Search NASA⌕ Search

SEARCH · Search NASA

Results for “in silico”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

In situ tumor model for longitudinal in silico imaging trials

Abstract Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast’s soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50μm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion’s local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Engineering↗
DOE OSTI2020

Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches

We describe the use of in silico approaches to improve the process of molecular assay development and reduce time and cost by utilizing available databases of whole genome pathogen sequences combined with modern bioinformatics and physical modeling tools. Well-characterized assays are needed for accurately detecting pathogens in environmental and patient samples and also for evaluation of the efficacy of a medical countermeasure that may be administered to patients. The polymerase chain reaction (PCR) remains the gold standard for pathogen detection due to the simplicity of its instrumentation, low cost of reagents, and outstanding limit of detection (LOD), sensitivity, and specificity. However, creation of such PCR assays often involves iterations of design, preliminary testing, and thorough validation with clinical isolates and testing in relevant matrices, which can be time consuming, costly, and result in suboptimal assays. Since formal validation (e.g., for Emergency Use Authorization [EUA] or Food and Drug Administration [FDA] licensure) of an infectious disease assay can be very expensive and can require extensive time of development, having a well-designed assay up front is a critical first step. Yet, many assays described in the literature utilized limited design capabilities and many initially promising assays fail the validation process, resulting in increased costs and timelines for successful product development. While the computational approaches outlined in this document by no means obviate the need for wet lab testing, they can reduce the amount of effort wasted on empirical optimization and iterative redesigns and also guide validation studies. The proposed computational approaches also result in higher performing assays with better sensitivity, specificity, and lower LOD and reduce the possibility of assay failure due to signature erosion. To provide clarity, an extensive glossary of defined terms is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity

Background Immunotherapies, driven by immune-mediated antitumorigenicity, offer the potential for significant improvements to the treatment of multiple cancer types. Identifying therapeutic strategies that bolster antitumor immunity while limiting immune suppression is critical to selecting treatment combinations and schedules that offer durable therapeutic benefits. Combination oncolytic virus (OV) therapy, wherein complementary OVs are administered in succession, offer such promise, yet their translation from preclinical studies to clinical implementation is a major challenge. Overcoming this obstacle requires answering fundamental questions about how to effectively design and tailor schedules to provide the most benefit to patients. Methods We developed a computational biology model of combined oncolytic vaccinia (an enhancer virus) and vesicular stomatitis virus (VSV) calibrated to and validated against multiple data sources. We then optimized protocols in a cohort of heterogeneous virtual individuals by leveraging this model and our previously established in silico clinical trial platform. Results Enhancer multiplicity was shown to have little to no impact on the average response to therapy. However, the duration of the VSV injection lag was found to be determinant for survival outcomes. Importantly, through treatment individualization, we found that optimal combination schedules are closely linked to tumor aggressivity. We predicted that patients with aggressively growing tumors required a single enhancer followed by a VSV injection 1 day later, whereas a small subset of patients with the slowest growing tumors needed multiple enhancers followed by a longer VSV delay of 15 days, suggesting that intrinsic tumor growth rates could inform the segregation of patients into clinical trials and ultimately determine patient survival. These results were validated in entirely new cohorts of virtual individuals with aggressive or non-aggressive subtypes. Conclusions Based on our results, improved therapeutic schedules for combinations with enhancer OVs can be studied and implemented. Our results further underline the impact of interdisciplinary approaches to preclinical planning and the importance of computational approaches to drug discovery and development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

In silico evolution of oscillatory gene dynamics formerly Code sharing for a manuscript to be submitted to the journal iScience

The disclosure is about the numerical simulations and visualization implemented in general-purpose language Python and C++. The simulations were in-silico evolution of hypothetical gene circuits that potentially admit oscillatory dynamics. The aim of the study, which was invited to be submitted to a special issue "Timekeeping, noise and development across kingdoms" in the journal iScience, is to demonstrate that in contrast to common belief, noise in gene expression dynamics could significantly facilitate the evolutionary process for searching oscillatory dynamics, which are essential for developing circadian rhythm in living organisms.

Lin, Yen Ting↗
DOE OSTI2022

Sweep-tracing algorithm: in silico slip crystallography and tension-compression asymmetry in BCC metals

Abstract Direct Molecular Dynamics (MD) simulations are being increasingly employed to model dislocation-mediated crystal plasticity with atomic resolution. Thanks to the dislocation extraction algorithm (DXA), dislocation lines can be now accurately detected and positioned in space and their Burgers vector unambiguously identified in silico, while the simulation is being performed. However, DXA extracts static snapshots of dislocation configurations that by themselves present no information on dislocation motion. Referred to as a sweep-tracing algorithm (STA), here we introduce a practical computational method to observe dislocation motion and to accurately quantify its important characteristics such as preferential slip planes (slip crystallography). STA reconnects pairs of successive snapshots extracted by DXA and computes elementary slip facets thus precisely tracing the motion of dislocation segments from one snapshot to the next. As a testbed for our new method, we apply STA to the analysis of dislocation motion in large-scale MD simulations of single crystal plasticity in BCC metals. We observe that, when the crystal is subjected to uniaxial deformation along its [001] axis, dislocation slip predominantly occurs on the {112} maximum resolved shear stress plane under tension, while in compression slip is non-crystallographic (pencil) resulting in asymmetric mechanical response. The marked contrast in the observed slip crystallography is attributed to the twinning/anti-twinning asymmetry of shears in the {112} planes relatively favoring dislocation motion in the twinning sense while hindering dislocations from moving in the anti-twinning directions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. Further, by combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In Silico Screening of CO 2 –Dipeptide Interactions for Bioinspired Carbon Capture

Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO 2 concentrations. On an industrial scale, amine-based solvents are extensively employed for CO 2 capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by-products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO 2 . Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Furthermore, those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO 2 affinity. Density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT) computations were performed for the calculation of CO 2 interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO 2 binding, as well as design principles that can further enhance CO 2 capture through cooperative binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

In silico evaluation of plant nitrification suppression effects on agroecosystem nitrogen loss

Abstract Nitrification regulates the potential for nitrogen (N) loss from ecosystems because it converts ammonium to nitrate, which is susceptible to leaching and gaseous emissions. Crops can suppress the microbes that perform nitrification by exuding nitrification‐inhibiting compounds from their roots and taking up available ammonium. However, the effect of nitrification suppression on agroecosystem N losses remains poorly characterized, and a lack of temporal synchrony between nitrification, N losses, and nitrification suppression by plants could limit the effect of nitrification suppression. We used the DayCent‐CABBI model to evaluate the effectiveness of the suppression of nitrification by sorghum to reduce N 2 O emissions and nitrate leaching in an energy sorghum/soybean rotation at the Energy Farm in Urbana‐Champaign, IL. We simulated nitrification suppression at the measured levels (MeasNS) and at the maximum measured level applied to the entire growing season (MaxNS), and we also explored ways to better utilize nitrification suppression by altering the timing of urea–ammonium–nitrate fertilizer applications. Model experiments showed that most nitrification occurred immediately after fertilizer was applied, whereas nitrification suppression began to ramp up more than a month after planting. On an annual basis, MeasNS experiments showed a 1%–2% reduction in annual N 2 O emissions relative to no nitrification suppression (NoNS), and MaxNS experiments showed a 4%–9% reduction in annual N 2 O emissions relative to NoNS. Both nitrification suppression levels showed <1% reduction in nitrate leaching. Altering the timing of fertilizer applications to better synchronize nitrification suppression with high soil ammonium levels had mixed effects on annual N 2 O emissions and nitrate leaching and sometimes resulted in increased N losses. The timing of simulated N 2 O emissions shifted with the timing of fertilization, and N 2 O emissions from denitrification increased when N 2 O emissions from nitrification decreased. Increasing N retention during the non‐growing season may be more effective than growing‐season nitrification suppression for reducing annual N losses in the rainfed Midwest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Synthesis and in silico and in vitro evaluation of trimethoxy-benzamides designed as anti-prion derivatives

Transmissible spongiform encephalopathies (TSEs), also known as prion diseases, are neurodegenerative disorders which affect mammals, including the human species, and arise after the conversion of the monomeric cellular prion protein (PrP{sup C}) into the aggregated scrapie form (PrP{sup Sc}). There is no therapy to treat TSEs and the identification of compounds that bind PrP{sup C}, preventing its conversion into PrP{sup Sc}, is a viable therapeutic strategy. We designed and synthesized six novel trimethoxy-benzamide compounds as anti-prion drug candidates. Molecular docking analyses predicted that all the derivatives bind to a hotspot region located in the PrP globular domain with very similar spatial orientation and interaction mode. Although none of the analogs inhibited in vitro-aggregation of recombinant PrP (rPrP) in a cell-free conversion assay, the RT-QuIC, compound 8a accelerated rPrP conversion into PrP{sup Sc}-like species. STD-NMR and ITC analyses indicated that both 8a and 8b bind to rPrP{sup 90–231}. These analogs were toxic to PrP{sup Sc}-infected cell lines, hence we could not assess their anti-prion activity by using this cellular approach, although this toxicity was cell line-dependent. These results point out that the 4-amino-quinoline trimethoxy-benzamide scaffold described herein represents a novel chemical pattern useful as a starting point for future structural optimization in the design of PrP ligands with improved affinity and safety profiles.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Understanding interactions between biomolecules and two-dimensional nanomaterials using in silico microscopes

We report two-dimensional (2D) nanomaterials such as graphene are increasingly used in research and industry for various biomedical applications. Extensive experimental and theoretical studies have revealed that 2D nanomaterials are promising drug delivery vehicles, yet certain materials exhibit toxicity under biological conditions. So far, it is known that 2D nanomaterials possess strong adsorption propensities for biomolecules. To mitigate potential toxicity and retain favorable physical and chemical properties of 2D nanomaterials, it is necessary to explore the underlying mechanisms of interactions between biomolecules and nanomaterials for the subsequent design of biocompatible 2D nanomaterials for nanomedicine. The purpose of this review is to integrate experimental findings with theoretical observations and facilitate the study of 2D nanomaterial interaction with biomolecules at the molecular level. We discuss the current understanding and progress of 2D nanomaterial interaction with proteins, lipid membranes, and DNA based on molecular dynamics (MD) simulation. In this review, we focus on the 2D graphene nanosheet and briefly discuss other 2D nanomaterials. With the ever-growing computing power, we can image nanoscale processes using MD simulation that are otherwise not observable in experiment. We expect that molecular characterization of the complex behavior between 2D nanomaterials and biomolecules will help fulfill the goal of designing effective 2D nanomaterials as drug delivery platforms.

2D nanomaterials↗
DOE OSTI2024

Generating synthetic signaling networks for in silico modeling studies

Predictive models of signaling pathways have proven to be difficult to develop. Reasons include the uncertainty in the number of species, the complexity in species’ interactions, and the sparseness and uncertainty in experimental data. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. For example, uncertainty and sparseness of the data often result in overfitted models that have little predictive value beyond recapitulating the experimental data itself. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. However, to determine the effectiveness of any new algorithm, a baseline model is needed to test its performance. To meet this need, we developed a method for generating artificial synthetic networks that are reasonably realistic and thus can be treated as ground truth models. These synthetic models can then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. Here, we describe a simple approach for generating synthetic signaling networks that can be used for this purpose.

42 ENGINEERING↗
DOE OSTI2023

Engineering the fracture resistance of 2H-transition metal dichalcogenides using vacancies: An in-silico investigation based on HRTEM images

Vacancy engineering of 2H-transition metal dichalcogenides (2H-TMDs) has recently attracted great attention due to its potential to fine-tune the phonon and opto-electric properties of these materials. From a mechanical perspective, this symmetry-breaking process typically reduces the overall crack resistance of the material and adversely affects its reliability. However, vacancies can trigger the formation of heterogeneous phases that synergistically improve fracture properties. In this study, using MoSe2 as an example, we characterize the types and density of vacancies that can emerge under electron irradiation and quantify their effect on fracture. Molecular dynamic (MD) simulations, employing a re-parameterized Tersoff potential capable of accurately capturing bond dissociation and structural phase changes, reveal that isolated transition metal monovacancies or chalcogenide divacancies tend to arrest the crack tip and hence enhance the monolayer toughness. In contrast, isolated chalcogenide monovacancies do not significantly affect toughness. The investigation further reveals that selenium vacancy lines, formed by high electron dose rates, alter the crack propagating direction and lead to multiple crack kinking. Using atomic displacements and virial stresses together with a continuum mapping, displacement, strain, and stress fields are computed to extract mechanistic information, e.g., conditions for crack kinking and size effects in fracture events. The study also reveals the potential of specific defect patterns, “vacancy engineering,” to improve the toughness of 2H-TMDs materials.

36 MATERIALS SCIENCE↗