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To find OpenSim models, you now have two options:\n\n1) Visit the summary table on the OpenSim documentation pages (http://simtk-confluence.stanford.edu:8080/display/OpenSim/Musculoskeletal+Models)\n\n2) Conduct a search on SimTK. Click here (https://simtk.org/search/search.php?srch=opensim&type_of_search=soft) and then narrow your search to \"models\" by checking the box on the left.\n\n------------------------------------------\nWelcome to the neuromuscular models library! The goal of this site is to provide a resource for students, researchers, and clinicians to access, use, test, and develop models. The majority of models in this library are for use with OpenSIM (which you can download free through simtk.org) and/or SIMM. Please take a look and enjoy.\n\n
Please respect your fellow modelers. \nIn using these models we ask that you respect the hard work of your fellow researchers by citing their work appropriately. When you go to the Download section you will be directed to individual project pages for each model which contain all of the files and documentation. Please carefully review the publications and cite the references in your future papers, presentations, grant applications, etc.\n\n
Have a model to contribute? \nDo you have a model which you would like to make available through this library? Providing others with access to your models can stimulate future studies, provide a foundation for young researchers, and maximize the impact of your model. It’s easy to set up a project page to post your model. This will allow you to track who is using your model and be in contact with them. Please consider contributing! The project administrators can help you post your model, so please contact us if you would like to get started.\n\n

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One may painstakingly produce a single movement simulation, but not the thousands of simulations that are required for predictive movement optimizations that are the state of the art in musculoskeletal dynamics. This has become a bottleneck for our own research, as well as for others. Our first aim, therefore, is to implement a generic, self-refining, surrogate modeling scheme, which aims to reproduce an underlying physics-based finite element model within a given error tolerance, but at a far lower computational cost. The self-refining feature is the key to reproduce the multi-dimensional input-output space of a typical finite element model of a joint or joint complex. 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Through specifying the physical properties of a system including material properties and geometrical variations, the resulting mechanical changes on the system from a stimulus can be computed. 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It is a collaborative effort that capitalizes on a diversity of expertise in areas such as clinical, experimental and computational biomechanics, nano-micro scale material modeling, finite element modeling, and neural networks. \n\nGrant Numer: 506297\nPrinciple Investigator: Trent Guess\nCo-Investigators: Ganesh Thiagarajan, Amil Misra, Reza Derakhshani (University of Missouri - Kanas City), Lorin Maletsky (University of Kansas), Terence McIff (University of Kansas Medical Center)\n\nAbstract from grant proposal\n\nDynamic loading of the knee is believed to play a significant role in the development and progression of tissue wear disease and injury. Macro level rigid body joint models provide insight into joint loading, motion, and motor control. The computational efficiency of these models facilitates dynamic simulation of neuromusculoskeletal systems, but a major limitation is their simplistic (or non-existent) representation of the non-linear, rate dependent behavior of soft tissue structures. This limitation prevents holistic computational approaches to investigating the complex interactions of knee structures and tissues, a limitation that hinders our understanding of the underlying mechanisms of knee injury and disease. \n\nThe objective of this project is to develop validated neural network models that reproduce the dynamic behavior of menisci-tibio-femoral articulations and to demonstrate the utility of these models in a musculoskeletal model of the leg. The specific aims of this study are:\n\nAim 1: Develop finite element (FE) models from micro-structure based constitutive methods that bridge the nano-micro scale behavior at the tissue level\n\nAim 2: Develop neural network (NN) based models that learn from FE simulation of dynamic behavior of menisci-tibio-femoral articulations \n\nAim 3: Validate the NN models within a rigid body dynamic model of a natural knee placed within a dynamic knee simulator\n\nAim 4: Demonstrate the utility of the NN models by placing them within a dynamic musculoskeletal model of the leg to study the interdependencies of the menisci and other knee tissues \n\nAim 5: Distribute the validated NN models of menisci-tibio-femoral dynamic response and contact pressure for use in any rigid body model of the knee or leg \n\nThe final product will be Neural Network (NN) models that conform to a modular application programming interface (API) that can be exported to any commercial integrated development environment (IDE) or in-house multi-body model. The NN models will be built upon a multi-scale approach and describe the non linear, rate dependent, non-homogenous dynamic response of menisci-tibio-femoral articulations in a computationally efficient modular package. The multi-scale modeling approach will be validated using a dynamic knee loading machine and the utility of the approach demonstrated by studying the interdependencies of menisci properties, tibio-femoral contact, and anterior cruciate ligament strain during a dual limb squat. A synergistic interdisciplinary team has been assembled to address the objective and aims of the proposed project comprising experts in rigid body dynamics and knee modeling, FE modeling, nano-micro scale material modeling, neural networks, and clinical and experimental biomechanics. \n\nThe proposed research will benefit society at large as the results of this work have potential applications to orthopedics, tissue engineering, and biomaterials. The work will also be a valuable asset to the musculoskeletal research community providing computational tools that may aid research in broad areas such as human movement, prosthetics, tissue engineering, sport injury, and disease.","has_downloads":false,"keywords":"knee,computational model,multiscale","ontologies":"Neuromuscular_Model,Computational_Model","projMembers":"mohammad kia,Lorin Maletsky,Katherine Bloemker,Trent Guess,Leo Olcott,Gavin Paiva,Reza Derakhshani,Ganesh Thiagarajan,Paul Wilson,Hongzeng Liu,Meenakshi Mishra","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"158","unix_group_name":"cmc","modified":"1162437707","downloads":"598","group_name":"Computed Muscle Control","logo_file":"cmc","short_description":"Provide a control library for rapidly generating muscle-actuated simulations movements that accurately reproduce a specified movement (e.g., an individual's gait pattern as measured in a clinical laboratory).","long_description":"This project consists of the Computed Muscle Control (CMC) library. CMC is an optimization-based method for controlling systems of articulated rigid bodies. It is specifically designed for controlling systems that have more than one actuator per degree of freedom. Human (and animal) musculoskeletal systems are examples of such systems as a single joint is often actuated by many muscles. CMC also handles the nonlinearities and time delays in force production associated with muscles. The advantage of CMC over alternative optimal control techniques is its speed. CMC makes it possible to generate muscle-actuated simulations with detailed three-dimensional models of the musculoskeletal system (e.g., 23 degrees of freedom, 92 muscles) with about 10 minutes of computer time, which is hundreds or even thousands of times faster than traditional optimal control techniques.\n\nThe CMC algorithm is implemented as a set of C++ classes written on top of OpenSim. OpenSim is an object-oriented framework for modeling, simulating, and analyzing the neuromusculoskeletal system that is also available on Simtk.org.\n\nNote that the CMC source code and documentation will be bundled with future releases of the OpenSim, so it will be unnecessary to download the CMC software from this project.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Frank Clay Anderson","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"159","unix_group_name":"nornalize","modified":"1252717779","downloads":"224","group_name":"noRNAlize: SHAPE data normalization software","logo_file":"nornalize","short_description":"noRNAlize normalizes SHAPE footprinting data","long_description":"This project is a data analysis package to analyze and normalize SHAPE data. Traditionally, SHAPE requires the addition of a 3' hairpin to the RNA for normalization. noRNAlize elminates the need for this experimental step by performing a global analysis of the SHAPE data, and establishing mean protection values. This is particularly important when SHAPE analysis is used to map crystal contacts in crystal structures as illustrated here.","has_downloads":true,"keywords":"footprinting,rna folding,molecular biology,chemical probing","ontologies":"Molecular_Interaction,Data_Analysis_Software","projMembers":"Alain Laederach,Quentin Vicens","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"161","unix_group_name":"openmm","modified":"1472086597","downloads":"134666","group_name":"OpenMM","logo_file":"openmm","short_description":"OpenMM includes everything one needs to run modern molecular simulations. 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Check out the discussion forums under Public Forums and the material from our workshops under Downloads. \n\nGET STARTED QUICKLY: Tutorials and sample scripts to run OpenMM are available in the OpenMM Code Repository.\n\nSOURCE CODE: The source code for OpenMM is available under Downloads and also from the Github Source Code Repository.\n\nBENCHMARKS: A collection of benchmarks is available to show the performance of OpenMM simulating a variety of molecular systems.\n\nCITING OPENMM: Any work that uses OpenMM should cite the papers listed on the Publications page.","has_downloads":true,"keywords":"graphics processing unit,molecular dynamics,gpu","ontologies":"Protein_Model,Molecular_Dynamics,RNA_Model,Molecular_Model,Molecular_Modeling_and_Classification","projMembers":"Vijay Pande,Peter Eastman,Joy Ku,Xuhui Huang,Michael Shirts,Rossen Apostolov,Kai Kohlhoff,Kyle Beauchamp,John Chodera,Imran Haque,Blanca Pineda,Lee-Ping Wang,Chris Sweet,Jack Middleton,D Glazer,Peter Kasson,Kim 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Balaraman,Mohtadin Hashemi,James Starlight,Claudia 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It is based on the CVODES integrator which is part of the DOE Sundials suite. CPODES was developed as a joint project between Simbios and LLNL and implemented by CVODES coauthor Radu Serban working with Michael Sherman, Jack Middleton, and Peter Eastman of Simbios.\n\nCPODES is intended for use with Simbody. It is a multistep integrator providing variable order Adams (up to 12th order) and BDF (up to 5th order) methods for non-stiff problems and BDF (up to 5th order) for stiff problems. It uses CVODES to advance the ODE, and then performs coordinate projection back to the constraint manifold to exactly solve the DAE. The projection is also incorporated back into the error test where it permits larger steps.\n\nIMPORTANT NOTE: binaries of this software are bundled with other SimTK Core modules. They can be found in the SimTKcore project downloads section. Only the source for CPodes is located here.","has_downloads":false,"keywords":"multibody equations,differential algebraic equations,numerical integrator,coordinate projection,stiff integration,implicit integration","ontologies":"Numerical_Integrator","projMembers":"Frank Clay Anderson,Peter Eastman,Randy Radmer,Jack Middleton,Christopher Bruns,Michael Sherman,Radu Serban","trove_cats":[{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"164","unix_group_name":"nano-machines","modified":"1166665285","downloads":"0","group_name":"Protein Models for Biological Machines","logo_file":"","short_description":"As a first step, this physics-based simulation will be used to develop methods to simulate the motion of these models in order to generate alternative plausible conformations.","long_description":"Electron cryomicroscopy (cryoEM) is a maturing field in structural biology that can determine structures of macromolecular machines and cells at a broad range of resolution from 4 to 100 Å. Evidence to the growth of the field, the increasing number of publications in cryoEM has prompted interest from the PDB and EBI to archive density maps and associated models derived from cryoEM (EMDB). Generally, the molecular mass of the biological machine is on the order of 1-100 MDa, which is often too difficult to study by conventional X-ray crystallography. CryoEM is an ideal technique to bridge the information gap between cell biology and crystallography/NMR of individual molecular components of biological machines including viruses, chaperonins, ion channels, ribosomes, transporters, enzymes, filaments and bundles. Single-particle cryoEM can be used to explore the complex and dynamic behavior of individual biological machines in different functional states. However, the resulting data from cryoEM experiments are presently limited to medium (5-10Å) to low (10-20Å) resolutions. This limited resolution is due to several factors including sample heterogeneity due to conformational flexibility. \n\nHere, we hypothesize that single particle cryoEM images contain data of mixed conformations, which can be sorted out computationally to derive multiple models from different subsets of particle images. Therefore, the theme of this proposal is to develop a physics-based computational methodology using SimTK tools to derive an ensemble of potential structural models, which we hypothesize would represent the dynamic nature of the biological machine itself.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mitul Saha","trove_cats":[{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"167","unix_group_name":"allopathfinder","modified":"1285041930","downloads":"1511","group_name":"Predicting allosteric communication in myosin via a conserved residue pathway","logo_file":"allopathfinder","short_description":"
  1. Better understand the allosteric communication pathway used by Myosin to convert ATP hydrolysis energy into movement along actin.
  2. Provide researchers with an application and code for finding protein allosteric pathways.
","long_description":"This project contains the AlloPathFinder application that allows users to compute likely allosteric pathways in proteins. The underlying assumption is that residues participating in allosteric communication should be fairly conserved and that communication happens through residues that are close in space. \nThe initial application for the code provided was to study the allosteric communication in myosin. Myosin is a well-studied molecular motor protein that walks along actin filaments to achieve cellular tasks such as movement of cargo proteins.\nIt couples ATP hydrolysis to highly-coordinated conformational changes that result in a power-stroke motion, or ''walking'' of myosin. Communication between a set of residues must link the three functional regions of myosin and transduce energy: the catalytic ATP binding region, the lever arm, and the actin-binding domain. We are investigating which residues are likely to participate in allosteric communication pathways.","has_downloads":true,"keywords":"allostery,allosteric communication","ontologies":"Computational_Model,Protein_Model,Structure-Based_Protein_Classification,Standalone_Application","projMembers":"Jeanette Schmidt,Susan Tang,Jung-Chi Liao,alex dunn","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":true},{"group_id":"172","unix_group_name":"pcrebin","modified":"1194457619","downloads":"0","group_name":"PCRE regular expression binaries for the SimTK core","logo_file":"","short_description":"Provide application developers with precompiled regular expression libraries for use from C and C++.","long_description":"This project intends to collect precompiled binaries for the PCRE (Perl compatible regular expression) library for the various platforms supported by simtk. This library permits application developers to use modern regular expressions from C and C++ programs.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Christopher Bruns","trove_cats":[{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"179","unix_group_name":"tiss_mech_wg","modified":"1242078311","downloads":"0","group_name":"IMAG Multiscale Modeling Tissue Mechanics Working Group","logo_file":"tiss_mech_wg","short_description":"Provide a project site for the Interagency Modeling and Analysis Group (IMAG) multi-scale modeling (MSM) tissue mechanics working group.","long_description":"The Tissue Mechanics working group has moved to the IMAG Wiki at: http://www.imagwiki.org/mediawiki/index.php?title=Multiscale_Modeling_Working_Groups\n\n\n\nThis project provides a forum for the IMAG (Interagency Modeling and Analysis Group) Multi-scale Modeling (MSM) initiative Tissue Mechanics working group. The working group was formed at IMAG's first annual Multiscale Modeling Consortium held on February 6, 2006. The purpose of the working group is to: 1) determine computational priorities and challenges related to MSM, specifically MSM of tissue mechanics, 2) explore solutions for model sharing, and 3) provide a forum for discussion of issues related to MSM and tissue mechanics.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jeff Reinbolt,Ahmet Erdemir,Trent Guess,Jeff Bischoff,Daniel Einstein,Merryn Tawhai","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"184","unix_group_name":"molmodel","modified":"1398888137","downloads":"338","group_name":"Molmodel: SimTK molecular modeling API","logo_file":"molmodel","short_description":"Provide C++ API for creating molecular models whose dynamics can be simulated using the SimTK Simbody library. A link to the online API reference documentation for Molmodel can be found on the Documents page.","long_description":"Molmodel is a programmer’s toolkit for building reduced-coordinate, yet still all-atom, models of large biopolymers such as proteins, RNA, and DNA. You control the allowed mobility. By default, Molmodel builds torsion-coordinate models in which bond stretch and bend angles are rigid while bond torsion angles are mobile. But you can rigidify or free any subsets of the atoms, such as the rigid benzene ring shown here.\n\nMolmodel is a C++ API for biochemist-friendly molecular modeling that extends the Simbody API to simplify construction of high-performance articulated models of molecules. All of the Simbody API is available when using Molmodel and Simbody must be installed and functioning in order to use Molmodel. See https://simtk.org/home/simbody for more information. 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Please respect your fellow OpenSim Users. \nIn using these utilities we ask that you respect the hard work of your fellow researchers by citing their work appropriately. When you go to the Download section you will be directed to individual project pages for each model which contain all of the files and documentation. Please carefully review the publications and cite the references in your future papers, presentations, grant applications, etc.\n\n
Have a utility to contribute?\nDo you have a utility which you would like to make available through this library? Providing others with access to your tools and utiities can stimulate future studies, provide a foundation for young researchers, and maximize the impact of your work. It’s easy to set up a project page to post your work. This will allow you to track who is using your utilities and be in contact with them. Please consider contributing! If you would like to have your project included on this site, please contact Jennifer Hicks, listed as one of the Project Leads.\n
\nNo guarantees about quality, correctness or support are provided by the SimTK team or OpenSim team. Use at your own risk. \n
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\nPlease cite the following paper:\n-\tHolzbaur KR, Murray WM, Delp SL.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control., Ann Biomed Eng. 2005 Jun;33(6):829-40. (2005)\n\n
\nAbout the model:\nThis model of the upper extremity includes 15 degrees of freedom representing the shoulder, elbow, forearm, wrist, thumb, and index finger, and 50 muscle compartments crossing these joints. The kinematics of each joint and the force-generating parameters for each muscle were derived from experimental data. The model estimates the muscle–tendon lengths and moment arms for each of the muscles over a wide range of postures. Given a pattern of muscle activations, the model also estimates muscle forces and joint moments. The moment arms and maximum moment generating capacity of each muscle group (e.g., elbow flexors) were compared to experimental data to assess the accuracy of the model. These comparisons showed that moment arms and joint moments estimated using the model captured important features of upper extremity geometry and mechanics. The model also revealed coupling between joints, such as increased passive finger flexion moment with wrist extension.\n\n
","has_downloads":true,"keywords":"muscle architecture parameters,upper limb,kinematic model,muscle moment arms","ontologies":"Modeling_and_Simulation","projMembers":"Scott Delp,Katherine Saul,Wendy Murray,Katherine Steele","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"325","unix_group_name":"wrist-model","modified":"1219187433","downloads":"1225","group_name":"Wrist Model","logo_file":"wrist-model","short_description":"Model of the wrist available for download along with accompanying publications.","long_description":"The project holds all the files necessary for a SIMM-based musculoskeletal model of the human wrist which can also be easily imported and used in OpenSIM. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work. The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n<hr> </hr>\n<b>Please cite the following paper:</b>\nGonzalez, R. V., Buchanan, T. S., Delp, S. L. How muscle architecture and moment arms affect wrist flexion-extension moments, Journal of Biomechanics Vol. 30, pp. 705-712, 1997.\n\n<hr> </hr>\n<b>About the model:</b>\nOriginally created by Delp and Gonzalez to investigate motion of the wrist and gain insight into surgical procedures this model consists of all of the bones of the arm and 10 degrees of freedom. Pronation-supination, flexion-extension, and ulnar-radial deviation are all included within the model as well as degrees of freedom for the elbow, thumb, and index finger. A total of 23 muscle actuators control motion.\n\n<hr> </hr>","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Delp,Katherine Steele,Thomas S Buchanan,Roger Gonzalez","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"327","unix_group_name":"dca","modified":"1274054485","downloads":"0","group_name":"Divide and Conquer Coarse-Grain Molecular Modeling","logo_file":"","short_description":"It is difficult to predict a static coarse grain structure which is simultaneously accurate and efficient for the entire course of the simulation. The goal is to develop an adaptive solver which can change the coarse-grain structure on-the-fly.","long_description":"The divide and conquer algorithm [1-3] would make it easier to implement frequent topology changes (by adding or constraining degrees of freedom) in coarse grain molecular models. This approach may be specially useful in situations where it is desirable to adaptively manipulate/change the coarse grain model locally, during the course of simulation.\n\nSimulation Example: \nhttps://simtk.org/docman/view.php/327/1350/pend.gif\n\nCurrent interface with Molmodel:\nhttps://simtk.org/docman/view.php/327/1380/molmodelDCA01.pdf\n\n[1] R. Featherstone, 1999a. A Divide-and-Conquer Articulated-Body Algorithm for Parallel O(log(n)) Calculation of Rigid-Body Dynamics. Part 1: Basic Algorithm. Int. J. Robotics Research, vol. 18, no. 9, pp. 867-875, 1999.\n\n[2] R. Featherstone, 1999b. A Divide-and-Conquer Articulated-Body Algorithm for Parallel O(log(n)) Calculation of Rigid-Body Dynamics. Part 2: Trees, Loops and Accuracy. Int. J. Robotics Research, vol. 18, no. 9, pp. 876-892, 1999.\n\n[3] Rudranarayan M. Mukherjee and Kurt S. Anderson, A Logarithmic Complexity Divide-and-Conquer Algorithm for Multi-flexible Articulated Body Dynamics, Journal of Computational and Nonlinear Dynamics, January 2007, Volume 2, Issue 1, pp. 10-21","has_downloads":false,"keywords":"Molecular Modeling,DCA,Adaptive","ontologies":"Structural_Model","projMembers":"Kishor Bhalerao","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"329","unix_group_name":"climber","modified":"1242774953","downloads":"697","group_name":"Climber: a non-linear protein trajectory morphing method","logo_file":"climber","short_description":"Provide high-fidelity morphed protein trajectories between two known protein conformations.","long_description":"We present a new morphing method that does not move linearly and can therefore go around high energy barriers, and which can produce different trajectories between the same two starting points. 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The model contains 23 degrees of freedom and 92 muscle-tendon actuators. The joint between the torso and the pelvis is represented by a ball-and-socket joint. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work. The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n<hr> </hr>\n<b>Please cite the following paper:</b>\n- Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, vol. 37, pp. 757-767, 1990.\n\n<hr> </hr>\n<b>About the model:</b>\nThe lower-extremity portion of the model was originally developed by Scott Delp to examine how surgical changes in musculoskeletal geometry and muscle architecture affect muscle force and joint motion. With the addition of the torso segment this model has 23 degrees of freedom and 92 muscle actuators. The model is about 1.8m tall and has the strength of a young, adult male. Muscle lines of action are based on their anatomical relationships to three-dimensional surface representations of bones. A model for each actuator was formulated to compute its isometric force-length relation. The kinematics of the lower extremity is specified by modeling the lumbar, hip, knee, ankle, subtalar, and metatarsophalangeal joints. \n\n<hr> </hr>","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Delp,Katherine Steele","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"331","unix_group_name":"jamboree","modified":"1507738885","downloads":"89","group_name":"OpenSim Developer's Jamboree 2008","logo_file":"jamboree","short_description":"Provide access to the latest version of OpenSim and a central repository for the OpenSim Jamboree participants","long_description":"This project will be a repository for the OpenSim Jamboree projects.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joy Ku,Scott Delp,Ajay Seth,Jeff Reinbolt,Ayman Habib,Chand John,Samuel Hamner,Jennifer Hicks,Thor Besier,Kevin Shelburne,Chris Richards,Katherine Saul,Ilse Jonkers,Friedl De Groote,B.J. 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While these tools have been developed, initially, in the context of skeletal structures, they can be applied to a virtually endless number of modeling applications.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"kiran shivanna,Vincent Magnotta,Nicole DeVries,Nicole Grosland,srinivas tadepalli,Austin Ramme,Anup Gandhi,swathi kode","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":false},{"group_id":"335","unix_group_name":"bv_micromechanics","modified":"1225838717","downloads":"0","group_name":"Blood vessel micromechanics","logo_file":"bv_micromechanics","short_description":"FEA package for performing qualitative studies on the micromechanics of blood vessel tissue.","long_description":"This project is a parallel finite element analysis (FEA) tool for nonlinear solid mechanics. The FEA tool uses discontinuous Galerkin which specifically designed for nearly incompressible materials such as biological tissue. In addition to the FEA libraries, the project also includes a set of binaries which describe the geometry of the elastin microstructure in rat aorta. 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The scripts can be configured for any laboratory configuration. This software is free without warranty but I do ask for acknowledgement if used in publications. Free download is available with documentation and two examples included. \n\nMain features of this script include:\n\nCustom markerset extraction\nFoot-plate detection algorithm\nKinetic extraction (ground reaction forces / moments)\nCenter of pressure calculation\nTransformation to customizable model coordinate system\nCustom EMG acquisition & processing tools\nXML file production (for OpenSim)\nLab customizable\n\nThe scripts require Motion Labs C3D Server software (freeware) and XML Toolbox (Marc Molinari)(freeware) which is included with the script download. Also requires Matlab 2008 or greater (32 bit only) with the Signal Processing Toolbox.\n\nAdditional C3D software may be useful and these are available at http://www.c3d.org/c3dapps.html. Review the included manual for version updates and additions. 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Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems, and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM) for exploring functional transitions.\n\nAs described by aANM, a series of intermediate conformations along the transition pathways between the initial and final conformations were generated by successive deformations of both end structures that were iteratively updated. The directions of deformations were determined by implementing the deformations along the directions of dominant ANM modes accessible to the intermediate states. The recruitment of the particular subsets of modes results from a tradeoff between minimizing the path length and selecting the direction of the lowest increase in internal energy. To calculate the ANM modes, please visit our related websites.\nANM: http://ignmtest.ccbb.pitt.edu/cgi-bin/anm/anm1.cgi \nGNM: http://ignm.ccbb.pitt.edu/GNM_Online_Calculation.htm\nPCA_NEST: http://ignm.ccbb.pitt.edu/oPCA_Online.htm\n\nThe bacterial chaperonin GroEL is a supramolecular machine that has been broadly studied in recent years using both experimental and theoretical or computational methods. Yet, a structure-based analysis of the transition of the intact chaperonin between its functional forms has been held back by the large size of the chaperonin. The aANM method is proposed as a first approximation toward approaching this challenging task. \n\nThe application of aANM to GroEL, not only elucidated the highly probable pathways and the hierarchic contribution of modes to achieve the transition; but also provided us with biologically significant information on critical interactions and sequence of events occurring during the chaperonin machinery and key contacts that make and break at the transition.\n\nOn a practical side, the major utility of the method lies in its application to the transitions of supramolecular systems beyond the range of exploration of other computational methods. The computing time in the present method is several orders of magnitude shorter than that required in regular molecular dynamics or Brownian dynamics simulations. \n\n*Figure above: Snapshots of the protein chaperonin GroEL in its transition pathway, evolving from open (upper left) to closed form (lower right). 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Simulation studies up until this point [6, 7] have primarily focused on using abnormal movement patterns to examine variations in muscle coordination without fully considering the impact of changed structural and mechanical muscle-tendon properties. \n\nThe underlying rationale for the proposed work is that muscle weakness arising from altered muscle-tendon structural and mechanical properties may be a primary factor limiting normal gait in CP. We aim to use experimental measures of muscle-tendon structural and mechanical properties along with 3D gait analysis to determine the influence of changed muscle properties on observed movement outcomes. \n\nAim 1: Determine the influence of using the experimentally determined muscle properties from an adolescent CP population on the predicted muscle activation patterns during both normal (TD) and equinus (CP) gait.\n\nAim 2: Perform sensitivity analysis to examine which muscle-tendon properties are most detrimental to gait performance in CP.\n\nREFS -\n1.\tGraham, H.K., Painful hip dislocation in cerebral palsy. Lancet, 2002. 359(9310): p. 907-8.\n2.\tBache, C.E., P. Selber, and H.K. Graham, The management of spastic diplegia. Current Orthopaedics, 2003. 17(2): p. 88-104.\n3.\tBarber, L., et al., Medial gastrocnemius muscle volume and fascicle length in children aged 2-5 years with cerebral palsy. Dev Med Child Neurol, 2011(In Press).\n4.\tBarrett, R.S. and G.A. 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Our model is derived from the lower body model published by Arnold et al. (2010) and the tracking upper body by Hamner et al. 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To find OpenSim motion and simulation data, you can conduct a search on SimTK. Go to https://simtk.org/search/search.php?srch=neuromuscular&type_of_search=soft and then narrow your search to \"data sets\" by checking the box on the left.\n\n----------------------------------\n\nA repository of motion data from experiments and simulations, contributed by members of the OpenSim community.\n\nPlease respect your fellow OpenSim Users. \nIn using these data we ask that you respect the hard work of your fellow researchers by citing their work appropriately. When you go to the Download section you will be directed to individual project pages for each model which contain all of the files and documentation. Please carefully review the publications and cite the references in your future papers, presentations, grant applications, etc.\n\nHave data to contribute?\nDo you have simulation or motion data which you would like to make available through this library? Providing others with access to your data can stimulate future studies, provide a foundation for young researchers, and maximize the impact of your work. It’s easy to set up a project page to post your work. This will allow you to track who is using your data and be in contact with them. Please consider contributing! If you would like to have your project included on this site, please contact Jennifer Hicks, listed as one of the Project Leads.\n\nNo guarantees about quality, correctness or support are provided by the SimTK team or OpenSim team. Use at your own risk. \n\nTo find out more about the OpenSim project, please visit http://opensim.stanford.edu.","has_downloads":false,"keywords":"OpenSim,Simulations,experimental data","ontologies":"Data_Resource,Modeling_and_Simulation","projMembers":"Jennifer Hicks","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":true},{"group_id":"779","unix_group_name":"implicit_ligand","modified":"1346182051","downloads":"49","group_name":"Implicit Ligand Theory: Rigorous Binding Free Energies from Molecular Docking","logo_file":"","short_description":"A reference, not a general-purpose tool, for researchers interested in performing implicit ligand theory calculations.","long_description":"In this study, a theoretical foundation was derived for molecular docking. 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The refined model will not only provide more accurate scientific data on obese locomotion, but will also be used in combination with forward dynamic analyses as a predictive tool to analyze locomotion. 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To view the interactive figure, please see the Downloads section.","has_downloads":true,"keywords":"NF-kB,single cell","ontologies":"Dissemination_Vehicle,Interactive_Web-Based_Tool","projMembers":"Jake Hughey,Derek Macklin,Miriam Gutschow,Markus Covert","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":true},{"group_id":"847","unix_group_name":"iamoeba","modified":"1432110674","downloads":"461","group_name":"iAMOEBA, an Inexpensive AMOEBA Polarizable Water Model","logo_file":"iamoeba","short_description":"The purpose of this project is to provide an easy-to-access repository of information pertaining to the iAMOEBA water model.","long_description":"iAMOEBA is an inexpensive and highly accurate polarizable water model. 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This is an initiative started at the Interagency Modeling and Analysis Group and Multiscale Modeling Consortium (http://www.imagwiki.nibib.nih.gov). Tentative charges of the Committee are to: \ni) adopt a consistent modeling & simulation terminology,\nii) define accreditation procedures for modeling and simulation practice,\niii) demonstrate accreditation workflows, and\niv) promote good practice.\n\nThese charges are executed by the Committee Executive Members and Advisory Council. A full list and profile of the Executive Members and Advisory Council are available on our Wiki page here: http://wiki.simtk.org/cpms/CPMS_Members","has_downloads":false,"keywords":"assessment,simulation,modeling,evaluation,computational medicine","ontologies":"Communication_and_Collaborative_Work,Network_and_Communication,Regulatory_Policy_Resource","projMembers":"Ahmet Erdemir,Marlei Walton,Alison Marsden,Joy Ku,Lealem Mulugeta,Martin Steele,Donna Lochner,Marc Horner,Jerry Myers,Pras Pathmanathan,C. 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It helps spectral clustering identify metastable aggregates with highly populated microstates rather than being distracted by lowly populated states.","has_downloads":true,"keywords":"Molecular Dynamics,Hierarchical Nyström Methods,Markov state model,Conformational Dynamics","ontologies":"Molecular_Dynamics","projMembers":"Yuan Yao,Greg Bowman,Xuhui Huang,jian sun,Raymond CUI,Daniel Silva","trove_cats":[{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"853","unix_group_name":"openmm-amoeba","modified":"1411177671","downloads":"121","group_name":"AMOEBA in OpenMM Speed and Accuracy Benchmark","logo_file":"openmm-amoeba","short_description":"Benchmark the AMOEBA force field as implemented in OpenMM.","long_description":"Click \"Documents\" and download the instruction manual to get started.\n\nThis project contains the files needed to benchmark the AMOEBA force field as implemented in OpenMM and TINKER. The contents are:\n\n1) OpenMM and TINKER input files for six water boxes of progressively increasing size (216 through 288,000 molecules) and DHFR in explicit water from the Joint AMBER-CHARMM benchmark. \n\n2) Script for automatically running the benchmark across different methods (i.e. force field and run parameters), systems, and platforms.\n\n3) Output data from running the benchmark on a high performance compute node (CPU: 2x Intel Xeon E5-2643 3.30GHz, GPU: NVidia GeForce GTX Titan Black).\n\n4) Instruction manual.\n\nThe simulations are NVT (298.15 K), 1 and 2 fs time steps (2 fs time step uses MTS algorithm), PME electrostatics with real space cutoff 7.0 Angstrom, vdW cutoff 9.0 Angstrom. Simulation speed is given in ns/day.","has_downloads":true,"keywords":"OpenMM,AMOEBA","ontologies":"","projMembers":"Lee-Ping Wang","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"854","unix_group_name":"children","modified":"1366699384","downloads":"0","group_name":"An Average Child's Model based on 5 MRI Data Sets","logo_file":"children","short_description":"Musculo-skeletal models for the lower limbs of children based on MRI-data","long_description":"The model generated for this project is an average child model for children. This model was generated using on 5 individual MRI-based models of children aged 7-9. A mail reason is to provide a template model for individual biomechanical analysis for children, where the scaling factors are not that high as when generating childs models based on the adult templates. Further, the child model reproduces the body composition of children. It has a muscular structure from young humans and not, as in most template models a muscle architecture that is based on the examination of cadavers of aged population.\nThe muscle parameters such as optimal muscle fiber length and tendon slack lengh have been set in a way that the model has similar optimal joint angles as the template models provides with the OpenSim installation.","has_downloads":false,"keywords":"muscle architecture parameters,Children,lower limbs,image-based geometric modeling","ontologies":"Computational_Model,Aggregate_Human_Data","projMembers":"Reinhard Hainisch","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"856","unix_group_name":"posturefeedback","modified":"1457549243","downloads":"0","group_name":"Sensory Components for Simulating Postural Feedback Control in OpenSim","logo_file":"posturefeedback","short_description":"This project proposes to develop new model components in OpenSim that create physiologically based sensory signals during dynamic simulations. These sensory components will be evaluated with simulations of postural stability.","long_description":"This project provides software for simulating physiological sensors and postural stability with feedback responses.","has_downloads":false,"keywords":"sensor components,musculoskeletal simulation,Perturbation Platform,Postural Stability,sensorimotor control,reflex controller,neuromuscular control","ontologies":"Neuromuscular_Model","projMembers":"Ajay Seth,Matt DeMers,Ian Stavness,Tybie Vickers,Gordon Cooke,Mohammad Shabani,Vaughn Friesen,Omar Zarifi","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":false},{"group_id":"860","unix_group_name":"opcontrol","modified":"1370155498","downloads":"0","group_name":"Operational-Space Control Framework for OpenSim","logo_file":"opcontrol","short_description":"Provide an easy-to-implement operational-space controller.","long_description":"The aim of this project is to provide a new tool for biomechanics researches using OpenSim. \n\nThe operaltional-space control framework, introduced by O.Khatib, 1993, is particularly useful in simulating human motion tasks, such as\n\nx) Manipulation\nx) Reaching\nx) Athletics","has_downloads":false,"keywords":"operational space control,custom controller","ontologies":"","projMembers":"Gerald Brantner","trove_cats":[{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":true},{"group_id":"861","unix_group_name":"max_jump_opt","modified":"1370290437","downloads":"126","group_name":"Sky Higher: Dynamic Optimization of Maximum Jump Height","logo_file":"","short_description":"Provides code for using dynamic optimization to maximize jump height of a musculoskeletal model with bang-bang control.","long_description":"This project was first created for a class project in ME 485 to discover a bang-bang excitation pattern for a muscle-driven model that maximizes jump height. This was done by using tools in SimBody and OpenSim to create a dynamic optimization routine. This project can be used as an example to create other dynamic optimizations.","has_downloads":true,"keywords":"musculoskeletal simulation,Optimization","ontologies":"Neuromuscular_Model","projMembers":"Matthew Titchenal,Carmichael Ong","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"862","unix_group_name":"calfshortening","modified":"1370502968","downloads":"16","group_name":"How Robust is Human Gait to Calf Muscle Shortening?","logo_file":"","short_description":"Provides the models and simulation results for testing the sensitivity of gait to progressive calf muscle shortening.","long_description":"This is a sensitivity study examining the robustness of human gait to progressive contracture of the gastrocnemius and soleus muscles. Study results can be used to understand what kinds and degrees of contracture likely lead to equinus, or tip toe walking, which is a characteristic feature of gait in Cerebral Palsy patients.\n\nhttp://www.youtube.com/watch?v=dSl9W6m16-4","has_downloads":true,"keywords":"musculoskeletal biomechanics,Gait analysis,OpenSim,Cerebral palsy","ontologies":"Neuromuscular_Model","projMembers":"Thomas Uchida,Katrina Wisdom","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"863","unix_group_name":"fatigablemuscle","modified":"1380233837","downloads":"211","group_name":"Developing a fatigable muscle model","logo_file":"","short_description":"Provides the code to implement a fatigable muscle model.","long_description":"This project is about developing a muscle model in OpenSim that demonstrates properties of muscle fatigue. This muscle model will also take into account fiber composition and some considerations for orderly recruitment of muscle fibers. This model is still a work in progress and was developed as part of 4-week project for a class on the modeling and simulation of human movement. A more detailed description and documentation of the model can be found on the OpenSim support page at http://simtk-confluence.stanford.edu:8080/display/OpenSim/Design+of+a+Fatigable+Muscle","has_downloads":true,"keywords":"muscle modeling,muscle,fatigue","ontologies":"Neuromuscular_Model,Dynamic_Model","projMembers":"Apoorva Rajagopal,Jennifer Yong","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"864","unix_group_name":"fatigablerunner","modified":"1370577964","downloads":"37","group_name":"The Fatigable Runner","logo_file":"","short_description":"Provide a platform to test different running styles to examine the effects of fatigue","long_description":"There has been an ongoing debate for a long time as to which running form is the optimal for performance for endurance, long distance running. Numerous personal accounts for improvement in performance has been reported by everyday athletes all the way to world champion triathletes describing a number of modified components to their running form that have helped in making them run faster and more efficiently. But none of which has really been well documented scientifically. Though this project is only a tiny step toward analyzing differences in running forms with preliminary data, it provides a platform for future biomechanists to examine the key differences in running forms of different individuals.\n\nThis is an extremely difficult question to answer since there is almost no way to experimentally test the differences between running forms. This is because it is impossible to have adequate controls. Every runner is different in height, weight, fitness level, muscular strength, etc. Every runner also runs differently. It’s not possible for one runner to replicate many different styles in an experiment as every runner has his/her own natural style. However, if different running motion kinematics can be replicated with one model of runner, the differences may be well observed.\n\nUtilizing opensim software, simulations can be performed using the same runner and replicating different running styles. Computed muscle controls also allows muscle activations to be calculated and compared. An additional feature is the adding custom muscle model that fatigues to mimic an endurance running event. Since there has been no model with fatigable muscle implemented before, there are a lot of questions that can only be answered through simulation with fatigable muscles. Differences in running form may be analyzed without considering muscle fatigue, but inducing muscle fatigue may provide more insight.","has_downloads":true,"keywords":"fatigue,running","ontologies":"","projMembers":"Yu Hsiao,Aaron Wayne","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":false},{"group_id":"865","unix_group_name":"flippin-felines","modified":"1381786134","downloads":"103","group_name":"Flippin' Felines: Controlling a Cat Model to Land on Its Feet","logo_file":"flippin-felines","short_description":"(1) Controlling cat models to land on their feet (2) Provide a framework for learning and teaching OpenSim, including an instructional tutorial","long_description":"Cats are known for an uncanny ability to land on their feet. This project explores this phenomenon, known as the \"cat-righting reflex\". It was completed for ME/BIOE 485: Modeling and Simulation of Human Movement, a course at Stanford University.\n\nFrom a research perspective, we aim to answer two questions: (1) When falling upside down, what control strategies does a cat use to flip itself over and land on its feet? (2) What is the minimum model of a cat that will flip in a biologically realistic manner? For example, can the cat flip without being able to twist its spine? We begin to answer these questions using dynamic optimization and 'step-wise' model creation. The modeling and optimization source code is available for download.\n\nIn addition to these basic research questions, we believe that the cat-flipping problem provides an ideal framework for learning and teaching OpenSim. For this reason, we have compiled simplified versions of our modeling and optimization code. While this code is also available for download here, it is laid out in the form of an instructional tutorial on our Confluence webpage, which also contains a more detailed description of the project and our results.\n\nHere is a short video overview of the project:\n\n","has_downloads":true,"keywords":"cat,dynamic optimization,reflex,modeling","ontologies":"Controllers,Multibody_Dynamics","projMembers":"Christopher Dembia,Sean Sketch","trove_cats":[{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":false},{"group_id":"866","unix_group_name":"exosuit","modified":"1381786639","downloads":"903","group_name":"Modeling, Evaluation, and Control Optimization of Exosuit with OpenSim","logo_file":"exosuit","short_description":"- Gives an example of how to use OpenSim as an analysis tool of wearable device\n- Provide example simulation models that is easy to use and modify","long_description":"This project tackles the challenges of developing wearable device using OpenSim simulation. Simulation can help developing wearable device as it can give an intuition on how wearable devices interact with human and how muscle activations change when a subject wear the device. We can also find the key features that one should account for when designing wearable device in order to make it efficient.\n\nIn this project, I focused on specific wearable device, called Harvard Exosuit. BioDesign group at Harvard University is developing this suit, and the main idea is to create a soft and deformable under-suit which can assists loaded walking.\n\nMy accomplishments through this project are\n\n- Evaluate the effectiveness of wearing active actuators on metabolic cost reduction.\n- Explain how Exosuit can help loaded gait\n- Verify the impact of changes of design parameters\n- Find optimal control inputs for active actuators \n\nHere is a video clip for project overview:\n\n\n\nIf you want more details of this project, please visit the project confluence webpage: Confluence page","has_downloads":true,"keywords":"External actuation,Exosuit,Metabolic cost,Wearable device,Loaded gait","ontologies":"","projMembers":"Jaehyun Bae","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"867","unix_group_name":"iaa_controller","modified":"1370591254","downloads":"210","group_name":"Induced Accelerations-based controller for balance","logo_file":"iaa_controller","short_description":"We attempt to use an Induced Accelerations Analysis of a biomechanical model to control position and maintain balance.","long_description":"We attempt to use an Induced Accelerations Analysis of a biomechanical model to control position and maintain balance.","has_downloads":true,"keywords":"balance control","ontologies":"Controllers","projMembers":"Christopher Ploch,Mishel Johns","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":false},{"group_id":"868","unix_group_name":"nmlmuscle","modified":"1491431745","downloads":"2","group_name":"Physiologically inspired muscle models","logo_file":"","short_description":"Development and validation of new muscle models for use in musculoskeletal modelling","long_description":"The purpose of this study is to implement current physiological findings about mechanical properties of whole muscles into the OpenSim platform, and to validate the performance of existing and enhanced muscle models. Recent testing and validation of the muscle models with experimental work has shown that muscle models with independent fast- and slow-contracting elements can predict muscle force better and more accurately than previous Hill-type models with only a single contractile element. We have developed a plug-in muscle model to achieve this property in OpenSim, supported by an OpenSim pilot project grant. Recent testing shows that the new plug-in module generates the expected changes in contractile performance when the proportion of fast- to slow- activation is changed. We are currently collecting experimental data to test the validity of the constant-thickness assumption of the existing muscle model, and propose to incorporate more structurally relevant properties if they improve the accuracy of the muscle model.\n\n\nPublication overview\nWakeling, J.M., Lee S.S.M., Arnold A.S., de Boef Miara, M., & Biewener, A.A.. A muscle's force depends on the recruitment patterns of its fibres. Ann. Biomed. Eng. 40, 1708-1720, (2012).\nWakeling, J.M. & Randhawa, A. 1D, 2D and 3D structural models for Hill-type muscle models. Computer Methods in Biomechanics and Biomedical Engineering (11th Int. Symposium). Salt Lake City, UT, (2013).\nLee, S.S.M., deBoef Miara, M, Arnold, A.S., Biewener, A.A., Wakeling, J.M. “Hill-type muscle model with slow and fast fiber contractile elements”. Annual Meeting of the American Society of Biomechanics, Omaha, NB, (2013).\nLee, S.S.M., deBoef Miara, M, Arnold, A.S., Biewener, A.A., Wakeling, J.M. “A two-element Hill-type model to predict muscle forces”. Society of Comparative and Integrative Biology Annual Meeting, San Francisco, CA, (2013).\nLee, S.S.M., de Boef Miara, M., Arnold, A.S., Biewener, A.A., Wakeling, J.M. Accuracy of gastrocenmius forces in walking and running goats predicted by one element and two-element Hill-type models. J. Biomech. In press.","has_downloads":true,"keywords":"Forward Dynamic Simulation,Musculoskeletal Model,muscle modeling","ontologies":"Neuromuscular_Model","projMembers":"Allison Arnold,david lu,Adrian Lai,Taylor Dick,James Wakeling,Sabrina Lee","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"871","unix_group_name":"sweetlead","modified":"1383955436","downloads":"614","group_name":"SWEETLEAD: A cheminformatics database of medicines, drugs, and herbal isolates.","logo_file":"sweetlead","short_description":"Individual chemical databases sometimes contain incorrect structural information about drugs. We seek to address this problem by providing accurate structures for use in drug discovery efforts and cheminformatics analysis.","long_description":"The SWEETLEAD database has been created to provide an exhaustive and highly curated resource for chemical structures of the world's approved medicines, illegal drugs, and isolates from traditional medicinal herbs. This database has been built using a consensus generating scheme pulling data from several public chemical databases (such as PubChem, ChemSpider, PharmGKB, etc.), as detailed in the publication.","has_downloads":true,"keywords":"computer-aided drug discovery,cheminformatics,repurposing","ontologies":"Database,Delimited_Table","projMembers":"Vijay Pande,Paul Novick","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"872","unix_group_name":"ncsu_exo_hop","modified":"1374129125","downloads":"0","group_name":"Musculoskeletal Modelling of Hopping in Elastic Ankle Exoskeletons","logo_file":"ncsu_exo_hop","short_description":"Understand how ankle exoskeletons affect ankle muscle mechanics and energy consumption","long_description":"Assistive exoskeletons have the potential to aid locomotor recovery and restore walking function in neuromuscularly and musculoskeletally impaired individuals (e.g. post-stroke or spinal cord injury). They also may be used to augment locomotor performance in healthy individuals by reducing the metabolic cost of locomotion or reducing skeletal loading. Whilst these devices have been shown to be effective in replacing joint moments and powers, little is known about how they influence the underlying muscle function. I have collected experimental data on humans examining how ankle exoskeletons affect Soleus muscle function. The purpose of this proposed work is to: (1) Develop an OpenSim-based model of hopping with spring-loaded ankle exoskeletons; (2) Verify that model with the experimental data; and (3) Use the model to examine the mechanics of other muscle groups within the leg.","has_downloads":false,"keywords":"Muscle Function,exoskeleton,Metabolic cost","ontologies":"Neuromuscular_Model","projMembers":"Dominic Farris","trove_cats":[{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"873","unix_group_name":"rhm3dwalk","modified":"1374866748","downloads":"0","group_name":"3D model for simulating the mechanics and energetics of human walking","logo_file":"","short_description":"Provide a code for customizing, performing, and optimizing forward dynamics simulations of human locomotion.","long_description":"This project provides a Fortran code for simulating the mechanics and energetics of human walking in three dimensions. The model has 23 degrees of freedom (pelvis, trunk, thighs, shanks, feet, toes) and 40 Hill-based muscle model actuators. The muscle excitation control scheme is customizable. Implementations of several popular muscle energetics models are provided. Experimental joint motion and GRF data are provided for performing tracking simulations. The model can also perform predictive simulations (no tracking) through customization of the cost function.","has_downloads":false,"keywords":"3D,Musculoskeletal model,Forward dynamics,Hill model","ontologies":"","projMembers":"Ross Miller","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"879","unix_group_name":"spine_mechanic","modified":"1375899330","downloads":"0","group_name":"Spine Movement","logo_file":"","short_description":"visualize 3D movement of spine","long_description":"Generate Euler Angles and joint forces of vertebral bodies","has_downloads":false,"keywords":"Lumbar Spine","ontologies":"","projMembers":"Linda McGrady","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"882","unix_group_name":"flexfbatfba","modified":"1387680600","downloads":"64","group_name":"Flexible (flexFBA) and time-linked (tFBA) Flux Balance Analysis methods","logo_file":"","short_description":"Reproduce publication results for methods flexible and time-linked Flux Balance Analysis (flexFBA and tFBA). Additionally a minimal flexFBA example using Matlab Cobra Toolbox formatted models.","long_description":"(provided for computational biologists to reproduce publication results, and a small utility written for example use with the Cobra toolbox) The associated publication describes two complimentary methods that remove the inherent long-time assumptions of the biomass reaction used in FBA. Implementing a flexible objective flexFBA, enables a metabolic network to produce biological process reactants independently from one another. This flexibility is in contrast to the rigid proportion held by the traditional biomass reaction of FBA. Also, time-linked simulation (tFBA) can represent transitions between metabolic steady states by returning cell process byproducts at subsequent time-steps.","has_downloads":true,"keywords":"flux-balance analysis","ontologies":"","projMembers":"Elsa Birch","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"886","unix_group_name":"prob_tool","modified":"1417553036","downloads":"149","group_name":"Probabilistic Tool for Considering Patient Populations & Model Uncertainty","logo_file":"prob_tool","short_description":"Will provide a probabilistic tool to assess model parameter uncertainty and intersubject variability.","long_description":"The goal of this project is to develop a generalized, probabilistic plugin for OpenSim and to demonstrate subject-specific and population-based applications of this tool. The tool will implement two probabilistic methods (Monte Carlo and advanced mean value) and provide a user-friendly interface to create analyses and visualize results. The probabilistic tool will quantify 5 to 95% confidence bounds for output measures and sensitivity factors, which are used to identify the most important input parameters that contribute to output variability. A subject-specific model will be used to account for measurement errors associated with motion capture and input parameter uncertainties. The code is currently written in Matlab but future releases and additions will explore other applications.","has_downloads":true,"keywords":"probabilistic,hip,population,gait,uncertainty,Variability","ontologies":"","projMembers":"Kevin Shelburne,Casey Myers,Bradley Davidson,Peter Laz","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"888","unix_group_name":"kneemodel","modified":"1472675369","downloads":"313","group_name":"Discrete Element Knee","logo_file":"kneemodel","short_description":"The purpose of this project is to provide an open source, discrete element knee model.","long_description":"This project implements a discrete element knee model in OpenSim. Specifically, the model is a six degree-of-freedom tibiofemoral and one degree-of-freedom patellofemoral joint. It includes eighteen ligament bundles and tibiofemoral contact and was validated against cadaveric data.\n\nPublications:\n\nSchmitz, A., Piovesan, D. (2015) Development of an Open-Source, Discrete Element Knee Model. IEEE Transactions on Biomedical Engineering Special Issue on Modeling. In press.\n\nSchmitz, A. (2015) Development of an Open-Source, Discrete Element Knee Model. (poster presentation the 39th Annual Meeting of the American Society of Biomechanics). Columbus, OH: ASB.","has_downloads":true,"keywords":"computational model,discrete element model,knee,OpenSim,open-source","ontologies":"","projMembers":"Anne Schmitz","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"891","unix_group_name":"mcbs","modified":"1389695132","downloads":"0","group_name":"Multidimensional Cubic B-Spline","logo_file":"","short_description":"Provide a software for fast estimation of lengths and three-dimensional moment arms for musculotendon actuators.","long_description":"This project presents a C++ implementation of multidimensional cubic B-spline for the fast estimation of lengths and three-dimensional moment arms for musculotendon actuators.\nAt first, an OpenSim musculoskeletal model is used to establish the length of each musculotendon actuator for different generalized coordinates (joint angles). Then, multidimensional spline function is computed to fit the previous data. Muscle moment arms are obtained by differentiating the musculotendon length spline function with respect to the generalized coordinate of interest.","has_downloads":false,"keywords":"muscle moment arms,Muscle-tendon length,musculoskeletal simulation","ontologies":"Modeling_and_Simulation,Neuromuscular_Model","projMembers":"Monica Reggiani,Massimo Sartori","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"892","unix_group_name":"strideinpatient","modified":"1471460828","downloads":"2","group_name":"Electronic Medical Record Data-Mining","logo_file":"","short_description":"Code and scripts to support mining EMR data from Stanford STRIDE","long_description":"EMR data-mining code such as association rules for order recommendations and outcome predictions and order set evaluation","has_downloads":true,"keywords":"EMR","ontologies":"","projMembers":"Jonathan Chen","trove_cats":[{"id":"400","fullname":"Data Sets"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"896","unix_group_name":"drugfeature","modified":"1503682809","downloads":"17","group_name":"Identifying druggable targets by protein microenvironments matching","logo_file":"","short_description":"The druggability of a target protein is its potential to be modulated by small, drug-like molecules. Druggability is an important criterion in the target selection phase of drug discovery. However, an effective standard for evaluating target druggability","long_description":"DrugFEATURE is a computational method that evaluates target druggability by assessing the protein microenvironments in potential small molecule binding sites.","has_downloads":true,"keywords":"drug discovery","ontologies":"","projMembers":"Russ Altman,Tianyun Liu","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"897","unix_group_name":"showmotion","modified":"1380646348","downloads":"0","group_name":"S-HOW Motion\nMovement Characterization based on wearable technology","logo_file":"showmotion","short_description":"1) Provide a quick reference for researchers regarding complex motions and scenarios\n\n2) Enhance research, going from the laboratory settings data to the on-the-field data available","long_description":"Wearable technology allows to capture data on the field, allowing new kinematics capture scenarios: interacting with the subject (for example by mobilization), running outside, going upstairs/downstairs, working at the desk, pitching on the field, walking along tens of meters.\n\nThis project will provide little by little new sets of motion data, initially mainly kinematics, which can be useful in research for several reasons:\n\n1) explore kinematics of complex movements\n\n2) explore new simulation opportunities\n\n3) concentrate your time (not your attention of course) more on simulation and data interpretation rather than capturing kinematics\n\n4) enhance your available data sets for running Opensim simulation\n\n5) discuss and share ideas around new motions to be captured, new scenarios where you need simulation\n\nData will be mainly provided applying the measurement protocols called ISEO and OUTWALK published on Medical & Biological Engineering & Computing in 2008 and 2010.\n\n[1]A. G. Cutti, A. Giovanardi, L. Rocchi, A. Davalli, and R. Sacchetti, “Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors,” Med Biol Eng Comput, vol. 46, no. 2, pp. 169–178, Feb. 2008.\n\n\n[2]A. Cutti, A. Ferrari, P. Garofalo, M. Raggi, A. Cappello, and A. Ferrari, “‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors,” Med Biol Eng Comput, Nov. 2009.","has_downloads":false,"keywords":"Running,Musculoskeletal model,motion capture,Motion Analysis,human motion characterization,Gait analysis,walking,kinematics,Trunk,gait,shoulder","ontologies":"Data_Exploration,Knowledge_Extraction,Data_Visualization,Individual_Human_Data,Data_Resource,Graph_Analysis","projMembers":"PIETRO GAROFALO","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"898","unix_group_name":"dmpsynergies","modified":"1381737788","downloads":"250","group_name":"Interaction Between Biology and Robotics","logo_file":"dmpsynergies","short_description":"Provides a musculoskeletal arm model with eleven muscles and an easy to use Matlab interface to run forward dynamics simulations.","long_description":"In this project we study a salient feature of human motor skill learning that is the ability to exploit similarities across related tasks. We test this concept in forward dynamics simulations of multiple reaching movements of a musculoskeletal arm model with eleven muscles. 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The expected outcome of this project is a more accurate model for simulating below-knee amputee gait that will form the basis for a new integrated design approach based on OpenSim to optimize the design of lower-limb prostheses. See the associated publication for more detail. The development of this model and related research were supported by a grant from the U.S. National Science Foundation (1526986) and pilot grant from the National Center for Simulation In Rehabilitation Research. \n\n","has_downloads":true,"keywords":"Compliant,Socket Interface,Amputee,Lower Limbs,Below-Knee","ontologies":"Neuromuscular_Model,Modeling_and_Simulation","projMembers":"Frank Sup,Vinh Nguyen,Andrew LaPre,Brian Umberger,Mark Price,Ryan Wedge,Russell Johnson","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"938","unix_group_name":"ghf-phd","modified":"1400865675","downloads":"19","group_name":"Connecting features of drugs to molecular, cellular, and organismal phenotypes","logo_file":"ghf-phd","short_description":"Datasets of protein targets and ligands for benchmarking virtual screening methods.","long_description":"This project contains datasets that I generated for my PhD thesis titled \"Connecting chemical features of drugs to molecular, cellular, and organismal phenotypes\". These data have been \nused to developed methods to quantify the connections between chemical features of drugs at the molecular, cellular, and organismal level. Specifically, in the thesis I present methods for predicting activities among structurally diverse sets of drugs, for predicting up-regulation of genes based on chemical features of drugs, and a quantified approach to estimating clinical impacts of genotype guided therapies.","has_downloads":true,"keywords":"pharmacogenomics,cheminformatics,virtual screening,bioinformatics","ontologies":"","projMembers":"Guy Haskin Fernald","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":true},{"group_id":"939","unix_group_name":"cruciate_model","modified":"1406809820","downloads":"0","group_name":"Specimen specific finite element model to study cruciate mechanics.","logo_file":"","short_description":"Provide a model of the knee in order to study the effects on the cruciate ligaments when forces of varying position and magnitude are applied to the knee joint.","long_description":"This project will create a model for the anterior and posterior cruciate ligaments (ACL and PCL)from magnetic resonance imaging (MRI) images. This model will allow users to discover the stresses, strains, and displacements of the ACL and PCL that will result from varying forces applied at different positions on the knee.","has_downloads":false,"keywords":"Knee Ligaments,cruciate","ontologies":"","projMembers":"Snehal Chokhandre,Ahmet Erdemir,Jason Halloran,Piyush Walia,Craig Bennetts,Katie Stemmer","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"940","unix_group_name":"normalhumanlvs","modified":"1511885180","downloads":"48","group_name":"Normal human left ventricular myofiber stress","logo_file":"normalhumanlvs","short_description":"Define normal human left ventricular myofiber mechanical properties and stress as a target for computational optimization of cardiac procedures.","long_description":"Ventricular wall stress is believed to be responsible for many physical mechanisms taking place in the human heart, including ventricular remodeling, which is frequently associated with heart failure. Therefore, normalization of ventricular wall stress is the cornerstone of many existing and new treatments for heart failure. In this paper, we sought to construct reference maps of normal ventricular wall stress in humans that could be used as a target for in silico optimization studies of existing and potential new treatments for heart failure. To do so, we constructed personalized computational models of the left ventricles of five normal human subjects using magnetic resonance images and the finite element method. These models were calibrated using left ventricular volume data extracted from magnetic resonance imaging (MRI) and validated through comparison with strain measurements from tagged MRI (950 ± 170 strain comparisons/subject). The calibrated passive material parameter values were C0 = 0.115 ± 0.008 kPa and B0 = 14.4 ± 3.18; the active material parameter value was Tmax = 143 ± 11.1 kPa. These values could serve as a reference for future construction of normal human left ventricular computational models. The differences between the predicted and the measured circumferential and longitudinal strains in each subject were 3.4% ± 6.3% and 0.5% ± 5.9%, respectively. The predicted end-diastolic and end-systolic myofiber stress fields for the five subjects were 2.21 ± 0.58 kPa and 16.54 ± 4.73 kPa, respectively. Thus, these stresses could serve as targets for in silico design of heart failure treatments.","has_downloads":true,"keywords":"Tagged MRI,computational modeling,Normal human subjects,Patient-specific modeling","ontologies":"","projMembers":"Julius Guccione,gabriel Acevedo-Bolton,Lik Chuan Lee,Martin Genet","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"4