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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 883 records · Page 49Linked to original sources

bioRxiv2020

BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts

Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions. Author summaryThis paper describes a new software package BioCRNpyler (pronounced "Biocompiler") designed to support rapid development and exploration of mathematical models of biochemical networks and circuits by computational biologists, systems biologists, and synthetic biologists. BioCRNpyler allows its users to generate large complex models using very few lines of code in a way that is modular. To do this, BioCRNpyler uses a powerful new representation of biochemical circuits which defines their parts, underlying biochemical mechanisms, and chemical context independently. BioCRNpyler was developed as a Python scripting language designed to be accessible to beginning users as well as easily extendable and customizable for advanced users. Ultimately, we see Biocrnpyler being used to accelerate computer automated design of biochemical circuits and model driven hypothesis generation in biology.

synthetic biology↗

bioRxiv2020

BERTology Meets Biology: Interpreting Attention in Protein Language Models

Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at https://github.com/salesforce/provis.

bioinformatics↗

bioRxiv2022

CRI-SPA, a mating based CRISPR-Cas9 assisted method for high-throughput genetic modification of yeast strain libraries

AbstractBiological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and the identification of phenotypic maximas would be of great benefit to synthetic biology. Thus, new research tools allowing simple and rapid mapping of sequence to function are required to forward these research fields. Here, we describe CRI-SPA, a method allowing the transfer of a chromosomal genetic feature from a donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion and Selective Ploidy Ablation and is executed within a week. We demonstrate the power of CRI-SPA by transferring four genes responsible for the production of betaxanthin, a yellow biosensor for the morphine precursor L-DOPA, into each strain of the yeast knock-out collection ({approx}4800 strains), providing a genome-wide overview of the genetic requirements for betaxanthin production. CRI-SPA is fast, highly reproducible, can be massively parallelized with automation and does not require selection for the transferred genetic feature.

systems biology↗

bioRxiv2024

Diversified, miniaturized and ancestral parts for mammalian genome engineering and molecular recording

As the synthetic biology and genome engineering fields mature and converge, there is a clear need for a "parts list" of components that are diversified with respect to both functional activity (to facilitate design) and primary sequence (to facilitate assembly). Here we designed libraries composed of extant, ancestral, mutagenized or miniaturized variants of Pol III promoters or guide RNA (gRNA) scaffolds and quantified their ability to mediate precise edits to the mammalian genome via multiplex prime editing. We identified thousands of parts that reproducibly drive a range of editing activities in human and mouse stem cells and cancer cell lines, including hundreds exhibiting similar or greater activity than the sequences used in conventional genome engineering constructs. We further conducted saturation mutagenesis screens of canonical Pol III promoters (U6p, 7SKp, H1p) and the prime editing guide RNA (pegRNA) scaffold, which identified tolerated variants that can be superimposed on baseline parts to further enhance sequence diversity. While characterizing thousands of orthologous promoters from hundreds of extant or ancestral genomes, we incidentally mapped the functional landscape of mammalian Pol III promoter evolution. Finally, to showcase the usefulness of these parts, we designed a "ten key" molecular recording array that lacks repetitive subsequences in order to facilitate its one-step assembly in yeast. Upon delivering this 15.8 kb tandem array of promoters and guides to mammalian cells, individual pegRNAs exhibited balanced activities as predicted by the activity of component parts, despite their relocation to a single locus. Looking forward, we anticipate that the diversified parts and variant effect maps reported here can be leveraged for the design, assembly and deployment of synthetic loci encoding arrays of gRNAs exhibiting predictable, differentiated levels of activity, which will be useful for multiplex perturbation, advanced biological recorders and complex genetic circuits.

synthetic biology↗

bioRxiv2023

Fully addressable, designer superstructures assembled from a single modular DNA origami

Intricate self-organization is essential in many biological processes, underpinning vital functions and interactions. In an effort to mimic such processes, synthetic biology aims to engineer dynamic structures with controllable functions using nanotechnological tools. A key requirement of engineered building blocks is the ability to assemble and disassemble hierarchically with precision. Using the DNA origami technique, we here present the moDON, a modular DNA origami nanostructure, which is capable of assembling into almost 20 000 diverse monomers, forming complex and controlled superstructures in three dimensions. While shape and addressability of DNA origami are nearly arbitrary, its overall size is limited by the scaffold size. Previous methods of extending the size of DNA origami (e.g. hierarchical assembly, modified scaffolds, etc.), either led to loss over control of shape and addressability beyond monomers or to proportionally increased cost and design effort. With the moDON we were able to overcome both issues. The modular design combines xy- and z-plane assembly methods, enabling the construction of finite and periodic structures beyond 1 GDa. We demonstrate xy-z orthogonality, by enabling controlled selective or parallel assembly and disassembly via distinct orthogonal triggers. The kinetic profile of assembly and disassembly aligns with biological time scales, paving the way for applications in dynamic nanomachinery and advanced biomaterials. Finally, we showcase the conjugation of gold nanoparticles to specific positions within superstructures, underscoring the efficacy of this approach for creating intricate and orthogonal nanoscale architectures with preserved site-specific addressability. The moDON thus offers an efficient, cost-effective solution for constructing large, precisely organized, and fully addressable structures with vast potential in synthetic cellular systems design.

biophysics↗

bioRxiv2022

Automated design of gene circuits with optimal mushroom-bifurcation behaviour

Recent advances in synthetic biology are enabling exciting technologies, including the next generation of biosensors, the rational design of cell memory, modulated synthetic cell differentiation and generic multi-functional bio-circuits. These novel applications require the design of gene circuits leading to sophisticated behaviours and functionalities. At the same time, designs need to be kept minimal to avoid compromising cell viability. Bifurcation theory of dynamical systems provides powerful tools to address complex nonlinear dynamics and multifunctionality, linking model topology and kinetic parameters with circuit behaviour. However, the challenge of incorporating bifurcation analysis to automated design has not been accomplished so far. In this work we present an optimisation-based method for the automated forward design of synthetic gene circuits with specified bifurcation diagrams, allowing us to find minimal topologies optimizing the required functionalities and taking into account additional requirements and/or context specifications. We apply the method to design of gene circuits exhibiting the so called mushroom bifurcation, a relatively unexplored multi-functional behaviour of particular relevance for developmental biology. Using the results of the optimisation analysis we explore the capabilities of the resulting circuits for possible applications in advanced biosensors, memory devices, and synthetic cell differentiation.

systems biology↗

bioRxiv2021

Developing an enzyme selection tool supporting multiple hosts contexts

Engineering biological organisms that allow the integration of alternative metabolic pathways to natural ones is one of the goals of synthetic biology. Based on this, some of the most attractive applications in terms of synthetic organisms manufacture include the production of a wide range of pharmacologically useful metabolites produced in a sustainable and environmentally friendly way. Also, the biostable molecules green-production involves different types of therapeutic processes, e.g. prostheses and grafts stabilisation. Regarding the viability of genetically modified organisms, metabolic pathways must be first properly designed, taking into consideration the type of host organism that will be involved in metabolic production, as well as its biochemical and environmental conditions. To ensure the correct growth of these synthetic organisms, the enzyme selection must guarantee both the organism survival (and proliferation) and the optimal production of the desired metabolite. Developing enzyme selection tools is essential to enhance and make cost-effective the metabolic pathways design. This technical note presents the update of Selenzyme, the enzyme selection tool which is based on organisms taxonomic compatibility and allows appropriate enzyme selection considering its amino acid sequence. The purpose of the update is to allow multiple host input, in order to perform an affinity comparison between target organisms and each host. The affinity differences will depend on which host to be considered, allowing the user to select the optimal host for the enzyme concerned.

bioinformatics↗

bioRxiv2021

Structural basis for topological regulation of Tn3 resolvase

Site-specific DNA recombinases play a variety of biological roles, often related to the dissemination of antibiotic resistance, and are also useful synthetic biology tools. The simplest sitespecific recombination systems will recombine any two cognate sites regardless of context. Other systems have evolved elaborate mechanisms, often sensing DNA topology, to ensure that only one of multiple possible recombination products is produced. The closely-related resolvases from the Tn3 and {gamma}{delta} transposons have historically served as paradigms for the regulation of recombinase activity by DNA topology. However, despite many proposals, models of the multi-subunit protein-DNA complex (termed the synaptosome) that enforces this regulation have been unsatisfying due to a lack of experimental constraints and incomplete concordance with experimental data. Here we present new structural and biochemical data that lead to a new, detailed model of the Tn3 synaptosome, and discuss how it harnesses DNA topology to regulate the enzymatic activity of the recombinase.

biochemistry↗

bioRxiv2023

A novel phenylpropanoid methyl esterase enables catabolism of aromatic compounds that inhibit biological nitrification

Agriculture is the largest source of greenhouse gases (GHG) production. Conversion of nitrogen fertilizers into more reduced forms by microbes through a process known as biological nitrification drives GHG production, enhances proliferation of toxic algal blooms, and increases cost of crop production. Some plants reduce biological nitrification in soils by exuding a diverse array of biological nitrification inhibitors (BNIs) that inhibit the ammonium oxidizing microbes responsible for nitrification. Applying synthetic biology to enhance and transfer BNI production into food and bioenergy crops is a promising approach to reduce nitrification but the success of this strategy is contingent upon improving our limited understanding of BNIs mechanisms-of-action and degradation in the soil. We addressed this gap by investigating the previously unknown metabolic route through which a prominent class of aromatic BNIs known as phenylpropanoid methyl esters (PPMEs) are catabolized. While neither transcriptomics (RNAseq) or high-throughput functional genomics (RB-TnSeq) methods reduced the genetic search space for pathway discovery into a tenable number of genes, the combination of narrowed the search space to a collection of 4 proteins of unknown function. Using genetic and biochemical analyses we found that two previously uncharacterized enzymes, including a novel phenylpropanoid methyl esterase, funnel PPMEs into an established phenylpropanoid catabolism pathway. Transfer of these two enzymes into bacteria capable of using other phenylpropanoids use of PPMEs as carbon sources. This work both provided insight into BNI catabolism and is the first step towards development of model in vivo plant-microbe systems for studying BNI mechanisms under well controlled conditions.

microbiology↗

bioRxiv2025

Systematic Review of Over A Century of Global Bioscience Research

Bioscience encompasses studies on living organisms, their components, and their interactions, with the main aim of translating research into useful applications for medical, clinical, industrial, and environmental uses. The broad field of bioscience has witnessed tremendous growth in research output. This study was designed to evaluate the current trends in bioscience research using bibliometric approaches and develop future policy pointers following the innovative systems framework. Research articles focusing on bioscience published between 1883 and 2024 were sourced from the Scopus database. From the retrieved dataset, relevant articles were systematically screened and included for the analysis. Bibliometrix package, VOSviewer, and Microsoft Excel were used to analyze and visualize the parameters. 8,678 documents involving 30,380 authors from diverse institutions across the globe were published in 3,549 sources, with an annual rise of 4.41%. Exponential growth in bioscience research output was observed after 2007 and remained steady till 2024. The United States and China were leading nations for bioscience research. The most relevant affiliation was the University of California, United States, while the most relevant source was the Brazilian Journal of Medical and Biological Research, in terms of the number of publications. Among the authors, Wang Y had the highest number of documents, while the most impactful author was Zhang J. The most frequent keywords in bioscience research included biological research, genetics, procedures, metabolism, biology, DNA, gene expression, proteins, genomics, and fluorescence. Furthermore, themes such as bioinformatics, microRNA, synthetic biology, CRISPR/Cas9, deep learning, multi-omics, and big data represented the recent research interests. The bioscience research landscape is defined by the dominance of high-income countries and a shift toward omics-driven and data-intensive approaches. Yet global participation remains uneven, with limited representation from low- and middle-income regions. This study highlights the need for inclusive, globally coordinated strategies that foster equitable access, capacity building, and cross-regional collaboration.

scientific communication and education↗

bioRxiv2022

Design principles for perfect adaptation in biological networks with nonlinear dynamics

Establishing a mapping between the emergent biological properties and the repository of network structures has been of great relevance in systems and synthetic biology. Adaptation is one such biological property of paramount importance that promotes regulation in the presence of environmental disturbances. This paper presents a nonlinear systems theory-driven framework to identify the design principles for perfect adaptation. Based on the prior information about the network, we frame precise mathematical conditions for adaptation using nonlinear systems theory. We first deduce the mathematical conditions for perfect adaptation for constant input disturbances. Subsequently, we first translate these conditions to specific necessary structural requirements for adaptation in networks of small size and then extend to argue that there exist only two classes of architectures for a network of any size that can provide local adaptation in the entire state space, namely, incoherent feed-forward structure and negative feedback loop with buffer node. The additional positiveness constraints further narrow the admissible set of network structures. This also aids in establishing the global asymptotic stability for the steady state given a constant input disturbance. The entire method does not assume any explicit knowledge of the underlying rate kinetics, barring some minimal assumptions. Finally, we also discuss the infeasibility of the incoherent feed-forward networks (IFFLP) to provide adaptation in the presence of downstream connections. Detailed and extensive simulation studies corroborate the theoretical findings. Moreover, we propose a generic and novel algorithm based on a nonlinear systems theory to unravel the design principles for global adaptation.

systems biology↗

bioRxiv2025

Receptor dimerization enables ligand discrimination through tunable response heterogeneity

Signaling pathways enable cells to coordinate collective behaviors by exchanging specific information. Many pathways utilize multiple ligand variants to activate the same intracellular signaling cascade, raising the question of how cells discriminate between these seemingly redundant signals. It has been shown that individual cells can discriminate between signals based on their induced level of activity, temporal dynamics or combinatorial effect. Here, we demonstrate that ligand discrimination could also emerge at the population level. Using mathematical models of ligand-receptor interactions, we examine how response heterogeneity at the population level can encode ligand identity. We introduce a local scaling metric to quantify how variation in pathway components affects the cellular response. Our results reveal that for pathways with dimeric receptors, and more significantly for heterodimeric receptors, biochemical parameters of the ligands control the resulting heterogeneity in the response of a population of cells. Furthermore, we show that the population-level heterogeneity encodes the enzymatic activity of the resulting receptor complex. This suggests a functional advantage for utilizing heterodimeric receptor complexes in pathways acting across a population of cells, such as the type I interferon pathway, which shows several of the characteristics of our model. This contrasts to juxtacrine pathways, such as Notch, that do not act at the population level and use a single component receptor. Overall, our findings highlight a novel mechanism by which receptor architecture enables cells to encode ligand-specific information through population-level heterogeneity, offering insights into immune regulation, tissue development, and synthetic biology. Significance StatementCells often communicate using distinct molecular signals (ligands) that activate the same pathway. Yet, how cells distinguish between these signals remains unclear. This study reveals that ligand discrimination can emerge at the level of entire cell populations, not just individual cells. By analyzing mathematical models, we show that certain receptor architectures, especially those involving heterodimeric receptors, allow signals to control the heterogeneity of the response level across a population. This mechanism enables cell populations to decode specific information about their environment, impacting processes like immune responses and tissue development. Our findings provide new insights into how signals coordinate collective behaviors and suggest strategies for designing synthetic systems to precisely control biological responses.

systems biology↗

bioRxiv2020

Metabolic Buffer Analysis Reveals the Simultaneous, Independent Control of ATP and Adenylate Energy Ratios

Determining the underlying principles behind biological regulation is important for understanding the principles of life, treating complex diseases, and creating de novo synthetic biology. Buffering - the use of reservoirs of molecules to maintain molecular concentrations - is a widespread and important mechanism for biological regulation. However, a lack of theory has limited our understanding of its roles and quantified effects. Here, we study buffering in energy metabolism using control theory and novel buffer analysis. We find that buffering can enable the simultaneous, independent control of multiple coupled outputs. In metabolism, adenylate kinase and AMP deaminase enable simultaneous control of ATP and adenylate energy ratios, while feedback on metabolic pathways is fundamentally limited to controlling one of these outputs. We also quantify the regulatory effects of the phosphagen system - the above buffers and creatine kinase - revealing which mechanisms regulate which outputs. The results are supported by human muscle and mouse adipocyte data. Together, these results illustrate the synergy of feedback and buffering in molecular biology to simultaneously control multiple outputs.

systems biology↗

bioRxiv2021

AI-assisted Biology: Predict the Conditional Probability Distributions from Noisy Measurements

Biological systems are inherently noisy so that two genetically identical cells in the exact same environment will sometimes behave in dramatically different ways. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic variables or categories per specific input condition. Furthermore, biological noise has been proven to play a crucial role in gene regulation mechanisms. The prediction of the average value of a given phenotype is not always sufficient to fully characterize a given biological system. In this study, we develop a deep learning algorithm that can predict the conditional probability distribution of a phenotype of interest with a small number of observations per input condition. The key innovation of this study is that the deep neural network automatically generates the probability distributions based on only few (10 or less) noisy measurements for each input condition, with no prior knowledge or assumption of the probability distributions. This is extremely useful for exploring unknown biological systems with limited measurements for each input condition, which is linked not only to a better quantitative understanding of biological systems, but also to the design of new ones, as it is in the case of synthetic biology and cellular engineering.

systems biology↗

bioRxiv2025

Random Mutagenesis for the Generation of Repertoire of Aureochrome-based Optogenetic Scaffolds

Engineering of the natural light-regulated transcription factors for the purposeful generation of synthetic photo-switches is a widely adopted strategy in synthetic biology and optogenetics. Aureochrome is one such potential, naturally occurring photoreceptor-cum-transcription factor that can be used for the development of synthetic optogenetic tools, especially meant for light-regulated gene expression. It is however well-known that different biological events demand different duration of signaling state lifetime and diverse affinity towards different DNA substrates. Therefore, in this study, we produce multiple variants of aureochromes via random mutagenesis - mimicking single generation directed evolution. After screening the single generation variants for in-frame transcripts, we sort them on the basis of altered photocycle kinetics as well as DNA-binding affinity. The variants that exhibited reversible photochemistry and light-regulated DNA-binding ability similar/comparable to that of the wild-type despite incurring mutations, are characterized and discussed in this manuscript. These can later be subjected to successive rounds of random mutations to get an eventual superior variety with enhanced functionality. Even the apparently unsuccessful ones, which depicted drastic alteration of photochemical/DNA-binding properties, helped us to identify amino acid residues - lesser known to be indispensable for a certain biological activity. The process of diversification and selection via random mutagenesis not only explains the functional significance of the different amino acids in aureochromes but also generates a repertoire of appropriate aureochrome variants that may be used in optogenetics.

biochemistry↗

bioRxiv2024

Biological oscillations without genetic oscillator or external forcing

Oscillators are fundamental to biological systems, underpinning essential processes such as cell division, circadian rhythms, and developmental cycles. While both natural and synthetic genetic oscillators have been extensively studied, oscillatory behaviors in cells can also emerge without dedicated genetic circuits. In earlier work, we uncovered sustained oscillations in phenotypic switching across diverse cellular systems and gene circuits, occurring spontaneously, without external forcing and linked them to the induction of slow-growing phenotypes. In this study, we identify the conditions that give rise to such intrinsic phenotypic instabilities, leading to population-level oscillations. We develop and analytically solve a simplified mathematical model of a stress-induced phenotype, mapping the range of continuous culture conditions that trigger oscillatory gene expression. This instability range, predicted by the model, was experimentally validated in Bacillus subtilis cultures. Our findings reveal that oscillations can arise in the complete absence of genetic oscillators or external perturbations. Although demonstrated here for a stress response in continuous culture, this phenomenon may occur in any long-term cultivation where environmental feedback links an inducer to the cellular system, broadening the landscape of possible oscillatory behaviors in microbiology and synthetic biology.

systems biology↗

bioRxiv2021

Monodopsis and Vischeria genomes elucidate the biology of eustigmatophyte algae

Members of eustigmatophyte algae, especially Nannochloropsis, have been tapped for biofuel production owing to their exceptionally high lipid content. While extensive genomic, transcriptomic, and synthetic biology toolkits have been made available for Nannochloropsis, very little is known about other eustigmatophytes. Here we present three near-chromosomal and gapless genome assemblies of Monodopsis (60 Mb) and Vischeria (106 Mb), which are the sister groups to Nannochloropsis. These genomes contain unusually high percentages of simple repeats, ranging from 12% to 21% of the total assembly size. Unlike Nannochloropsis, LINE repeats are abundant in Monodopsis and Vischeria and might constitute the centromeric regions. We found that both mevalonate and non-mevalonate pathways for terpenoid biosynthesis are present in Monodopsis and Vischeria, which is different from Nannochloropsis that has only the latter. Our analysis further revealed extensive spliced leader trans-splicing in Monodopsis and Vischeria at 36-61% of genes. Altogether, the high-quality genomes of Monodopsis and Vischeria not only serve as the much-needed outgroups to advance Nannochloropsis research, but also shed new light on the biology and evolution of eustigmatophyte algae.

genomics↗

bioRxiv2024

Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants

O_LISynthetic biology has the potential to revolutionize biotechnology, public health and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed. C_LIO_LIWe have adapted bacterial allosteric transcription factors (aTFs) to control gene expression in plants in a ligand-specific manner. The aTFs used here function as transcription repressors of semi-synthetic promoters, and aTF activity is regulated by specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations. C_LIO_LIThree aTFs, CouR, FapR and TtgR, were able to achieve [~]95% repression of their respective target promoters. For TtgR, a 6-fold de-repression could be triggered by inducing its ligand (naringenin) accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit. C_LIO_LIWe showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a pre-defined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products. C_LI

plant biology↗