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Results for “Synthetic Biology”

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bioRxiv2017

An automated model test system for systematic development and improvement of gene expression models

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

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bioRxiv2021

A burden-free gene overexpression system

Heterologous gene activation causes non-physiological burden on cellular resources that cells are unable to adjust to. Here, we introduce a feedforward controller that increases ribosome level upon activation of a gene of interest (GOI) to compensate for such a burden. The controller achieves this by activating a modified SpoT enzyme with sole hydrolysis activity, which lowers ppGpp level and thus de-represses ribosomes. Without the controller, activation of the GOI decreased growth rate by more than 50%. With the controller, we could activate the GOI to the same level without a growth rate decrease. A cell strain armed with the controller in co-culture enabled persistent population-level activation of a GOI, which could not be achieved by a strain devoid of the controller. The feedforward controller is a tunable, modular, and portable tool that for the first time allows dynamic gene activation without growth rate defects for bacterial synthetic biology applications.

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bioRxiv2016

Analytic framework for stochastic binary biological switches

We propose an analytic solution for the stochastic dynamics of a binary biological switch, defined as a DNA unit with two mutually exclusive configurations, each one triggering the expression of a different gene. Such a device could be used as a memory unit for biological computing systems designed to operate in noisy environments. We discuss a recent implementation of an exclusive switch in living cells, the recombinase addressable data (RAD) module. In order to understand the behavior of a RAD module we compute the exact time dependent distributions of the two expressed genes starting in one state and evolving to another asymptotic state. We consider two operating regimes of the RAD module: fast and slow stochastic switching. The fast regime is \"aggregative\" and produces unimodal distributions, whereas the slow regime is \"separative\" and produces bimodal distributions. Both regimes can serve to prepare pure memory states when all cells are expressing the same gene. The slow regime can also separate mixed states by producing two sub-populations each one expressing a different gene. Our model provides a simplified, general phenomenological framework for studying biological memory devices and our analytic solution can be further used to clarify theoretical concepts in bio-computation and for optimal design in synthetic biology.

Synthetic Biology↗

bioRxiv2023

Designing a protein with emergent function by combined in silico, in vitro and in vivo screening

Recently, utilization of machine learning (ML) based methods has led to astonishing progress in protein design and, thus, the design of new biological functionality. However, emergent functions that require higher-order molecular interactions, such as the ability to self-organize, are still extremely challenging to implement. Here, we describe a comprehensive in silico, in vitro, and in vitro screening pipeline (i3-screening) to develop and validate ML-designed artificial homologs of a bacterial protein that confers its role in cell division through the emergent function of spatiotemporal pattern formation. Moreover, we present complete substitution of a wildtype gene by an ML-designed artificial homolog in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.

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bioRxiv2025

Stimuli-driven cyclical content exchange in a composite synthetic cell

Engineering sophisticated behaviours in synthetic cells lacking complex biomolecular machinery remains a central challenge in synthetic biology. Here, we introduce a protein-free approach for dynamic content modulation in liposome-based synthetic cells using an internal gelation strategy. By crosslinking a polymer hydrogel within the lumen of giant vesicles and tethering it to the inner membrane leaflet, we create a composite architecture that enables controlled and reversible membrane permeabilisation via osmotic swelling and shrinking, facilitating externally gated material exchange without reconstituted protein pores or electroporation. Simultaneously, the hydrogel matrix affords control over membrane fluidity and the diffusion of cytoplasmic clients. We deploy the transport-regulation platform to construct a synthetic-cell bioreactor whereby reversible membrane permeabilisation enables content supplementation and fuels a biocatalytic reaction. The composite gel-GUV chassis provides an adaptive, robust and expandable solution for engineering increasingly modular and functional synthetic cellular systems. These findings may echo how primordial cells harnessed environmental fluctuations for content exchange through chemically distinct pathways.

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bioRxiv2026

Noise analysis of derivative-action biomolecular topologies

Temporal gradient sensing is a fundamental capability observed across diverse natural biological systems, contributing to the coordination of their functions. Harnessing this ability is also of significant interest in synthetic biology, particularly for sensing and control applications. In this work, we focus on a biomolecular topology that exemplifies a broader class of signal-differentiating architectures, while introducing a structural variant of it. We examine their behavior under both nominal and non-ideal conditions, accounting for stochastic noise arising from different sources. Our investigation includes scenarios where these topologies operate independently, as well as when embedded within minimal regulatory architectures based on negative as well as positive feedback. We analyze the stability of the resulting macroscopic dynamics--a prerequisite for practical deployment--and quantify stochastic fluctuations in system output, providing comparisons with the corresponding input/unregulated process. Importantly, our results demonstrate that signal differentiation can be effectively implemented in a biomolecular setting without incurring deleterious noise amplification--a major concern in the utilization of derivative action across disciplines.

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bioRxiv2014

Engineering a circular riboregulator in Escherichia coli

Circular RNAs have recently been shown to be important gene expression regulators in mammalian cells. However, their role in prokaryotes remains elusive. Here, we engineered a synthetic riboregulator that self-splice to produce a circular molecule, exploiting group I permuted intron-exon (PIE) sequences. We demonstrated that the resulting circular riboregulator can activate gene expression, showing increased dynamic range compared to the linear form. We characterized the system with a fluorescent reporter and with an antibiotic resistance marker. Thanks to the increased regulatory activity by higher stability, isolation due to self-splicing, and modularity of PIE, we envisage engineered circular riboregulators in further synthetic biology applications.

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bioRxiv2015

A statistical approach reveals designs for the most robust stochastic gene oscillators

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative structural robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of auto-regulation, and degradation of mRNA and protein affects the frequency, amplitude and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modelling approaches to systems and synthetic biology.

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bioRxiv2022

Tunable dynamics in a multi-straintranscriptional pulse generator

A major challenge in synthetic biology is the manipulation of engineered gene circuits toward a specified behavior. This challenge becomes more difficult as synthetic systems become more complex by incorporating additional genes or strains. Here we demonstrate that circuit dynamics can be tuned in synthetic consortia through the manipulation of strain fractions within the community. To do this, we constructed a microbial consortium comprised of three strains of engineered Escherichia coli that, when co-cultured, use homoserine lactone (HSL) mediated intercellular signaling to create a multi-strain incoherent type-1 feedforward loop (I1-FFL). Like naturally occurring I1-FFL motifs in gene networks, this engineered microbial consortium acts as a pulse generator of gene expression. We demonstrated that the amplitude of the pulse can be easily tuned by adjusting the relative population fractions of the strains. We created a mathematical model for the temporal dynamics of the microbial consortium and, using this model, identified population fractions that produced desired pulse characteristics. Our work demonstrates that intercellular gene circuits can be effectively tuned simply by adjusting the starting fractions of each strain type.

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bioRxiv2024

Red light responsive Cre recombinase for bacterial optogenetics

Optogenetic tools have been used in a wide range of microbial engineering applications that benefit from the tunable, spatiotemporal control that light affords. However, the majority of current optogenetic constructs for bacteria respond to blue light, limiting the potential for multichromatic control. In addition, other wavelengths offer potential benefits over blue light, including improved penetration of dense cultures and reduced potential for toxicity. In this study, we introduce OptoCre-REDMAP, a red light inducible Cre recombinase system in Escherichia coli. This system harnesses the plant photoreceptors PhyA and FHY1 and a split version of Cre recombinase to achieve precise control over gene expression and DNA excision. We optimized the design by modifying the start codon of Cre and characterized the impact of different levels of induction to find conditions that produced minimal basal expression in the dark and induced full activation within four hours of red light exposure. We characterized the systems sensitivity to ambient light, red light intensity, and exposure time, finding OptoCre-REDMAP to be reliable and flexible across a range of conditions. In co-culture experiments with OptoCre-REDMAP and the blue light responsive OptoCre-VVD, we found that the systems responded orthogonally to red and blue light inputs. Direct comparisons between red and blue light induction with OptoCre-REDMAP and OptoCre-VVD demonstrated the superior penetration properties of red light. OptoCre-REDMAPs robust and selective response to red light makes it suitable for advanced synthetic biology applications, particularly those requiring precise multichromatic control.

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bioRxiv2024

PEG Adjustment Enables Genome Transplantation Using Mycoplasma Mycoides Recipient

Pioneering advances in synthetic biology, initiated at the J. Craig Venter Institute, have enabled the creation of the first cell driven by synthetic genome and opened a new era of creating designer microbes. This technology offers far greater potential for genetic modification than traditional genome editing techniques and holds promise for the routine creation of synthetic cells as DNA synthesis costs decrease and genetic knowledge expands. Two essential technologies underpin this achievement: the ability to clone entire genomes in host organisms, such as Saccharomyces cerevisiae, and the successful transplant of these genomes into recipient cells to generate living organisms. In all previous work the recipient cell in genome transplantation experiments has been Mycoplasma capricolum. In this study, we explored the potential of using Mycoplasma mycoides strains as recipient cells for genome transplantation. By increasing polyethylene glycol (PEG) concentration from 5% to 10%, we successfully transplanted various M. mycoides strains using several M. mycoides recipient cells. Additionally, we demonstrated the ability to transplant M. capricolum genomes into M. mycoides recipient cells; although with lower efficiency compared to M. mycoides strains. These findings provide a modified transplantation protocol and likely get us closer to expanding genome transplantation to other bacterial species.

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bioRxiv2018

Investigating the unknown functions in the minimal bacterial genome reveals many transporter proteins

The recent identification of the minimal bacterial genome revealed that nearly one third (149) of the 473 encoded genes were of unknown function, demonstrating our limited understanding of the essential functions of life. Application of state of the art in silico methods for functional annotation demonstrated that these proteins of unknown function lack orthologs, known protein domains, and templates to model their structure. Combination of the results from different complementary approaches enabled functions to be assigned to 94 of the 149 proteins, although often with general terms such as transporter or DNA binding. 22 likely transporter proteins were identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell, where many metabolic enzymes have been removed. These results advance our understanding of the minimal bacterial genome and therefore aid synthetic biology and its application to biotechnology.

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bioRxiv2025

Repurposing the diatom periplastidial compartment for heterologous terpenoid production

Diatoms are promising microorganisms to provide sustainable routes for photosynthetic terpenoid production from CO2, yet their potential for compartmentalized engineering remains largely unexplored. Here, we systematically profiled the biosynthetic capacity of Phaeodactylum tricornutum by targeting representative synthases for hemi, mono, sesqui, and tetraterpenoids to the cytosol, chloroplast, and periplastidial compartment (PPC). This comprehensive analysis revealed that all major prenyl phosphate precursors, DMAPP, GPP, FPP, and GGPP, are accessible in all compartments, including in the PPC and can sustain heterologous flux without major physiological penalties, although production efficiency varies across compartments and product classes. By determining precursor availability, we propose the diatom PPC as a minimal engineerable organelle directly interfaced with a eukaryotic chloroplast. Moreover, we highlight its utility as a unique interface to investigate metabolic exchange between the MVA and MEP pathways. These findings provide a systematic framework for compartment-specific terpenoid engineering in diatoms and open new opportunities for modular pathway assembly and synthetic biology in photosynthetic eukaryotes.

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bioRxiv2025

Rigorous Quantitative Analysis of Nonlinear Uncertain Biomolecular Systems using Validated Methods

The paper addresses the critical challenge of accurately characterising steady states in biomolecular systems, which are often complex, nonlinear, multistable and subject to significant uncertainties. Traditional numerical methods often fail to provide complete or guaranteed solutions under these conditions. To overcome these limitations, the research proposes and evaluates the application of interval analysis methodologies. We provided algorithms for interval Newton and interval Krawczyk methods for rigorously bounding all possible steady states (both stable and unstable) in multistable, multidimensional nonlinear systems. This study involves a comparative analysis of these two methods in conjunction with interval bisection and interval constraint propagation. We addressed numerical examples for an array of biologically plausible models, involving both feedback and feedforward gene networks. The work recommends the choice of the most suitable method for various types of biomolecular systems, ultimately offering a robust computational framework to understand cellular functions and design synthetic biological circuits.

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bioRxiv2022

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be non-functional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular AND gate logic system based on the B. subtilis natively split {sigma}-factor, SigO-RsoA, designed to maximise future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potential wider applicability of the approach to other systems and genetic backgrounds.

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bioRxiv2022

Building an RNA switch-based selection system for enzyme evolution in yeast

Recent advances in synthetic biology and metabolic engineering have enabled yeast as a favorable platform for synthesis of valuable natural and semi-synthetic compounds through expression of multiple heterologous enzymes sourced from plants, fungi and bacteria. However, these heterologous enzymes can suffer from low activity, specificity, stability and solubility in yeast, resulting in arduous iterations of design-build-test-learn cycles to optimize their production often performed on a single enzyme basis. Laboratory directed evolution has proven to be a powerful and high-throughput method for protein engineering, albeit its limited application for biosynthetic enzymes. Here, we harness small molecule-sensing, RNA-based switches to develop a generalizable selection system facilitating enzyme evolution. Our design utilizes an RNA-based switch for detection of intracellular compound production, which then regulates the expression of a selection gene. Our initial data shows that the auxotrophy selection gene SpHIS5 exhibits the highest selective capability in combination with a theophylline-responsive RNA-based switch. Using the theophylline-responsive RNA-based switch, we demonstrated the enrichment of a high-producing variant of caffeine demethylase, in a population size of 103. We target to demonstrate the use of this RNA-based selection system as a general approach for enzyme evolution.

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bioRxiv2024

Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity

An important and largely unsolved problem in synthetic biology is how to target gene expression to specific cell types. Here, we apply iterative deep learning to design synthetic enhancers with strong differential activity between two human cell lines. We initially train models on published datasets of enhancer activity and chromatin accessibility and use them to guide the design of synthetic enhancers that maximize predicted specificity. We experimentally validate these sequences, use the measurements to re-optimize the predictor, and design a second generation of enhancers with improved specificity. Our design methods embed relevant transcription factor binding site (TFBS) motifs with higher frequencies than comparable endogenous enhancers while using a more selective motif vocabulary, and we show that enhancer activity is correlated with transcription factor expression at the single cell level. Finally, we characterize causal features of top enhancers via perturbation experiments and show enhancers as short as 50bp can maintain specificity.

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bioRxiv2024

CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria

Foundational techniques in molecular biology--such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins--rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

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