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18 projects in result set.
Statistical analysis of conformational ensembles
- This project provides computational tools and methods to analyze conformational ensembles of biomolecules, as well as their assemblies, such as those obtained from molecular simulations.
(A) PROTEINS: The molecular understanding of the functional regulation of proteins requires assessment of various states, including active and inactive states, as well as their interdependencies. For several proteins, their various states can be distinguished from each other on the basis of their minimum energy 3D structures. For many other proteins, like GPCRs, PDZ domains, nuclear transcription factors, heat shock proteins, T-cell receptors and viral attachment proteins, their states can be distinguished categorically from each other only when their finite-temperature conformational ensembles are considered alongside their minimum-energy structures. We are developing tools/methods for:
(A1) Direct comparison of conformational ensembles - The traditional approach to compare two or more conformational ensembles is to compare their respective summary statistics. This approach is, however, prone to artifactual bias, as data is compared after dimensionality reduction. The proper way to compare ensembles is to compare them directly with each other and prior to any dimensionality reduction. g_ensemble_comp is a tool we have developed that does just that and reports the difference between ensembles in terms of a true metric defined by the zeroth law of thermodynamics.
(A2) Prediction of allosteric signaling networks - method under development.
(B) LIPID MEMBRANES: The surface area of a lipid bilayer is related fundamentally to many other observables, such as thermal phase transitions and domain formation in mixed lipid bilayers. We have developed g_tessellate_area to compute the 3D surface area of a bilayer using Delunay tessellation. | |
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Activity Percentile: 94.66 Registered: 2015-09-15 17:52 |
Are subject-specific musculoskeletal models robust to parameter identification?
- This study analyzed the sensitivity of the predictions of an MRI-based musculoskeletal model (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the unavoidable uncertainties in parameter identification, i.e., body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. | |
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Activity Percentile: 92.75 Registered: 2014-11-10 15:19 |
A three-dimensional musculoskeletal model of the dog
- The domestic dog is interesting to investigate because of their wide range of body size, body mass, and physique. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. The use of detailed musculoskeletal models may help fill the knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence neuronal control and joint loading in dog locomotion. | |
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Registered: 2020-11-30 08:11 |
Neuromusculoskeletal Modeling (NMSM) Pipeline
- <div style="display:inline-block"><a href="https://nmsm.rice.edu"><img src="https://nmsm.rice.edu/img/nmsm-pipeline-social-card.jpg" style="float:left;max-width:calc(100% - 40px);"></a></div>
Full project information is available at: https://nmsm.rice.edu. Please direct any inquiries about the NMSM Pipeline to us by posting your questions on this SimTK project forum or emailing nmsm@rice.edu.
Neuromusculoskeletal Modeling (NMSM) Pipeline is a set of tools for personalizing models and designing treatments for movement impairments and other pathologies.
The NMSM Pipeline consists of two toolsets:
Model Personalization - Personalize joint, muscle-tendon, neural control, and ground contact model properties.
Treatment Optimization - Design treatments using personalized models and an optimal control methodology.
At this time, Treatment Optimization requires the use of <a href="https://www.gpops2.com/">GPOPS-II optimal control solver</a>.
The NMSM Pipeline is written in MATLAB to lower the barrier for entry and to facilitate accessibility to the core codebase. We encourage users to modify the code to meet their needs.
The core codebase and examples are available to download for use in research. At this time, we ask that you wait to publish any work that uses the NMSM Pipeline until the journal article reference for the software is available. Please get in touch with us if you have any questions.
If you need help or want to start a discussion, please use the SimTK forum for this project.
Note: This project is a living entity. Updates will be made available as the Pipeline, examples, and tutorials are developed further and improved. | |
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Registered: 2022-07-07 14:55 |
Multicore parallel computing with OpenSim Moco
- In this project, we investigated the computational speed‐up obtained via multicore parallel computing relative to solving problems serially (i.e., using a single core) in optimal control simulations of human movement in OpenSim Moco. Simulations were solved using up to 18 cores with a variety of temporal mesh interval densities and using two different initial guess strategies. Considerable speed‐up can be achieved for some optimal control simulation problems in OpenSim Moco by leveraging the multicore processors often available in modern computers.
This work is described in the paper "Computational performance of musculoskeletal simulation in OpenSim Moco using parallel computing" which is available on the Publications page. Models and complete working examples are provided on the Downloads page. This project was supported by a Rackham Graduate Student Research Grant. | |
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Registered: 2023-08-21 23:33 |
ForceBalance : Systematic Force Field Optimization
- ForceBalance is free software for force field optimization.
It facilitates the development of more accurate force fields using a systematic and reproducible procedure.
ForceBalance is highly versatile and can optimize nearly any set of parameters using experimental measurements and/or ab initio calculations as reference data.
<b>SOURCE CODE:</b> For the newest features, visit the GitHub source code repository at https://github.com/leeping/forcebalance.
The SVN repository on the left frame is an outdated archive.
<b>RELEASES:</b> Stable versions of the code updated once every few months. Click "Releases" on the left frame for the most recent release and notes.
<b>CONTACT:</b> Please contact me (Lee-Ping, right frame) if you have questions or comments! | |
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Registered: 2011-12-20 17:04 |
Neuromuscular Models for the Predictive Treatment of Parkinson's Disease
- NoTremor aims to provide patient specific computational models of the coupled brain and neuromuscular systems that will be subsequently used to improve the quality of analysis, prediction and progression of Parkinson’s disease. In particular, it aspires to establish the neglected link between brain modelling and neuromuscular systems that will result in a holistic representation of the physiology for PD patients. | |
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Activity Percentile: 6.87 Registered: 2014-06-18 13:56 |
KGS: Sampling and Characterizing Protein and RNA conformational landscapes
- The Kino-Geometric Sampling (KGS) software suite uses advanced, robotics-inspired algorithms to rapidly explore the conformational landscape of folded proteins, RNA, and their complexes. Combined with powerful statistical techniques, it structurally characterizes collective motions and excited substates from sparse, spatiotemporally averaged data. | |
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Activity Percentile: 0.00 Registered: 2015-12-11 21:41 |
Prediction of trunk muscle size and position
- This project provides programs for predicting trunk muscle size and position values given sex, age, height, and weight. The predictions apply regressions developed based on CT measurements in a multi-ethnic sample of the Framingham Heart Study. This implements the regressions in Python code, specifically enabling users to run online via Google Colab notebooks. This may be of interest for researchers creating musculoskeletal models or other studies needing estimates of muscle morphometry.
The code (available in downloads) provides estimations of trunk muscle cross-sectional areas and positions at the vertebral levels between T4 and L4. Copy the Google Colab notebook to your Google Colab account to run. https://colab.research.google.com/
The form requires submission of a subject's sex, weight ( kg / lb ), height ( cm / in ), Age and ID (optional). After clicking the play button on the form an excel workbook will be downloaded. It contains four sheets: Cross-Sectional Area in mm^2 , Distance in mm^2 , Angle in degrees and an additional sheet describing your subject's inputs. Note that this calculator was developed with a dataset containing healthy adults between ages 40 and 90. Application outside this range may not be accurate, and this should not be used for children and adolescents.
FAQ.
1: What sample pool was used to generate the regression used in this calculator?
Table 1: Mean (SD) [Range] characteristics of participants included in the sample.
<table border="1"> <tr> <th style="background-color: gray"> </th> <th style="background-color: gray"> Men (N=247) </th> <th style="background-color: gray"> Women (N=260 ) </th> </tr> <tr> <th style="background-color: gray">Age (years)</th> <td>60.8 (14.1) [40-88]</td> <td>61.8 (12.6) [40-90]</td> </tr> <tr> <th style="background-color: gray">Height (cm)</th> <td>173.8 (7.2) [155.5-193.7] </td> <td>159.9 (6.6) [139.7-175.9] </td> </tr> <tr> <th style="background-color: gray"> Weight (kg)</th> <td>86.0 (14.4) [47.2-122.9]</td> <td>70.7 (15.3) [40.4-127.0]</td> </tr>
</table>
2: Can this calculator be used for anyone?
The calculator can be used for anyone who falls within the data ranges noted above (i.e age 40 – 90, 140cm-195cm (4ft-6.4ft) and 70kg -130kg (154lbs-286lbs). Outside these ranges, the calculator may still be used, but will generate a warning that predictions are being extrapolated. Prediction intervals will also increase as inputs move outside the range of the sample. If an age < 40 is entered, the calculations will be performed for age = 40, as aging-related effects are likely not found in the same way for adults under 40.
3: How were the muscle distance and angle measurements calculated?
Measurements were performed in transverse plane CT scans at the mid-level of the vertebral body. After segmenting a muscle, the CSA is defined as its area in this plane. The distance and angle refer to the transverse plane polar coordinates representing position of a muscle’s centroid in relation to the centroid of the vertebral body, where the posterior direction is 0°.
4. What are the prediction intervals?
The prediction intervals are calculated at each vertebral level along with the predicted value. The prediction intervals for an outcome (lower 95% and upper 95%) provide a likely range of values for an individual with the given input sex, age, height and weight. A hard lower limit of 0 is applied for CSA predictions and distance predictions, and lower and upper limits of 0° and 180°, respectively, for angle predictions.
5. How do I run multiple individuals at once?
Use the MuscleCalculator_ForBulkUse. pynb code and fill in the arrays with your individuals' information, using commas for delineation and quotation marks for Sex, WeightUnits, HeightUnits and Names. Click run and your files will download. Your browser may ask you to allow multiple files to be downloaded. If you do not see a notification for the files being downloaded, check your google drive folder as some browsers may automatically send it there.
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Registered: 2022-12-09 18:54 |
Investigating the effects of pelvic floor muscles during pregnancy
- Developing a risk predictive Model about how the pelvic floor muscles change during pregnancy and how they stretch during the delivery in order to identify and discover knowledge about these muscles to avoid damage during delivery. Which damage increases the risk of urinary incontinence or pelvic organ prolapse later in life. | |
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Registered: 2016-11-22 20:54 |
Tibial forces in independently ambulatory children with spina bifida
- Experimental motion capture and bone strength data and simulation results from 16 independently ambulatory children with spina bifida and 16 age- and sex-matched children with typical development. Additional motion capture and EMG data and simulation results for 6 independently ambulatory children with spina bifida and 1 child with typical development. Custom scripts were used to calculate joint kinematics, moments, and forces. Post-simulation analyses were conducted to compare these waveforms between the group with spina bifida and the group with typical development.
The manuscript using these data and simulations can be found here:
Lee MR, Hicks JL, Wren TAL, and Delp SL (2022). Independently ambulatory children with spina bifida experience near-typical knee and ankle joint moments and forces during walking. Gait and Posture, 99:1-8. https://doi.org/10.1016/j.gaitpost.2022.10.010 | |
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Registered: 2022-06-01 20:00 |
3D Numerical Investigation of Endothelial Shear Stress in Arteries
- 3D numerical investigation of endothelial shear stress in coronary arteries. | |
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Activity Percentile: 0.00 Registered: 2015-11-30 13:34 |
Finite Element Mesh Overclosure Reduction and Slicing (FEMORS)
- The code was developed with the project to make freely available 3D geometries of the lower limbs of the Visible Human Female and Visible Human Male. The FEMORS code was used to remove all overclosures between adjacent geometries. The VH 3D geometries are available at: https://simtk.org/projects/3d-vh-geometry
The code was implemented in MATLAB utilizing the Machine Learning Toolbox and is available free and open-source, but we ask that you cite the following two works:
Andreassen, T. E., Hume, D. R., Hamilton, L. D., Higinbotham, S. E. & Shelburne, K. B. "An Automated Process for 2D and 3D Finite Element Overclosure and Gap Adjustment using Radial Basis Function Networks". 1–13 (2022) https://doi.org/10.48550/arXiv.2209.06948
TE Andreassen, DR Hume, LD Hamilton, K Walker, SE Higinbotham, KB Shelburne, "Three-dimensional lower extremity musculoskeletal geometry of the Visible Human Female and Male,” Sci Data 10, 34 (2023). https://doi.org/10.1038/s41597-022-01905-2.
Adding changes to the code is encouraged and can be added to the repository by contacting the author. The author will check new or revised content for accuracy and completeness and add it to the repository.
Future/ongoing work aims to recreate the code using code that does not need the Machine Learning Toolbox, as well as implementing the code into a Python Toolbox for widespread use. | |
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Registered: 2023-03-27 19:58 |
ACL Reconstruction Decision Support Through Personalized Simulation of the Lachm
- The objective of the proposed approach is to develop a clinical decision support system (DSS) that will help clinicians optimally plan the ACL reconstruction procedure in a patient specific manner.
Methods: A full body model is developed in this study with 23 degrees of freedom and 93 muscles. The knee ligaments are modeled as non-linear spring-damper systems and a tibiofemoral contact model was utilized. The parameters of the ligaments were calibrated based on an optimization criterion. Forward dynamics were utilized during simulation for predicting the model’s response to a given set of external forces, posture configuration and physiological parameters.
Results: The proposed model is quantified using MRI scans and measurements of the well-known Lachman test, on several patients with a torn ACL. The clinical potential of the proposed framework is demonstrated in the context of flexion-extension, gait and jump actions. The clinician is able to modify and fine tune several parameters such as number of bundles, insertion position on the tibia or femur and the resting length that correspond to the choices of the surgical procedure and study their effect on the biomechanical behavior of the knee.
Conclusion: Computational knee models can be used to predict the effect of surgical decisions and to give insight on how different parameters can affect the stability of the knee. Special focus has to be given in proper calibration and experimental validation.
<iframe width="560" height="315" src="https://www.youtube.com/embed/zgcq0c5_w3c" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> | |
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Activity Percentile: 0.00 Registered: 2015-08-31 08:55 |
Proteolytic and non-proteolytic regulation of collective cell invasion
- Cancer cells manoeuvre through extracellular matrices (ECMs) using different invasion modes, including single cell and collective cell invasion. These modes rely on MMP-driven ECM proteolysis to make space for cells to move. How cancer-associated alterations in ECM influence the mode of invasion remains unclear. Further, the sensitivity of the two invasion modes to MMP dynamics remains unexplored. In this paper, we address these open questions using a multiscale hybrid computational model combining ECM density-dependent MMP secretion, MMP diffusion, ECM degradation by MMP and active cell motility. Our results demonstrate that in randomly aligned matrices, collective cell invasion is more efficient than single cell invasion. Although increase in MMP secretion rate enhances invasiveness independent of cell–cell adhesion, sustenance of collective invasion in dense matrices requires high MMP secretion rates. However, matrix alignment can sustain both single cell and collective cell invasion even without ECM proteolysis. Similar to our in-silico observations, increase in ECM density and MMP inhibition reduced migration of MCF-7 cells embedded in sandwich gels. Together, our results indicate that apart from cell intrinsic factors (i.e., high cell–cell adhesion and MMP secretion rates), ECM density and organization represent two important extrinsic parameters that govern collective cell invasion and invasion plasticity. | |
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Activity Percentile: 0.00 Registered: 2016-03-07 06:05 |
Application for the simulation of the prosthetic gait
- This application has a dataset belonging to macha prosthetic patterns , in which the angle of the socket and prosthetic foot is changed.
It focuses on patients with transtibial amputation and uses opensim in MATLAB libraries to link and generate a model for opensim , based on data captured from a measuring TECHNAID brand. | |
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Registered: 2016-08-24 14:21 |
HTMD - High Throughput Molecular Dynamics
- In a single script, it is possible to plan an entire computational experiment, from manipulating PDBs, building, executing and analyzing simulations, computing Markov state models, kinetic rates, affinities and pathways.
See more information on <a href="https://www.htmd.org/">https://www.htmd.org</a>.
HTMD Forum: <a href="https://forum.htmd.org/">https://forum.htmd.org</a>
We are also on Github: <a href="https://github.com/Acellera/htmd">https://github.com/Acellera/htmd</a>
Report issues on: <a href="https://github.com/Acellera/htmd/issues">https://github.com/Acellera/htmd/issues</a> | |
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Registered: 2016-05-13 07:45 |
Mobilize Center Planning
- Enables coordination, collaboration, and planning for the Mobilize Center | |
Registered: 2014-09-02 17:48 |