Competition and evolutionary selection among core regulatory motifs in gene expression control.

Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory schemes that carry their own bioenergetic cost. By ensuring that regulators are only expressed when needed, we demonstrate that autoregulation generally offers an advantage in an environment combining mutation and time-varying selection. Whether positive or negative feedback emerges as dominant depends primarily on the demand for the target gene product, typically to ensure that the detrimental impact of inevitable mutations is minimized. While self-repression of the regulator curbs the spread of these loss-of-function mutations, self-activation instead facilitates their propagation. By analyzing the transcription network of multiple model organisms, we reveal that reduced bioenergetic cost may contribute to the preferential selection of autoregulation among transcription factors. Our results not only uncover how seemingly equivalent regulatory motifs have fundamentally different impact on population structure, growth dynamics, and evolutionary outcomes, but they can also be leveraged to promote the design of evolutionarily robust synthetic gene circuits.

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use in school work. We find that ChatGPT's performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks.

A blueprint for a synthetic genetic feedback optimizer.

Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.

Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains.

The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters.

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources.

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.

A Practical Step-by-Step Guide for Quantifying Retroactivity in Gene Networks.

One of the fundamental properties of engineered large-scale complex systems is modularity. In synthetic biology, genetic parts exhibit context-dependent behavior. Here, we describe and quantify a major source of such behavior: retroactivity. In particular, we provide a step-by-step guide for characterizing retroactivity to restore the modular description of genetic modules. Additionally, we also discuss how retroactivity can be leveraged to quantify and maximize robustness to perturbations due to interconnection of genetic modules.

Pattern Formation over Multigraphs.

Two of the most common pattern formation mechanisms are Turing-patterning in reaction-diffusion systems and lateral inhibition of neighboring cells. In this paper, we introduce a broad dynamical model of interconnected modules to study the emergence of patterns, with the above mentioned two mechanisms as special cases. Our results do not restrict the number of modules or their complexity, allow multiple layers of communication channels with possibly different interconnection structure, and do not assume symmetric connections between two connected modules. Leveraging only the static input/output properties of the subsystems and the spectral properties of the interconnection matrices, we characterize the stability of the homogeneous fixed points as well as sufficient conditions for the emergence of spatially non-homogeneous patterns. To obtain these results, we rely on properties of the graphs together with tools from monotone systems theory. As application examples, we consider patterning in neural networks, in reaction-diffusion systems, and contagion processes over random graphs.

Creating Single-Copy Genetic Circuits.

Synthetic biology is increasingly used to develop sophisticated living devices for basic and applied research. Many of these genetic devices are engineered using multi-copy plasmids, but as the field progresses from proof-of-principle demonstrations to practical applications, it is important to develop single-copy synthetic modules that minimize consumption of cellular resources and can be stably maintained as genomic integrants. Here we use empirical design, mathematical modeling, and iterative construction and testing to build single-copy, bistable toggle switches with improved performance and reduced metabolic load that can be stably integrated into the host genome. Deterministic and stochastic models led us to focus on basal transcription to optimize circuit performance and helped to explain the resulting circuit robustness across a large range of component expression levels. The design parameters developed here provide important guidance for future efforts to convert functional multi-copy gene circuits into optimized single-copy circuits for practical, real-world use.

Isocost Lines Describe the Cellular Economy of Genetic Circuits.

Genetic circuits in living cells share transcriptional and translational resources that are available in limited amounts. This leads to unexpected couplings among seemingly unconnected modules, which result in poorly predictable circuit behavior. In this study, we determine these interdependencies between products of different genes by characterizing the economy of how transcriptional and translational resources are allocated to the production of proteins in genetic circuits. We discover that, when expressed from the same plasmid, the combinations of attainable protein concentrations are constrained by a linear relationship, which can be interpreted as an isocost line, a concept used in microeconomics. We created a library of circuits with two reporter genes, one constitutive and the other inducible in the same plasmid, without a regulatory path between them. In agreement with the model predictions, experiments reveal that the isocost line rotates when changing the ribosome binding site strength of the inducible gene and shifts when modifying the plasmid copy number. These results demonstrate that isocost lines can be employed to predict how genetic circuits become coupled when sharing resources and provide design guidelines for minimizing the effects of such couplings.

Modular composition of gene transcription networks.

Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.

Ethylene oxide in blood of ethylene-exposed B6C3F1 mice, Fischer 344 rats, and humans.

The gaseous olefin ethylene (ET) is metabolized in mammals to the carcinogenic epoxide ethylene oxide (EO). Although ET is the largest volume organic chemical worldwide, the EO burden in ET-exposed humans is still uncertain, and only limited data are available on the EO burden in ET-exposed rodents. Therefore, EO was quantified in blood of mice, rats, or 4 volunteers that were exposed once to constant atmospheric ET concentrations of between 1 and 10 000 ppm (rodents) or 5 and 50 ppm (humans). Both the compounds were determined by gas chromatography. At ET concentrations of between 1 and 10 000 ppm, areas under the concentration-time curves of EO in blood (µmol × h/l) ranged from 0.039 to 3.62 in mice and from 0.086 to 11.6 in rats. At ET concentrations ≤ 30 ppm, EO concentrations in blood were 8.7-fold higher in rats and 3.9-fold higher in mice than that in the volunteer with the highest EO burdens. Based on measured EO concentrations, levels of EO adducts to hemoglobin and lymphocyte DNA were calculated for diverse ET concentrations and compared with published adduct levels. For given ET exposure concentrations, there were good agreements between calculated and measured levels of adducts to hemoglobin in rats and humans and to DNA in rats and mice. Reported hemoglobin adduct levels in mice were higher than calculated ones. Furthermore, information is given on species-specific background adduct levels. In summary, the study provides most relevant data for an improved assessment of the human health risk from exposure to ET.

1,2:3,4-Diepoxybutane in blood of male B6C3F1 mice and male Sprague-Dawley rats exposed to 1,3-butadiene.

The important industrial chemical 1,3-butadiene (BD; CAS Registry Number: 106-99-0) is a potent carcinogen in B6C3F1 mice and a weak one in Sprague-Dawley rats. This difference is mainly attributed to the species-specific burden by the metabolically formed 1,2:3,4-diepoxybutane (DEB). However, only limited data exist on the DEB blood burden of rodents at BD concentrations below 100 ppm. Considering this, DEB concentrations were determined in the blood of mice and rats immediately after 6h exposures to various constant concentrations of BD of between about 1 and 1200 ppm. Immediately after its collection, blood was injected into a vial that contained perdeuterated DEB (DEB-D(6)) as internal standard. Plasma samples were prepared and treated with sodium diethyldithiocarbamate that derivatized metabolically produced DEB and DEB-D(6) to their bis(dithiocarbamoyl) esters, which were then analyzed by high performance liquid chromatography coupled with an electrospray ionization tandem mass spectrometer. DEB concentrations in blood versus BD exposure concentrations in air could be described by one-phase exponential association functions. Herewith calculated (±)-DEB concentrations in blood increased in mice from 5.4 nmol/l at 1 ppm BD to 1860 nmol/l at 1250 ppm BD and in rats from 1.2 nmol/l at 1 ppm BD to 92 nmol/l at 200 ppm BD, at which exposure concentration 91% of the calculated DEB plateau concentration in rat blood was reached. This information on the species-specific blood burden by the highly mutagenic DEB helps to explain why the carcinogenic potency of BD in rats is low compared to that in mice.

Kinetics of ethylene and ethylene oxide in subcellular fractions of lungs and livers of male B6C3F1 mice and male fischer 344 rats and of human livers.

Ethylene (ET) is metabolized in mammals to the carcinogenic ethylene oxide (EO). Although both gases are of high industrial relevance, only limited data exist on the toxicokinetics of ET in mice and of EO in humans. Metabolism of ET is related to cytochrome P450-dependent mono-oxygenase (CYP) and of EO to epoxide hydrolase (EH) and glutathione S-transferase (GST). Kinetics of ET metabolism to EO and of elimination of EO were investigated in headspace vessels containing incubations of subcellular fractions of mouse, rat, or human liver or of mouse or rat lung. CYP-associated metabolism of ET and GST-related metabolism of EO were found in microsomes and cytosol, respectively, of each species. EH-related metabolism of EO was not detectable in hepatic microsomes of rats and mice but obeyed saturation kinetics in hepatic microsomes of humans. In ET-exposed liver microsomes, metabolism of ET to EO followed Michaelis-Menten-like kinetics. Mean values of V(max) [nmol/(min·mg protein)] and of the apparent Michaelis constant (K(m) [mmol/l ET in microsomal suspension]) were 0.567 and 0.0093 (mouse), 0.401 and 0.031 (rat), and 0.219 and 0.013 (human). In lung microsomes, V(max) values were 0.073 (mouse) and 0.055 (rat). During ET exposure, the rate of EO production decreased rapidly. By modeling a suicide inhibition mechanism, rate constants for CYP-mediated catalysis and CYP inactivation were estimated. In liver cytosol, mean GST activities to EO expressed as V(max)/K(m) [µl/(min·mg protein)] were 27.90 (mouse), 5.30 (rat), and 1.14 (human). The parameters are most relevant for reducing uncertainties in the risk assessment of ET and EO.

Metabolism of 1,3-butadiene to toxicologically relevant metabolites in single-exposed mice and rats.

1,3-Butadiene (BD) was carcinogenic in rodents. This effect is related to reactive metabolites such as 1,2-epoxy-3-butene (EB) and especially 1,2:3,4-diepoxybutane (DEB). A third mutagenic epoxide, 3,4-epoxy-1,2-butanediol (EBD), can be formed from DEB and from 3-butene-1,2-diol (B-diol), the hydrolysis product of EB. In BD exposed rodents, only blood concentrations of EB and DEB have been published. Direct determinations of EBD and B-diol in blood are missing. In order to investigate the BD-dependent blood burden by all of these metabolites, we exposed male B6C3F1 mice and male Sprague-Dawley rats in closed chambers over 6-8h to constant atmospheric BD concentrations. BD and exhaled EB were measured in chamber atmospheres during the BD exposures. EB blood concentrations were obtained as the product of the atmospheric EB concentration at steady state with the EB blood-to-air partition coefficient. B-diol, EBD, and DEB were determined in blood collected immediately at the end of BD exposures up to 1200 ppm (B-diol, EBD) and 1280 ppm (DEB). Analysis of BD was done by GC/FID, of EB, DEB, and B-diol by GC/MS, and of EBD by LC/MS/MS. EB blood concentrations increased with BD concentrations amounting to 2.6 micromol/l (rat) and 23.5 micromol/l (mouse) at 2000 ppm BD and to 4.6 micromol/l in rats exposed to 10000 ppm BD. DEB (detection limit 0.01 micromol/l) was found only in blood of mice rising to 3.2 micromol/l at 1280 ppm BD. B-diol and EBD were quantitatively predominant in both species. B-diol increased in both species with the BD exposure concentration reaching 60 micromol/l at 1200 ppm BD. EBD reached maximum concentrations of 9.5 micromol/l at 150 ppm BD (rat) and of 42 micromol/l at 300 ppm BD (mouse). At higher BD concentrations EBD blood concentrations decreased again. This picture probably results from a competitive inhibition of the EBD producing CYP450 by BD, which occurs in both species.