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Results for “Synthetic Biology”

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bioRxiv2025

Design principles of neuromorphic computing using genetic circuits

Cells have evolved to sense a wide range of input combinations and integrate those signals through signaling pathways to produce context-specific responses, such as differentiation, cell-type specification, and patterning. To replicate this information-processing capacity, synthetic biology has developed large-scale circuitry inspired by the fundamental principles of computer science. Within this framework, neuromorphic computing implemented using genetic circuits offers the opportunity to significantly enhance the computational capabilities of single cells. In this work, we establish design principles for implementing neuromorphic computing in living cells by identifying the key feature that enables a chemical reaction network to function as a perceptron: an input-output mapping with a tunable threshold. We demonstrate that four ubiquitous chemical reaction networks, namely molecular sequestration, catalytic degradation, competitive binding, and activation/deactivation cycles, all satisfy this requirement and can be engineered as perceptrons. By layering these perceptrons into multi-layer architectures, we then show how to construct both linear and nonlinear decision boundaries through rational tuning of production rates that encode network weights. As proof of principle, we apply this framework to design neural networks capable of discriminating between healthy and cancer cells based on gene expression data from 19 tissue types. Together, this work formalizes the design principles for engineering genetic circuits as neural networks and establishes a foundation for implementing next-generation cellular computation.

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bioRxiv2025

Magnetized Cellbots to Spatiotemporally Control Differentiation of Human-Induced Pluripotent Stem Cells

Precise spatiotemporal control of gene expression and cellular differentiation is essential for engineering native-like multicellular structures. Current cell differentiation approaches typically rely on externally provided inputs whose effects are not targeted to distinct cells in the appropriate state and hence cannot spatially organize and mature tissue structures as needed. Our work introduces a magnetically controlled microrobot (MR) platform for guiding mammalian cells to desired locations that, combined with synthetic biology, delivers biological signals at precise locations and times, enabling spatiotemporal control of cell-fate decision-making. We use synNotch, a cell-cell contact-based biological signaling that induces relevant gene expression in receivers when the receiver cells contact sender cells through ligand-receptor binding. Magnetically driven MRs are then allowed to be internalized by sender cells, resulting in magnetized sender cellbots. Using a 3-pair orthogonal Helmholtz coil system, we guided magnetized sender cellbots to precise locations in a receiver cell culture, activating desired fluorescent protein expression in target Chinese Hamster Ovary (CHO) receiver cells. Next, we engineered Human-Induced Pluripotent Stem Cells (hiPSC) to function as receivers that can be instructed by senders to differentiate into endothelial cells (ECs) via overexpression of ETV2 (ETS variant transcription factor 2), a master transcriptional regulator of endothelial cell development. Using our magnetic platform, we guided multiple sender cellbots to target locations on a monolayer of hiPSC receivers, resulting in differentiation of receivers into ECs and possible onset of vascular formation. Our approach provides a foundation for the engineering spatial patterns by activating conditional triggers based on MR location and cell state at multiple time points, enabling several applications such as control of organoid architecture.

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bioRxiv2026

SYNBRIDGE: A VERSATILE FRAMEWORK FOR BIDIRECTIONAL INTERCONVERSION BETWEEN BIOBRICKS AND GOLDEN GATE ASSEMBLY STANDARDS

Component reusability and interoperability are fundamental to synthetic biology. While BioBricks laid the groundwork for part domestication, Golden Gate-based assembly standards, in particular Modular Cloning (MoClo), revolutionized multipart assembly. Despite widespread adoption, standardized conversion between these two popular frameworks remains challenging due to sequence incompatibilities. While de novo synthesis remains constrained by cost and sequence complexity, conventional PCR-based conversion suffers from off-target binding driven by sequence redundancy in multigene devices, unscalable primer optimization costs, and amplicon size constraints. To address interconversion from an engineering perspective, we present SynBridge: a closed-loop idempotent workflow for bidirectional BioBrick-MoClo conversion built around two core operations, Bri2Mo and Mo2Bri. As a proof of concept, we validated SynBridge by constructing a composite genetic system integrating a BioBrick module for LuxR-mediated RFP expression with a MoClo-assembled device for thermoregulated GFP expression. Quantitative cloning efficiency and functional characterization confirmed a robust and reproducible workflow that operates without requiring ad-hoc solutions. SynBridge provides a versatile engineering tool that ensures backward compatibility, maximizes part reusability, and establishes a standardized interchangeability framework suitable for automated procedures.

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bioRxiv2022

Overcoming leak sensitivity in CRISPRi circuits using antisense RNA sequestration and regulatory feedback

The controlled binding of the catalytically-dead CRISPR nuclease (dCas) to DNA can be used to create complex, programmable transcriptional genetic circuits, a fundamental goal of synthetic biology. This approach, called CRISPR interference (CRISPRi), is advantageous over existing methods because the programmable nature of CRISPR proteins enables the simultaneous regulation of many different targets without crosstalk. However, such gene circuit elements are limited by 1) the sensitivity to leaky repression of CRISPRi logic gates and 2) retroactive effects owing to a shared pool of dCas proteins. By utilizing antisense RNAs (asRNAs) to sequester guide RNA transcripts, as well as CRISPRi feedback to self-regulate asRNA production, we demonstrate a mechanism that suppresses unwanted CRISPRi repression and improve logical gene circuit function in E. coli. This improvement is particularly pronounced during stationary expression when CRISPRi circuits do not achieve the expected regulatory dynamics. Further, the use of dual CRISPRi/asRNA inverters restores logical performance of layered circuits such as a double inverter. By studying circuit induction at the single cell level in microfluidic channels, we provide insight into the dynamics of antisense sequestration of gRNA and regulatory feedback on dCas-based repression and derepression. These results demonstrate how CRISPRi inverters can be improved for use in more complex genetic circuitry without sacrificing the programmability and orthogonality of dCas proteins.

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bioRxiv2023

Microbial cell factory optimisation using genome-wide host-pathway interaction screens

The ubiquity of genetic interactions in living cells challenges the concept of parts orthogonality, which is a cornerstone of synthetic biology. Parts, such as heterologously expressed genes, draw from shared pools of limited cellular resources and interactions between parts themselves and their host are inevitable. Instead of trying to eliminate or disregard these interactions, we propose to leverage them to promote desirable phenotypes. We recently described CRI-SPA, a method for high-throughput genome-wide gene delivery and screening of host:pathway interactions in Saccharomyces cerevisiae. In this study, we combine this method with biosensor-based high-throughput screening and high-density colony image analysis to identify lead engineering targets for optimising cis-cis-muconic acid (CCM) production in yeast cell factories. Using the biosensor screen, we phenotype >9,700 genotypes for their interaction with the heterologously expressed CCM biosynthesis pathway, including both gene knock-out and overexpression, and identify novel metabolic targets belonging to sulphur assimilation and methionine synthesis, as well as cellular redox homeostasis, positively impacting CCM biosynthesis by up to 280%. Our genome-wide exploration of host pathway interaction opens novel strategies for the metabolic engineering of yeast cell factories.

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bioRxiv2021

Minicells from Highly Genome Reduced Escherichia coli: Cytoplasmic and Surface Expression of Recombinant Proteins and Incorporation in the Minicells

Minicells, small cells lacking a chromosome, produced by bacteria with mutated min genes, which control cell division septum placement, have many potential uses. Minicells have contributed to basic bacterial physiology studies and can enable new biotechnological applications, including drug delivery and vaccines. Genome-reduced (GR) bacteria are another informative area of investigation. Investigators identified that with even almost 30% of the E. coli genome deleted, the bacteria still live. In biotechnology and synthetic biology, GR bacteria offer certain advantages. With GR bacteria, more recombinant genes can be placed into GR chromosomes and fewer cell resources are devoted to purposes apart from biotechnological goals. Here, we show that these two technologies can be combined: min mutants can be made in GR E. coli. The minCminD mutant GR E. coli produce minicells that concentrate engineered recombinant proteins within these spherical delivery systems. We expressed recombinant GFP protein in the cytoplasm of GR bacteria and showed that it is concentrated within the minicells. We also expressed proteins on the surfaces of minicells made from GR bacteria using a recombinant Gram-negative AIDA-I autotransporter expression cassette. As some autotransporters, like AIDA-I, are concentrated at the bacterial poles, where minicells bud, and because the surface-to-volume ratio of the small minicells is higher than bacteria, recombinant proteins expressed on surfaces of the GR bacteria are concentrated on the minicells. Minicells made from GR bacteria can enable useful biotechnological innovations, such as drug delivery vehicles and vaccine immunogens.

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bioRxiv2022

Development of a dedicated Golden Gate Assembly platform (RtGGA) for Rhodotorula toruloides

Rhodotorula toruloides is a potential chassis to microbial cell factories as this yeast can metabolise different substrates into a diverse range of natural products, but the lack of efficient synthetic biology tools hinders its applicability. In this study, the modular, versatile and efficient Golden Gate DNA assembly system was adapted to the first basidiomycete, an oleaginous yeast R. toruloides (RtGGA). R. toruloides CCT 0783 was sequenced, and used for the RtGGA design. The DNA fragments were assembled with predesigned 4-nt overhangs and a library of standardized parts was created containing promoters, genes, terminators, insertional regions, and resistance genes. The library was combined to create cassettes for the characterization of promoters strength and to overexpress the carotenoid production pathway. A variety of reagents, plasmids, and strategies were used and the RtGGA proved to be robust. The RtGGA was used to build three versions of the carotenoid overexpression cassette were built by using different promoter combinations. The cassettes were transformed into R. toruloides and the three new strains were characterized. Total carotenoid concentration increased by 41%. The dedicated GGA platform fills a gap in the advanced genome engineering toolkit for R. toruloides, enabling the efficient design of complex metabolic pathways.

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bioRxiv2025

Cellulase secretion by engineered Pseudomonas putida enables growth on cellulose oligomers.

Pseudomonas putida is an attractive synthetic biology platform organism for chemical synthesis from low-grade feedstocks due to its high tolerance to chemical solvents and lignin-derived small molecules that are often inhibitory to other biotechnologically relevant microorganisms. However, there are few molecular tools available for engineering P. putida and other gram-negative bacteria to secrete non-native enzymes for extracellular feedstock depolymerisation. In this study P. putida was transformed to secrete cellulase enzymes and evaluated for growth on polymeric or oligomeric cellulose substrates. Active exo- and endocellulase enzymes were secreted into the culture supernatant, and a preferred set of twin-arginine translocase secretion signal peptides were identified. Extracellular cellulase activity was sufficient to support growth of P. putida using cellotriose or cellotetraose as the sole source of carbon and energy. This work supports progress towards consolidated bioprocessing of cellulosic materials using P. putida, and advances the state of engineered protein secretion in gram negative bacteria. Key PointsO_LIEngineered Pseudomonas putida secreted cellulase enzymes into the culture medium C_LIO_LICellulase activity was sufficient to support growth on cellulose oligomers C_LI

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bioRxiv2019

An endoribonuclease-based incoherent feedforward loop for decoupling resource-limited genetic modules

Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a devices output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is competition among genes for shared cellular resources, such as those required for transcription and translation, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource sharing on engineered genetic systems in mammalian cells and develop an endoribonuclease-based incoherent feedforward loop (iFFL) to make gene expression levels robust to changes in resource availability. Our iFFL accurately controls gene expression levels in various cell lines and in the presence of significant resource sequestration by transcriptional activators. In addition to mitigating resource sharing, our iFFL also adapts gene expression to multiple log decades of DNA copy number variation, substantially improving upon previously-described miRNA-based iFFLs. Ultimately, our iFFL device will enable predictable, robust, and context-independent control of gene expression in mammalian cells.

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bioRxiv2015

Improving fold activation of small transcription activating RNAs (STARs) with rational RNA engineering strategies.

Engineered RNAs have become integral components of the synthetic biology and bioengineering toolbox for controlling gene expression. We recently expanded this toolbox by creating small transcription activating RNAs (STARs) that act by disrupting the formation of a target transcriptional terminator hairpin placed upstream of a gene. While STARs are a promising addition to the repertoire of RNA regulators, much work remains to be done to optimize the fold activation of these systems. Here we apply rational RNA engineering strategies to improve the fold activation of two STAR regulators. We demonstrate that a combination of promoter strength tuning and multiple RNA stabilization strategies can improve fold activation from 5.4-fold to 13.4-fold for a STAR regulator derived from the pbuE riboswitch terminator. We then validate the generality of our approach and show that these same strategies improve fold activation from 2.1-fold to 14.6-fold for an unrelated STAR regulator. We also establish that the optimizations preserve the orthogonality of these STARs between themselves and a set of antisense RNA transcriptional repressors, enabling these optimized STARs to be used in more sophisticated circuits. These optimization strategies open the door for creating a generation of additional STARs to use in a broad array of biotechnologies.

Synthetic Biology↗

bioRxiv2017

Optimization-based synthesis of stochastic biocircuits with statistical specifications

Model-guided design has become a standard approach to engineering biomolecular circuits in current synthetic biology. However, the stochastic nature of biomolecular reactions is often overlooked in the design process. As a result, cell-cell heterogeneity causes unexpected deviation of biocircuit behaviors from model predictions and requires additional iterations of design-build-test cycles. To enhance the design process of stochastic biocircuits, this paper presents a computational framework to systematically specify the level of intrinsic noise using well-defined metrics of statistics and design highly heterogeneous biocircuits based on the specifications. Specifically, we use descriptive statistics of population distributions as an intuitive specification language of stochastic biocircuits and develop an optimization based computational tool that explores parameter configurations satisfying design requirements. Sensitivity analysis methods are also developed to ensure the robustness of a biocircuit design. These design tools are formulated using convex optimization programs to enable efficient and rigorous quantification of the statistics without approximation, and thus, they are amenable to the synthesis of stochastic biocircuits that require high reliability. We demonstrate these features by designing a stochastic negative feedback biocircuit that satisfies multiple statistical constraints. In particular, we use a rigorously quantified parameter map of feasible design space to perform in-depth study of noise propagation and regulation in negative feedback pathways.

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bioRxiv2021

BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis

SummaryModelling in Synthetic Biology constitutes a powerful tool in our continuous search for improved performance with rational Design-Build-Test-Learn approach. In particular, kinetic models unravel system dynamics, enabling system analysis for guiding experimental designs. However, a systematic yet modular pipeline that allows one to identify the "right" model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we introduce a unified python package, BMSS2, which offers the principal tools in model development and analysis--simulation, Bayesian parameter inference, global sensitivity analysis, with an emphasis on model selection, and a priori and a posteriori identifiability analysis. The whole package is database-driven to support interactive retrieving and storing of models for reusability. This allows ease of manipulation and deposition of models for the model selection and analysis process, thus enabling better utilization of models in guiding experimental designs. Availability and ImplementationThe python package and examples are available on https://github.com/EngBioNUS/BMSS2. A web page which allows users to browse and download the models (SBML format) in MBase is also available with the link provided on GitHub. Supplementary InformationSupplementary data is available.

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bioRxiv2024

A general strategy to engineer high-performance mammalian Whole-Cell Biosensors

Transcription-based whole-cell biosensors (WCBs) are cells engineered with an analyte-responsive promoter driving the transcription of a reporter gene. WCBs can sense and report on bioactive molecules (analytes) relevant to human health. Designing an analyte-sensitive promoter requires a cumbersome trial-and-error approach and usually results in biosensors with poor performance. Here, we integrated Synthetic Biology with Control Engineering to design, computationally model, and experimentally implement high-performance biosensors in mammalian cells. Our approach, unlike traditional methods, does not rely on optimizing individual components such as promoters and transcription factors. Instead, it uses biomolecular circuits to enhance the biosensors performance despite inherent component flaws. We experimentally implemented eight different biosensors by employing CRISPR-Cas systems, then quantitatively compared their performance and identified one configuration, which we named CASense, that overcomes the limitations of current biosensors. Our approach is generalisable and can be adapted to sense any analyte of interest for which there is an analyte-sensitive promoter, making it a versatile tool for multiple applications. As a proof of concept, we engineered a high-performance biosensor of intracellular copper due to the critical role that copper plays in human health and disease, and to the lack of techniques able to measure intracellular copper levels in living cells. The significance of our work lies in its potential to impact the monitoring of bioactive molecules and chemicals both in vitro and in vivo, which is crucial in areas such as toxicology, drug discovery, disease diagnosis and therapy.

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bioRxiv2025

Toehold-VISTA: A machine learning approach to decipher programmable RNA sensor-target interactions

RNA-based biosensors have emerged as essential tools in synthetic biology and diagnostics, enabling precise and programmable responses to diverse RNA inputs. However, the time to design, produce, and screen high-performance RNA sensors remains a critical challenge. The fundamental rules governing RNA-RNA interactions--specifically the structure-function relationships that determine sensor performance--remain poorly understood. Here, we present a method enabling versatile in-silico RNA-targeting analysis (VISTA), a machine learning-guided framework for the rapid design of RNA sensors. VISTA integrates biophysical modeling of both sensor and target RNAs with a partial least squares discriminant analysis (PLS-DA) machine learning framework. Using high-throughput experimental measurements with sequence-structure feature extraction to train predictive models, we capture the key determinants of RNA sensor performance. We find that by using toehold switches as a model RNA sensor, Toehold-VISTA successfully designs RNA sensors with improved function against SARS-CoV-2 RNA. These findings establish a broadly applicable, target-aware design strategy for accelerating RNA sensor engineering across biotechnology and diagnostic applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/669990v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1f33be7org.highwire.dtl.DTLVardef@1e1f523org.highwire.dtl.DTLVardef@1a4d914org.highwire.dtl.DTLVardef@1a7eb5c_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

Semi-rational Engineering of Terminal Deoxynucleotidyl Transferase for High-efficiency Enzymatic de Novo DNA Synthesis

The 21st century has witnessed rapid advancements in synthetic biology, with DNA synthesis emerging as a foundational technology. Conventional phosphoramidite-based methods face significant limitations, including short DNA elongation lengths (<300 nt), hazardous chemical waste, and low stepwise incorporation efficiency. Enzymatic DNA synthesis using terminal deoxynucleotidyl transferase (TdT) offers a promising alternative, enabling kilobase-scale assembly with greater efficiency and minimal environmental impact. Here, we identified Bos taurus TdT (BtTdT) through UniProt database mining as a catalytically active scaffold for natural and 3'-modified dNTPs. Comprehensive characterization of BtTdTs enzymatic properties--including pH, temperature, metal ion dependence, and substrate specificity--revealed its optimal conditions. Truncation of the BRCT domain generated variants with enhanced activity compared to wild-type BtTdT. Guided by AlphaFold3-predicted structural models, we engineered a quintuple mutant (M5: Bt15AAR336L/K338G/L397M/E456S/D395G) optimized for 3'-ONH2-dNTP incorporation. M5 exhibited 30-fold activity enhancement relative to the triple mutant M3 (Bt15AAR336L/K338G/L397M) and achieved stepwise incorporation efficiency exceeding 98% in de novo synthesis of 10-nt ssDNA, demonstrating its potential for scalable enzymatic DNA synthesis. This work establishes a rational framework for TdT engineering through rational domain truncation and computational design, showing potential toward industrial-scale enzymatic DNA manufacturing.

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bioRxiv2017

Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis

Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature, thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have training data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of protein length and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multi-protein synthetic circuits, RNA circuits or the cell free production of small molecule products.

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bioRxiv2025

DNA framework array enables ultra-high throughput DNA synthesis

High-throughput DNA synthesis has revolutionized synthetic biology, molecular diagnostics, genome engineering, and DNA data storage by enabling the scalable and precise construction of nucleotide-based information systems. However, existing high-throughput synthesis technologies rely on top-down fabrication strategies to maximize throughput, yet they are inherently constrained by physical limitations. These constraints make it exceedingly difficult to achieve single-molecule level synthesis control that is essential for advancing next-generation DNA synthesis. Here, we present a DNA framework-based bottom-up enzymatic synthesis strategy that enables single-molecule level control of DNA synthesis with high-throughput. Leveraging the nanoscale addressability of DNA frameworks, we achieved a synthesis site pitch of 10.9 nm--183-fold smaller than that of previously reported electrode arrays. Theoretical projections suggest that this approach could scale synthesis throughput to 5.3 x 1011 sequences per square centimeter, representing a four-order-of-magnitude improvement over existing synthesis arrays. This approach lays the foundation for gigabyte-scale DNA writing and offers a highly accessible pathway toward large-scale DNA synthesis for emerging applications such as molecular data storage.

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bioRxiv2018

Characterizing Genetic Circuit Components in E. coli towards a Campylobacter jejuni Biosensor.

Campylobacter jejuni is responsible for most cases of bacterial gastroenteritis (food poisoning) in the United Kingdom. The most common routes of transmission are by contact with raw poultry. Current detection systems for the pathogen are time-consuming, expensive or inaccessible for everyday users. In this article we propose a cheaper and faster system for detection of C. jejuni using a synthetic biology approach. We aimed to detect C. jejuni by the presence of xylulose, an uncommon bacterial capsular saccharide. We characterized two sugar-based regulatory systems that displayed potential to act as tools for detection of xylulose. Using a two-plasmid reporter system in Escherichia coli, we investigated the regulatory protein component (MtlR) of the mannitol operon from Pseudomonas fluorescens. Our findings suggest that the promoter of mtlE is activated by MtlR in the presence of a variety of sugar inducer molecules, and may exhibit cross-activity with a native regulator of E. coli. Additionally, we engineered the L-arabinose transcriptional activator (AraC) of E. coli for altered ligand specificity. We performed site-specific saturation mutagenesis to generate AraC variants with altered effector specificity, with an aim to generate a mutant activated by xylulose. We characterized several mutant AraC variants which have lost the ability to respond specifically to the native L-arabinose effector. We promote this technique as a powerful tool for future iGEM teams to create regulatory circuits activated by novel small molecule ligands.

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