

In this paper we discuss data and methodological challenges for building bacterial communication networks using two examples: Escherichia coli as a flagellate bacterium and of Geobacter sulfurreducens as a biofilm forming bacterium. We first highlight the link between the bacterial network communication design with respect to metabolic information processing design. The potentialities of designing routing network schemes described previously in literature and based on bacteria motility and genetic message exchanges will depend on the genes coding for the intracellular and intercellular signalling pathways. In bacteria, the \"mobilome\" is related to horizontal gene transfer. Bacteria trade off the acquisition of new genes which could improve their survival (and often their communication bandwidth), keeping their genome enough small to assure quick DNA replication and increase fast the biomass to speed up cell division. First, by using a multi-objective optimisation procedure, we search for the optimal trade off between energy production, which is a requirement for the motility, and the biomass growth, which is related to the overall survival and fitness of the bacterium. We use flux balance analysis of genome-scale biochemical network of Escherichia coli k-13 MG1655. Then, as a second case study we analyze the electric properties and biomass trade-off of the bacterium Geobacter sulfurreducens which constructs an electric biofilm where electrons move across the nanowires. Here we discuss the potentialities of optimisation methodologies to design and select bacterial strains with desiderata properties. The optimisation methodologies establish also a relation between metabolism, network communication and computing. Moreover, we point to genetic design and synthetic biology as key areas to develop bacterial nano communication networks.

Systems Biology↗

bioRxiv2015

A mechanistic link between cellular trade-offs, gene expression and growth

Intracellular processes rarely work in isolation but continually, interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that because of limitations in levels of cellular energy, free ribosomes, and proteins are faced by all living cells and construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modelling framework has potentially wide application, including in both biotechnology and medicine.

Systems Biology↗

bioRxiv2015

Exopolysaccharide biosynthetic glycoside hydrolases can be utilized to disrupt and prevent Pseudomonas aeruginosa biofilms

Bacterial biofilms are a significant medical challenge as they are recalcitrant to current therapeutic regimes. A key component of biofilm formation in the opportunistic human pathogen Pseudomonas aeruginosa is the biosynthesis of the exopolysaccharides Pel and Psl, which are involved in the formation and maintenance of the structural biofilm scaffold and protection against antimicrobials and host defenses. Given that the glycoside hydrolases - PelAh and PslGh - encoded in the pel and psl biosynthetic operons, respectively, are utilized for in vivo exopolysaccharide processing, we reasoned that these would provide specificity to target P. aeruginosa biofilms. Evaluating these enzymes as potential therapeutics, we demonstrate that these glycoside hydrolases selectively target and degrade the exopolysaccharide component of the biofilm matrix and that nanomolar concentrations of these enzymes can both prevent biofilm formation as well as rapidly disrupt preexisting biofilms in vitro. This treatment was effective against clinical and environmental P. aeruginosa isolates and reduced biofilm biomass by 58-94%. These non-cytotoxic enzymes potentiated antibiotics as the addition of either enzyme to a sub-lethal concentration of colistin reduced viable bacterial counts by 2.5 orders of magnitude. Additionally, PelAh was able to increase neutrophil killing by ~50%. This work illustrates the feasibility and benefits of using bacterial exopolysaccharide biosynthetic glycoside hydrolases and synthetic biology to develop novel anti-biofilm therapeutics.

Microbiology↗