Input Signal for Synthetic Inertia: Estimated ROCOF Versus Remote Machine Acceleration.
Abstract not provided.
Wilches-Bernal, Felipe↗
SEARCH · Engineering Papers
Results for “Synthetic acceleration”
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Interpolation of computed gamma-ray detector response functions
Gamma-ray spectra measured by traditional detectors contain features that result from a combination of the effects of detector materials/geometry, the incident gamma-ray energy, and the angle of entry. The features, such as the full-energy photopeak, Compton continuum, annihilation peak, and escape peaks, are governed by simple relationships depending on incident energy and have been known for a long time. Monte Carlo computer simulations of gamma rays interacting with a detector will show these features, and with a resolution function applied, the results should look similar to real measurements. The traditional approach to creating a detector response function requires many separate simulations of monoenergetic gamma rays striking the detector. This paper presents a new approach to developing computed detector response functions. The new approach involves a much smaller number of monoenergetic gamma-ray simulations and uses interpolation to quickly generate the responses of gamma rays that were not simulated. During the interpolation process, the underlying physics equations are used to accurately compute the response of a given energy gamma ray from the small set of simulations. Such work enables accelerated generation of synthetic radiation detector data.
Detector response↗
Development, optimization, and application of an episomal plasmid system for Rhodotorula toruloides
Rhodotorula toruloides is an emerging oleaginous yeast with strong potential as a microbial cell factory for the production of acetyl-CoA-derived bioproducts. However, engineering of this organism has been limited by the absence of a functional episomal plasmid system, a foundational genetic tool for rapid gene expression, pathway testing, and CRISPR-based genome engineering. Here, we report the first episomal plasmid system for R. toruloides . Through systematic screening of candidate autonomously replicating sequences (ARSs) from diverse sources, we identified multiple functional ARS elements and selected C63F4, a fragment derived from Contig 63 of R. toruloides CBS14, because of its stable performance. The resulting pC63F4 plasmid was maintained episomally, supported GFP reporter expression, exhibited a copy number of 2.39 ± 0.13, and showed good stability during long term cultivation. To overcome poor transformation efficiency, we developed a Cre- loxP -mediated in vivo re-circularization strategy that enabled reliable delivery of the episomal plasmid. Using this improved system, we demonstrated functional episomal expression of metabolic engineering genes and multi-gene pathways for the production of triacetic acid lactone, fatty alcohols, and limonene. Finally, we leveraged this platform to establish a redesigned CRISPR system that enables seamless genome editing in R. toruloides for the first time, while also simplifying marker recycling. Together, this work establishes a long-needed episomal plasmid platform and associated CRISPR toolkit that will accelerate metabolic engineering, synthetic biology, and fundamental studies in R. toruloides .
CRISPR-Cas9↗
Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications
Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.
Kim, Haseong↗
Machine learning predicts new anti-CRISPR proteins
The increasing use of CRISPR–Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR–Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR–Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.
59 BASIC BIOLOGICAL SCIENCES↗
Data for Development, Optimization, and Application of an Episomal Plasmid System for Rhodotorula toruloides
Rhodotorula toruloides is an emerging oleaginous yeast with strong potential as a microbial cell factory for the production of acetyl-CoA-derived bioproducts. However, engineering of this organism has been limited by the absence of a functional episomal plasmid system, a foundational genetic tool for rapid gene expression, pathway testing, and CRISPR-based genome engineering. Here, we report the first episomal plasmid system for R. toruloides . Through systematic screening of candidate autonomously replicating sequences (ARSs) from diverse sources, we identified multiple functional ARS elements and selected C63F4, a fragment derived from Contig 63 of R. toruloides CBS14, because of its stable performance. The resulting pC63F4 plasmid was maintained episomally, supported GFP reporter expression, exhibited a copy number of 2.39 ± 0.13, and showed good stability during long term cultivation. To overcome poor transformation efficiency, we developed a Cre-loxP-mediated in vivo re-circularization strategy that enabled reliable delivery of the episomal plasmid. Using this improved system, we demonstrated functional episomal expression of metabolic engineering genes and multi-gene pathways for the production of triacetic acid lactone, fatty alcohols, and limonene. Finally, we leveraged this platform to establish a redesigned CRISPR system that enables seamless genome editing in R. toruloides for the first time, while also simplifying marker recycling. Together, this work establishes a long-needed episomal plasmid platform and associated CRISPR toolkit that will accelerate metabolic engineering, synthetic biology, and fundamental studies in R. toruloides .
Gene Editing↗
Accelerating full-waveform inversion using source stacking: synthetic experiments at the global scale in a realistic 3-D earth model
SUMMARY The spectral element method is currently the method of choice for computing accurate synthetic seismic wavefields in realistic 3-D earth models at the global scale. However, it requires significantly more computational time, compared to normal mode-based approximate methods. Source stacking, whereby multiple earthquake sources are aligned on their origin time and simultaneously triggered, can reduce the computational costs by several orders of magnitude. We present the results of synthetic tests performed on a realistic radially anisotropic 3-D model, slightly modified from model SEMUCB-WM1 with three component synthetic waveform ‘data’ for a duration of 10 000 s, and filtered at periods longer than 60 s, for a set of 273 events and 515 stations. We consider two definitions of the misfit function, one based on the stacked records at individual stations and another based on station-pair cross-correlations of the stacked records. The inverse step is performed using a Gauss–Newton approach where the gradient and Hessian are computed using normal mode perturbation theory. We investigate the retrieval of radially anisotropic long wavelength structure in the upper mantle in the depth range 100–800 km, after fixing the crust and uppermost mantle structure constrained by fundamental mode Love and Rayleigh wave dispersion data. The results show good performance using both definitions of the misfit function, even in the presence of realistic noise, with degraded amplitudes of lateral variations in the anisotropic parameter ξ. Interestingly, we show that we can retrieve the long wavelength structure in the upper mantle, when considering one or the other of three portions of the cross-correlation time series, corresponding to where we expect the energy from surface wave overtone, fundamental mode or a mixture of the two to be dominant, respectively. We also considered the issue of missing data, by randomly removing a successively larger proportion of the available synthetic data. We replace the missing data by synthetics computed in the current 3-D model using normal mode perturbation theory. The inversion results degrade with the proportion of missing data, especially for ξ, and we find that a data availability of 45 per cent or more leads to acceptable results. We also present a strategy for grouping events and stations to minimize the number of missing data in each group. This leads to an increased number of computations but can be significantly more efficient than conventional single-event-at-a-time inversion. We apply the grouping strategy to a real picking scenario, and show promising resolution capability despite the use of fewer waveforms and uneven ray path distribution. Source stacking approach can be used to rapidly obtain a starting 3-D model for more conventional full-waveform inversion at higher resolution, and to investigate assumptions made in the inversion, such as trade-offs between isotropic, anisotropic or anelastic structure, different model parametrizations or how crustal structure is accounted for.
Geochemistry & Geophysics↗
Mechanically Accelerated Depolymerization of Entangled Linear Polymer Melts
Mechanical forces can enhance the chemical depolymerization of synthetic polymers when shear flow accelerates chain scission. To quantify the extent of mechanically-accelerated scission, the effect of simple shear flow (duration and strength) with low Weissenberg and Deborah numbers was investigated by considering the impact of applied work in both simple shear and shear dominated mixed flows. Hydrogenated polyisoprene was chosen as a model linear, entangled system. The conditions (strain amplitude, frequency, and shearing time) necessary to increase chain scission were assessed in the rubbery melt. Shear flow accelerated chain scission at higher temperatures, suggesting an activated process. Isothermal scission versus work curves were superposed by applying shift factors a T,S , whose Arrhenius-like temperature dependence gave an apparent activation energy for chain scission of ~ 110 kJ/mol, which is likely a combination of the activation energy of viscosity and bond energy. This work provides a base for quantifying the impact of shear on depolymerization of polymer melts and highlight the connection between viscous dissipation and scission chemistry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Utilization of machine learning to accelerate colloidal synthesis and discovery
Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Genomics-enabled analysis of specialized metabolism in bioenergy crops: Current progress and challenges
Plants produce a staggering diversity of specialized small molecule metabolites that play vital roles in mediating environmental interactions and stress adaptation. This chemical diversity derives from dynamic biosynthetic pathway networks that are often species-specific and operate under tight spatiotemporal and environmental control. A growing divide between demand and environmental challenges in food and bioenergy crop production have intensified research on these complex metabolite networks and their contribution to crop fitness. High-throughput omics technologies provide access to ever-increasing data resources for investigating plant metabolism. However, the efficiency of using such system-wide data to decode the gene and enzyme functions controlling specialized metabolism has remained limited; due largely to the recalcitrance of many plants to genetic approaches and the lack of ‘user-friendly’ biochemical tools for studying the diverse enzyme classes involved in specialized metabolism. With emphasis on terpenoid metabolism in the bioenergy crop switchgrass as an example, this review aims to illustrate current advances and challenges in the application of DNA synthesis and synthetic biology tools for accelerating the functional discovery of genes, enzymes and pathways in plant specialized metabolism. These technologies have accelerated knowledge development on the biosynthesis and physiological roles of diverse metabolite networks across many ecologically and economically important plant species and can provide resources for application to precision breeding and natural product metabolic engineering.
59 BASIC BIOLOGICAL SCIENCES↗
Precision engineering of biological function with large-scale measurements and machine learning
As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.
59 BASIC BIOLOGICAL SCIENCES↗
Position Papers for the 2024 ASCR Workshop on Analog Computing for Science
Analog computing capabilities have existed since the dawn of science, but modern techniques potentially allow for the construction, verification, and characterization of complicated analog computing systems in a wide variety of contexts, from high-performance computational accelerators to nanorobotics and synthetic biology. In short, advances in analog computing can enable the creation of physical systems with complex behaviors that meet sophisticated requirements. Meeting our nation's needs, from needs in computing and modeling, to needs for advanced materials and energy technologies, continues to motivate pursuing novel kinds of complex systems, and thus analog computing techniques, in all of these spaces.
97 MATHEMATICS AND COMPUTING↗
26th International Symposium on Plant Lipids
The 2024 International Symposium on Plant Lipids (ISPL) successfully advanced scientific knowledge in plant lipid biology by presenting new discoveries in lipid metabolism, membrane structure and function, lipid signaling, and biotechnology. The symposium fostered professional development for early-career scientists through oral and poster presentation opportunities, lightning talks, and networking events. It promoted the exchange of new technologies, including advances in mass spectrometry, metabolic modeling, and synthetic biology, that will accelerate research across plant biology and related fields. ISPL also strengthened international collaborations, drawing 220 participants from 15 countries across four continents, and established a platform for ongoing scientific exchange and community-building within the global plant lipid research community.
59 BASIC BIOLOGICAL SCIENCES↗
Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering
Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.
Chen, Junyu [Univ. of Illinois at Urbana-Champaign↗
Narrowing the Phase Distribution of Quasi-2D Perovskites for Stable Deep-Blue Electroluminescence
Solution-processed quasi-2D perovskites contain multiple quantum wells with a broad width distribution. Inhomogeneity results in the charge funneling into the smallest bandgap components, which hinders deep-blue emission and accelerates Auger recombination. Here, a synthetic strategy applied to a range of quasi-2D perovskite systems is reported, that significantly narrows the quantum well dispersity. It is shown that the phase distribution in the perovskite film is significantly narrowed with controlled, simultaneous evaporation of solvent and antisolvent. Modulation of film formation kinetics of quasi-2D perovskite enables stable deep-blue electroluminescence with a peak emission wavelength of 466 nm and a narrow linewidth of 14 nm. Light emitting diodes using the perovskite film show a maximum luminance of 280 cd m –2 at an external quantum efficiency of 0.1%. This synthetic approach will serve in producing new materials widening the color gamut of next-generation displays.
36 MATERIALS SCIENCE↗
Polymer Hole Transport Material Functional Group Tuning for Improved Perovskite Solar Cell Performance
As lead halide perovskites (LHPs) continue to achieve success as a light-harvesting material in perovskite solar cells (PSCs), exploring and understanding other materials in the device stack become increasingly important. Particularly, selection of suitable hole transport materials (HTMs) that demonstrate high performance and stability is imperative in the design of P-I-N PSCs. Presented here are a family of 12 structurally related polymers based on either fluorene or carbazole main chains with select aromatic side groups that introduce tunable properties for use in PSCs. How properties such as the highest occupied molecular orbital energy level, conductivity, glass-transition temperature, and wettability of the HTM affect the PSC performance is explored. Devices that incorporate the polymer HTMs perform well relative to PTAA in benchmark P-I-N PSC architectures while exhibiting similar or superior stability under accelerated aging studies. The relative synthetic simplicity and resultant performance of the HTMs in PSCs coupled with the ability to customize properties with different functional groups demonstrates the potential of this family of HTMs for a variety of LHP materials.
14 SOLAR ENERGY↗
Generation and evaluation of synthetic patient data
Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.
59 BASIC BIOLOGICAL SCIENCES↗
Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework
Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.
LOSAC↗