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14 results for “computer modelling and simulation”
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow
<p>L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008).</p> <p>The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard.</p> <p>In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008).</p> <p>Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning.</p> <p>The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock.</p> <p>What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references).</p> <p><em><strong>Description of files</strong></em></p> <p>Simulation 1: L-PEACH-d over three years of growth.</p> <p>Simulation 2: Detailed growth of leaves and fruit using L-PEACH.</p> <p>Simulation 3: Pruning L-PEACH-d to a v-system.</p> <p>Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h.</p> <p>Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h.</p> <p>Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.</p>
Simulations from "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors"
<h1>IL6R/IL8R Antibody Binding Model Code</h1> <p>Christina M.P. Ray, Huilin Yang, Jamie B. Spangler, Feilim Mac Gabhann</p> <p>This dataset contains all simulation output files generated for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". The model is comprised of a coupled set of ordinary differential equations (ODEs) where each individual ODE describes one molecule (antibody or receptor) or molecular complex (antibody + receptor). The terms in the ODEs represent each binding interaction (binding and unbinding processes) in the system.</p> <p>The code for the binding model and for the analysis and visualization results is available on GitHub at <a href="https://github.com/christyray/bispecific-binding-model">christyray/bispecific-binding-model</a>.</p> <h2>Specific Simulations</h2> <p>The <code>.csv</code> and <code>.rds</code> files in the correspond to the results from the simulations performed for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". These files can be read into R using the <code>import_data()</code> function included in the <a href="https://github.com/christyray/bispecific-binding-model">GitHub repository</a>.</p> <p>The <code>id</code> files contain simulation IDs to link the molecule concentrations (<code>yin</code>) and parameter values (<code>params</code>) with the simulation results (<code>out</code>). When applicable, the <code>norm</code> files contain normalized simulation output, and the <code>occupied</code> files contain receptor fractional occupancy values calculated from the simulation output.</p> <ul> <li><code>optimization</code>: Optimization of binding rate constants (association and dissociation) to experimental <em>in vitro</em> flow cytometry data; results displayed in Figure 2</li> <li><code>binding-curve</code>: Model simulations using the best-fit parameter set for comparison to the experimental data used to fit the model parameters; results displayed in Figure 3</li> <li><code>compare-opt</code>: Model simulations using each of the optimized parameter sets; results displayed in the Supplemental Information</li> <li><code>time</code>: Simulations of antibody binding dynamics over time; results displayed in Figure 4</li> <li><code>concentration</code>: Simulations with varying antibody concentrations and receptor expression levels; results displayed in Figure 5</li> <li><code>monovalent</code>: Simulations restricted to monovalent antibody binding only; results displayed in Figure 6</li> <li><code>compare-ab</code> and <code>compare-recep</code>: Simulations of both the bispecific antibody BS1 and the combination of monospecific antibodies tocilizumab and 10H2 for comparsion; results displayed in Figure 7</li> <li><code>local</code> and <code>global</code>: Local and global univariate sensitivity analyses; results displayed in Figure 8</li> </ul> <h2>References</h2> <blockquote> <p>H. Yang, M. N. Karl, W. Wang, B. Starich, H. Tan, A. Kiemen, A. B. Pucsek, Y.-H. Kuo, G. C. Russo, T. Pan, E. M. Jaffee, E. J. Fertig, D. Wirtz, and J. B. Spangler. Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis. Molecular Therapy, 30(11):3430–3449, Nov. 2022. doi:<a href="https://doi.org/10.1016/j.ymthe.2022.07.008">10.1016/j.ymthe.2022.07.008</a></p> </blockquote>
Data for: Caspase-Based Fusion Protein Technology: Substrate Cleavability Described by Computational Modeling and Simulation
<p>This dataset contains all files necessary to set up the simulations conducted in this work. It further contains the scripts that were used to do the stitching and combining of the CASPON-tag and the N-termini of the POIs. The manuscript was just submitted and accepted: <a href="https://doi.org/10.1021/acs.jcim.4c00316">10.1021/acs.jcim.4c00316</a></p>
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Simulation data for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly"
<p>This is the original simulation data sets for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly".</p>
PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models
Open the record for dataset details and reuse information.
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
Open the record for dataset details and reuse information.
Test Model for GTT Computational Fluid Dynamics Simulation by TransAT
<p>This archive contains the model and associated code need to run the GTT benchmark test for TransAT.</p>
Inferring macroevolutionary parameters from on a model of adaptive radiation by Approximate Bayesian Computation - ABC Simulations
<p>Recent advances in DNA sequencing are providing increasingly accurate phylogenetic trees to study, but understanding the evolutionary forces at play in different contexts remains a huge challenge. To tackle this issue, we applied an Bayesian approach to an existing model of phenotypic and species diversification [1] in order to retrieve 11 underlying parameters (such as basal speciation and extinction rates, but also competition strength) from phylogenetic trees with known traits values at the tips.</p> <p>This dataset corresponds to the Approximate Bayesian Computation simulations realized for the inference.</p>
Data from: Drug delivery in a tumour cord model: a computational simulation
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Computational Modelling and Experimental Characterisation of the Fabrication of Tuneable Photonic Crystal pH Sensors: Supplementary Simulation Videos
<p>Simulation output videos to accompany the paper 'Computational Modelling and Experimental Characterisation of the Fabrication of Tuneable Photonic Crystal pH Sensors'.</p> <ul> <li>Film writing, with exported film image</li> <li>Film reading, at various expansions and pulse wavelengths (selected as the diffraction peak wavelengths identified in the paper)</li> </ul>
Uncertainty Analysis of Computational Model to Simulate Neurostimulation Caused by Gradient Fields in MRI
ClinicalTrials.gov study NCT04674787. IPD Sharing: YES. Countries: 1. Publications: 0.
Process Mapping and Data Collection to Inform a Computer Simulation Model of Hospitalised Patients With Bloodstream Infection, Sepsis and Systemic Infection
ClinicalTrials.gov study NCT06271031. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.