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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 145 records · Page 8Linked to original sources

bioRxiv2019

Evolutionary regain of lost gene circuit function

Evolutionary reversibility - the ability to regain a lost function - is an important problem both in evolutionary and synthetic biology, where repairing natural or synthetic systems broken by evolutionary processes may be valuable. Here, we use a synthetic positive-feedback (PF) gene circuit integrated into haploid Saccharomyces cerevisiae cells to test if the population can restore lost PF function. In previous evolution experiments, mutations in a gene eliminated the fitness costs of PF activation. Since PF activation also provides drug resistance, exposing such compromised or broken mutants to both drug and inducer should create selection pressure to regain drug resistance and possibly PF function. Indeed, evolving seven PF mutant strains in the presence of drug revealed three adaptation scenarios through genomic mutations outside of the PF circuit that elevate PF basal expression, possibly by affecting transcription, translation, degradation and other fundamental cell functions. Nonfunctional mutants gained drug resistance without ever developing high expression, while quasi-functional and dysfunctional PF mutants developed high expression which then diminished, although more slowly for dysfunctional mutants where revertant clones arose. These results highlight how intracellular context, such as the growth rate, can affect regulatory network dynamics and evolutionary dynamics, which has important consequences for understanding the evolution of drug resistance and developing future synthetic biology applications.\n\nSignificance StatementNatural or synthetic genetic modules can lose their function over long-term evolution if the function is costly. How populations can evolve to restore broken functions is poorly understood. To test the reversibility of evolutionary breakdown, we use yeast cell populations with a chromosomally integrated synthetic gene circuit. In previous evolution experiments the gene circuit lost its costly function through various mutations. By exposing such mutant populations to conditions where regaining gene circuit function would be beneficial we find adaptation scenarios with or without repairing lost gene circuit function. These results are important for drug resistance or future synthetic biology applications where loss and regain of function play a significant role.

systems biology↗

bioRxiv2026

Synthetic biology-guided modular engineering of the secreted cutinase FoCut5a with a carbohydrate-binding module improves poly(ester-urethane) depolymerization in Yarrowia lipolytica

Poly(ester-urethane) bioconversion requires efficient polymer-degrading enzymes and microbial hosts capable of processing the resulting monomers. However, the optimal modular design of secreted cutinases for depolymerizing insoluble polyester-polyurethanes remains largely unexplored. Here, we established a combinatorial synthetic biology strategy in Yarrowia lipolytica based on the fungal cutinase FoCut5a from Fusarium oxysporum. A library of 96 Golden Gate-assembled constructs, combining four secretion signals, four interdomain linkers, and six substrate-binding domains, was integrated site-specifically into the genome and screened for depolymerization of the polyester-polyurethane dispersion Impranil DLN-SD. The best-performing variant fused FoCut5a to the carbohydrate-binding module TrCBM1. LC-MS analysis demonstrated release of adipic acid, 1,6-hexanediol, and neopentyl glycol, confirming ester-bond hydrolysis within the polyester domains of the polymer and additionally detected 6-hydroxycaproate, indicating oxidation of polyurethane-derived 1,6-hexanediol by Y. lipolytica. Purified FoCut5a-TrCBM1 displayed unchanged activity on p-nitrophenyl butyrate but reduced Impranil turbidity 5.6-fold faster and increased release of 1,6-hexanediol and adipic acid by 76-fold and 220-fold, respectively, compared to the unfused enzyme. These results highlight modular enzyme engineering as an effective strategy to enhance polyester-polyurethane deconstruction and support Y. lipolytica as a platform for polymer bioconversion and upcycling.

synthetic biology↗

bioRxiv2017

RetroPath2.0: A retrosynthesis workflow for metabolic engineers

Synthetic biology applied to industrial biotechnology is transforming the way we produce chemicals. However, despite advances in the scale and scope of metabolic engineering, the bioproduction process still remains costly. In order to expand the chemical repertoire for the production of next generation compounds, a major engineering biology effort is required in the development of novel design tools that target chemical diversity through rapid and predictable protocols. Addressing that goal involves retrosynthesis approaches that explore the chemical biosynthetic space. However, the complexity associated with the large combinatorial retrosynthesis design space has often been recognized as the main challenge hindering the approach. Here, we provide RetroPath2.0, an automated open source workflow for retrosynthesis based on generalized reaction rules that perform the retrosynthesis search from chassis to target through an efficient and well-controlled protocol. Its easiness of use and the versatility of its applications make of this tool a valuable addition into the biological engineer bench desk. We show through several examples the application of the workflow to biotechnological relevant problems, including the identification of alternative biosynthetic routes through enzyme promiscuity; or the development of biosensors. We demonstrate in that way the ability of the workflow to streamline retrosynthesis pathway design and its major role in reshaping the design, build, test and learn pipeline by driving the process toward the objective of optimizing bioproduction. The RetroPath2.0 workflow is built using tools developed by the bioinformatics and cheminformatics community, because it is open source we anticipate community contributions will likely expand further the features of the workflow.\n\nHighlightsO_LIState-of-the-art Computer-Aided Design retrosynthesis solutions lack open source and ease of use\nC_LIO_LIWe propose RetroPath2.0 a modular and open-source workflow to perform retrosynthesis\nC_LIO_LIRetroPath2.0 computes reaction network between Source and Sink sets of compounds\nC_LIO_LIRetroPath2.0 is distributed as a KNIME workflow for desktop computers\nC_LIO_LIRetroPath2.0 is ready-for-use and distributed with reaction rules\nC_LI\n\nFundingThis work was supported by the French National Research Agency [ANR-15-CE1-0008], the Biotechnology and Biological Sciences Research Council, Centre for synthetic biology of fine and speciality chemicals [BB/M017702/1]; Synthetic Biology Applications for Protective Materials [EP/N025504/1], and GIP Genopole.

systems biology↗

bioRxiv2023

Optimization of synthetic oscillatory biological networks through Reinforcement Learning

In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RLs capabilities in navigating biological systems inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms computational speed. Despite these limitations, the study highlights RLs significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks.

bioengineering↗

bioRxiv2020

SBOL Visual 2 Ontology

Standardising the visual representation of genetic parts and circuits is vital for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology. Graphical TOC Entry O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

synthetic biology↗

bioRxiv2026

Make it so: Rapid and affordable plasmid sequencing on ONT platforms with PICARD-seq

Plasmid construction underpins molecular biology and synthetic biology, yet validation is often limited to the inserted fragment rather than the whole plasmid, and around a third of laboratory-made plasmids carry sequence errors that can affect function. Sanger sequencing scales poorly across whole plasmids, while short-read approaches cannot resolve the repeated DNA parts, such as promoters, that are common in synthetic constructs. We present PICARD-seq, a rapid nanopore-based protocol that uses off-the-shelf Tn5 rapid barcoding reagents and a MinION to sequence pools of whole plasmids in under a day, and we systematically benchmark the computational pipelines available for analysing the resulting data. Using a curated set of 25 plasmids of known sequence spanning 3.0-20.6 kbp, including various dilution series and repetitive multi-part constructs, we ran five independent replicates of each pipeline. The ONT EPI2ME Clone Validation workflow was fast (13-18 min) but stochastic, varying between replicates for both plasmids assembled and what sequence was returned; Canu outperformed the default Flye assembler, and reducing the minimum coverage parameter from 60x to 20x substantially improved assembly of large, repetitive, and dilute samples. The ensemble assembler Autocycler was slower (81-111 min) but gave the highest and most consistent rate of recovering the expected sequence. Complementary read mapping with minimap2 distinguished genuine sequence differences from assembly artefacts. Applying PICARD-seq to problematic plasmids revealed backbone concatemers, a misincorporated promoter part, and a mixed population of rearranged molecules in a repetitive construct. PICARD-seq makes routine whole-plasmid validation practical and affordable for individual laboratories.

bioinformatics↗

bioRxiv2021

Genomically Mined Acoustic Reporter Genes Enable On-Demand In Vivo Monitoring of Tumor-Homing Bacteria

A major outstanding challenge in the fields of biological research, synthetic biology and cell-based medicine is visualizing the function of natural and engineered cells noninvasively inside opaque organisms. Ultrasound imaging has the potential to address this challenge as a widely available technique with a tissue penetration of several centimeters and spatial resolution below 100 m. Recently, the first genetically encoded acoustic reporters were developed based on bacterial gas vesicles to link ultrasound signals to molecular and cellular function. However, the properties of these first-generation acoustic reporter genes (ARGs) resulted in limited sensitivity and specificity for imaging gene expression in vivo. Here, we describe second-generation ARGs with greatly improved acoustic properties and expression characteristics, identified through a phylogenetic screen of candidate gene clusters from diverse bacteria and archaea. The resulting constructs offer major qualitative and quantitative improvements, including much stronger ultrasound contrast, the ability to produce nonlinear signals distinguishable from background tissue, and stable long-term expression. We demonstrate the capabilities of these next-generation ARGs by imaging in situ gene expression in mouse models of breast cancer and tumor-homing therapeutic bacteria, noninvasively revealing the unique spatial distributions of tumor growth and colonization by therapeutic cells in living subjects and providing real-time guidance for interventions such as needle biopsies.

bioengineering↗

bioRxiv2021

ANDOR and beyond: dynamically switchable logic gates as modules for flexible information processing

Understanding how complex (bio-)chemical pathways and regulatory networks may be capable of processing information in efficient, flexible, and robust ways is a key question with implications touching fields across biology, systems biology, biochemistry, synthetic biology, dynamical systems theory, and network science. Considerable effort has been focused on the identification and characterization of structural motifs in these signaling networks, and companion efforts have instead sought to cast their operation as controlled by dynamical modules that appear out of dynamical correlations during information processing. While both these approaches have been successful in many examples of biological information processing, cases in which the signaling or regulatory network exhibits multi-functionality or context dependence remain problematic. We here propose a small set of higher-order effective modules that simultaneously incorporate both network structure and the attendant dynamical landscape. In so doing, we render effective computational units that can perform different logical operations based purely on the basin of attraction in which the network dynamics resides or is steered to. These dynamically switchable biochemical logic gates require fewer chemical components or gene products overall than their traditional analogs where static, separate gates are used for each desired function. We demonstrate the applicability and limits of these flexible gates by determining a robust range of parameters over which they correctly operate and further characterize the resilience of their function against intrinsic noise of the constituent reactions using the theory of large deviations. We also show the capability of this framework for general computations by designing a binary adder/subtractor circuit composed of only six components.

biophysics↗

bioRxiv2022

Biosynthesis of pleuromutilin congeners using an Aspergillus oryzae expression platform

Pleuromutilin is an antibiotic diterpenoid made by Clitopilus passeckerianus and related fungi, and it is the progenitor of a growing class of semi-synthetic antibiotics used in veterinary and human medicine. To harness the biotechnological potential of this natural product class, a full understanding of its biosynthetic pathway is essential. Previously, a linear pathway for pleuromutilin biosynthesis was established. Here we report two shunt pathways involving Pl-sdr and Pl-atf that were identified through the rational heterologous expression of combinations of pleuromutilin biosynthetic genes in Aspergillus oryzae. Three novel pleuromutilin congeners were isolated, and their antimicrobial activity was investigated, alongside that of an additional derivative produced through a semi-synthetic approach. It was observed that the absence of substituents - C-3 keto, C-11 hydroxy or C-21 keto - from the pleuromutilin core affected the antibacterial activity of pleuromutilin congeners. This study expands our knowledge on the biosynthesis of pleuromutilin and provides avenues for the development of novel pleuromutilin analogues by combining synthetic biology and synthetic chemistry.

microbiology↗

bioRxiv2024

Differential amino acid usage leads to ubiquitous edge effect in proteomes across domains of life that can be explained by amino acid secondary structure propensities

BackgroundAmino acids are the building blocks of proteins and enzymes, which are pivotal for life on Earth. Amino acid usage provides critical insights into the functional constraints acting on proteins and illuminates molecular mechanisms underpinning traits. Despite this, we have limited knowledge of the genome-wide signatures of amino acid usage across domains of life, precluding new genome and proteome patterns to being discovered. ResultsHere, we analysed the proteomes of 5,590 species across four domains of life and found that only a small subset of amino acids is most and least frequently used across proteomes. This creates a ubiquitous edge effect on amino acid usage diversity by rank that arises from protein secondary structural constrains. This edge effect was not driven by the evolutionary chronology of amino acids, showing that functional rather than evolutionary constrains shape amino acid usage in the proteome. We also tested contemporary hypotheses about similarities in amino acid usage profiles and the relationship between amino acid usage and growth temperature, and found that, contrary to previous beliefs, amino acid usage varies across domains of life and temperature only weakly contributes to variance in amino acid usage. ConclusionWe have described a novel and ubiquitous pattern of amino acid usage signature across genomes, which reveals how structural constrains shape amino acid usage at the proteome level. This can ultimately influence the way in which we probe deep evolutionary relationships of protein families across the tree of life and engineer biology in synthetic biology.

molecular biology↗

bioRxiv2024

Assembling a true "Olympic Gel" from >16,000 combinatorial DNA rings

Olympic gels are an elusive form of soft matter, comprising a three-dimensional network of mechanically interlocked cyclic molecules. In the absence of defined network junctions, the high conformational freedom of the molecules was previously theorized to confer unique mechanical properties to Olympic gels, such as non-linear elasticity and unconventional swelling characteristics. However, the synthesis of an Olympic gel exhibiting these intriguing features is challenging, since unintended crosslinking and polymerization processes are often favored over cyclization. Here, we report the successful assembly of a true Olympic gel from a library of DNA rings comprising more than 16,000 distinct molecules. Each of these rings contains a unique sequence domain that can be enzymatically activated to produce reactive termini that favor intramolecular cyclization. We characterized the genetic, mechanical, and structural characteristics of the material by next-generation sequencing, oscillatory rheology, large-scale computational simulations, atomic force microscopy, and cryogenic electron microscopy. Our results confirm the formation of a stable Olympic gel, which exhibits unique swelling behavior and an elastic response that is exclusively determined by entanglements, yet persists on long time scales. By combining concepts from polymer physics, synthetic biology, and DNA nanotechnology, this new material class provides a flexible experimental platform for future studies into the effects of network topology on macroscopic material properties and its function as a carrier of genetic information in biological and biomimetic systems. This work moreover demonstrates that exotic material properties can emerge in systems with a high compositional complexity that is more reminiscent of biological than synthetic matter.

bioengineering↗

bioRxiv2025

Coarse-Grained RNA Model for the Martini 3 Force Field

In this work we developed a coarse-grained model for RNA that is compatible with the Martini 3 force field. The model is parameterized following the Martini philosophy combining the top-down and bottom-up approaches. The nonbonded interactions in the model are derived from the partitioning of nucleobases between polar and nonpolar solvents, along with calculations of the potential of mean force between bases. For bonded interactions, parameters were refined based on atomistic simulations of double-stranded RNA. Additionally, an elastic network was incorporated to maintain the structural integrity of complex RNA molecules, such as transfer RNA, and other specific RNA configurations. We present the implementation of the Martini 3 RNA model and demonstrate its ability to capture the properties of individual bases, single-stranded RNA, double-stranded RNA, and RNA-protein complexes. Compared to the Martini 2 version, the current model offers several key advantages. It is fully compatible with the updated Martini 3 force field, exhibits greater numerical stability--allowing for the successful simulation of larger RNA-protein complexes, such as ribosomes, using the standard Martini timestep of 20 fs, and it demonstrates improved agreement with all-atom models and experimental data. This new RNA model enables realistic large-scale explicit-solvent molecular dynamics simulations of complex RNA-containing systems. SignificanceThis research introduces a new coarse-grained model for explicit water MD simulations of RNA compatible with Martini 3 software. The model demonstrates improved agreement with both all-atom simulations and experimental data, enabling more accurate and computationally efficient simulations of large RNA-protein complexes. This advancement facilitates the study of RNA and RNA-protein interactions, allowing for the modeling of larger biological complexes and paving the way for more efficient simulations of RNA systems across various fields, including therapeutics, molecular biology, and synthetic biology, where understanding RNA interactions is crucial for developing biomedical applications and advancing fundamental research.

biophysics↗

bioRxiv2018

Stochasticity of cellular growth: sources, propagation and consequences

Cellular growth impacts a range of phenotypic responses. Identifying the sources of fluctuations in growth and how they propagate across the cellular machinery can unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. In addition to several population-averaged data, the model quantitatively recovers how growth fluctuations in single cells change across nutrient conditions. We develop a framework to analyse stochastic chemical reactions coupled with cell divisions and use it to identify sources of growth heterogeneity. By visualising cross-correlations we then determine how such initial fluctuations propagate to growth rate and affect other cell processes. We further study antibiotic responses and find that complex drug-nutrient interactions can both enhance and suppress heterogeneity. Our results provide a predictive framework to integrate single-cell and bulk data and draw testable predictions with implications for antibiotic tolerance, evolutionary biology and synthetic biology.

systems biology↗

bioRxiv2018

Convolutional models of RNA energetics

Nucleic acid molecular biology and synthetic biology are undergoing rapid advances with the emergence of designer riboswitches controlling living cells, CRISPR/Cas9-based genome editing, high-throughput RNA-based silencing, and reengineering of mRNA translation. Many of these efforts require the design of nucleic acid interactions, which relies on accurate models for DNA and RNA energetics. Existing models utilize nearest neighbor rules, which were parameterized through careful optical melting measurements. However, these relatively simple rules often fail to quantitatively account for the biophysical behavior of molecules even in vitro, let alone in vivo. This is due to the limited experimental throughput of optical melting experiments and the infinitely large space of possible motifs that can be formed. Here, we present a convolutional neural network architecture to model the energies of nucleic acid motifs, allowing for learning of representations of physical interactions that generalize to arbitrary unmeasured motifs. First, we used existing parameterizations of motif energies to train the model and demonstrate that our model is expressive enough to recapitulate the current model. Then, through training on optical melting datasets from the literature, we have shown that the model can accurately predict the thermodynamics of hairpins containing unmeasured motifs. This work demonstrates the utility of convolutional models for capturing the thermodynamic parameters that underlie nucleic acid interactions.

biophysics↗

bioRxiv2018

High gene dosage changes transcriptional regulation of genetic structures and contributes to diversity and heterogeneity

Differences in gene dosages between and within species are widely observed. Nevertheless, our understanding of the impacts of gene dosages on transcriptional regulation is far from complete and it is largely ignored in many genetic studies. In this article, we showed that dynamic properties of three genetic structures became significantly different when gene dosages were high and, for self-inhibiting genes, high gene dosages could facilitate monoallelic expression at single cell level and heterogeneity of cell population. This shed lights on potential links between levels of ploidy and phenotypes. In addition, in the context of modern molecular biology, especially synthetic biology, gene dosage needs to be taken into consideration when designing or modifying genetic circuits.

systems biology↗

bioRxiv2021

Mobius Assembly for Plant Systems highlights promoter-terminator interaction in gene regulation

Plant synthetic biology is a fast-evolving field that employs engineering principles to empower research and bioproduction in plant systems. Nevertheless, in the whole synthetic biology landscape, plant systems lag compared to microbial and mammalian systems. When it comes to multigene delivery to plants, the predictability of the outcome is decreased since it depends on three different chassis: E. coli, Agrobacterium, and the plant species. Here we aimed to develop standardised and streamlined tools for genetic engineering in plant synthetic biology. We have devised Mobius Assembly for Plant Systems (MAPS), a user-friendly Golden Gate Assembly system for fast and easy generation of complex DNA constructs. MAPS is based on a new group of small plant binary vectors (pMAPs) that contains an origin of replication from a cryptic plasmid of Paracoccus pantotrophus. The functionality of the pMAP vectors was confirmed by transforming the MM1 cell culture, demonstrating for the first time that plant transformation is dependent on the Agrobacterium strains and plasmids; plasmid stability was highly dependent on the plasmid and bacterial strain. We made a library of new short promoters and terminators and characterised them using a high-throughput protoplast expression assay. Our results underscored the strong influence of terminators in gene expression, and they altered the strength of promoters in some combinations and indicated the presence of synergistic interactions between promoters and terminators. Overall this work will further facilitate plant synthetic biology and contribute to improving its predictability, which is challenged by combinatorial interactions among the genetic parts, vectors, and chassis.

synthetic biology↗

bioRxiv2020

Model Reduction Tools For Phenomenological Modeling of Input-Controlled Biological Circuits

We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits from descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical reaction model of the circuit under the assumptions of mass-action kinetics using various existing tools. However, using these models to guide design decisions during an experiment is difficult due to a large number of reaction rate parameters and species in the model. Hence, phenomenological models are often developed that describe the effective relationships among the circuit inputs, outputs, and only the key states and parameters. In this paper, we present an algorithm to obtain these phenomenological models in an automated manner using a Python package for circuits with inputs that control the desired outputs. This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species abundance to obtain the reduced models that can be used for design of synthetic biological circuits. We consider an example of a simple gene expression circuit and another example of a layered genetic feedback control circuit to demonstrate the use of the model reduction procedure.

synthetic biology↗

bioRxiv2023

In-vivo multicellular control of gene expression in microbial consortia

This paper presents an innovative approach in synthetic biology, focusing on the engineering of biomolecular feedback control systems within microbial consortia to achieve robust and precise regulation of desired phenotypes. Traditional biomolecular control strategies, while effective, are predominantly confined to single-cell applications, limiting their complexity and adaptability due to metabolic constraints. We propose a novel methodology that distributes control functionalities across different cell populations within a microbial consortium. This approach leverages the division of labor and cooperative interactions among microbial populations, significantly enhancing flexibility and robustness. We build on the foundation of synthetically engineered microbial consortia, known for their applications in complex compound production and advanced computational processes. However, the challenge of implementing a distributed feedback control loop for precise phenotype regulation in synthetic communities has remained unaddressed. Our work closes this gap by developing and validating an in-vivo feedback control loop distributed across a bacterial consortium, specifically designed using two Escherichia coli strains. These strains are engineered to perform the three essential control functions: computation, sensing, and actuation. We employ orthogonal quorum sensing signaling molecules for communication between the controller and target cell populations. Through in-vivo experiments, we compare closed-loop and open-loop configurations to demonstrate the effectiveness of our approach. The results show that our distributed control system can regulate gene expression levels in target populations with high precision and robustness, even in the presence of consortium composition variations. This feature is particularly vital for practical applications in synthetic biology where stability and adaptability are crucial. Our study not only addresses a significant gap in synthetic biology literature but also opens avenues for more complex and reliable synthetic biology applications.

synthetic biology↗