Investigators
Dr. Satish S. Nair, Dr. David J. Schulz, Dr. Ilker Ozden, Dr. Yi Zhang
1. Define context(s)
identify/explore new therapies
other
Primary goal of the model/tool/database
Network models for the lower urinary tract (LUT) are not publicly available presently to guide the development and optimization of components of the neural lower urinary tract in mammals.
Although research has helped shed light on the functioning of specific components of the neural LUT in mammals, and of the effect of neuromodulation on specific dysfunction, the dynamics of the linkages among the various components of the LUT circuit and how they coordinate function during normal and injury cases remains unclear. There is need for a fully functional biophysical network model integrating the key components of the LUT for understanding function and for designing stimulation techniques to alleviate dysfunction, to both (i) optimize existing strategies and (ii) help design improved neuromodulation therapy for bladder dysfunction.
Biological domain of the model
lower urinary tract (LUT) of mammals
Structure(s) of interest in the model
neural LUT, bladder, afferents, efferents
Spatial scales included in the model
10^-6 to 10^-2 meters
Time scales included in the model
10^-4 to 10^2 seconds
Other uses for the model (optional)
The sand-box models of component and overall network models will also be very useful for training researchers and device development teams about (i) the normal functioning of the lower urinary tract system, and (ii) the effect of neuromodulation therapies such as open- and closed-loop electrical stimulation, and pharmacological interventions to alleviate dysfunction. In addition, the material can be incorporated into undergraduate and graduate courses such as physiology, neuroscience, bioengineering, device development, and computational neuroscience, at both graduate, undergraduate and K-12 levels. At Univ of Missouri, we plan to incorporate this as a 'case study' into undergraduate and graduate computational neuroscience course that the PI teaches, and into neural engineering lessons that he is developing for K-12.
2. Data for building and validating the model
| Data for building the model |
Published? |
Private? |
How is credibility checked? |
Current Conformance Level / Target Conformance Level |
| in vitro (primary cells cell, lines, etc.) |
Yes |
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Extensive |
| ex vivo (excised tissues) |
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| in vivo pre-clinical (lower-level organism or small animal) |
Yes |
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Extensive |
| in vivo pre-clinical (large animal) |
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| Human subjects/clinical |
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| Other: ________________________ |
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| Data for validating the model |
Published? |
Private? |
How is credibility checked? |
Current Conformance Level / Target Conformance Level |
| in vitro (primary cells cell, lines, etc.) |
Yes |
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Extensive |
| ex vivo (excised tissues) |
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| in vivo pre-clinical (lower-level organism or small animal) |
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| in vivo pre-clinical (large animal) |
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| Human subjects/clinical |
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| Other: ________________________ |
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3. Validate within context(s)
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Who does it? |
When does it happen? |
How is it done? |
Current Conformance Level / Target Conformance Level |
| Verification |
On-line session held every six months for independent verification by users |
Continuous by the developers. Every six months by independent users. |
We will conduct on-line session sessions for independent users by providing them with access to models hosted at our Lab server. Interested users will be provide free accounts so they can test out the versions at the site and provide feedback |
Extensive |
| Validation |
Developers and Users will perform independent evaluation |
Through out the model development process |
Again, on-line sessions will be provided for independent users. They will be provided with access to models hosted at our Lab server. |
Extensive |
| Uncertainty quantification |
One of the central goals of the model is to quantify the role of uncertainty in component and network parameters, all of which impact the network outputs |
Through out the development process |
Since this is one of the key objectives of the project, all developers explicitly design the model providing options for uncertainty in parameters |
Extensive |
| Sensitivity analysis |
The model GUI will have buttons to permit users to perform sensitivity analysis. Results from such analysis will be provided in the documentation for the key parameters, one at a time. Users can try other combinations. |
For each new case |
We will provide a tool to perform such an analysis automatically, once the user selects the parameter of interest |
Adequate |
| Other:__________ |
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| Additional Comments |
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4. Limitations
| Disclaimer statement (explain key limitations) |
Who needs to know about this disclaimer? |
How is this disclaimer shared with that audience? |
Current Conformance Level / Target Conformance Level |
| Mouse; But will provide models for cat and human using limited data presently available |
Researchers and Clinicians |
Models for mouse, cat and humans will be provided, but highlighting that data are only beginning to come in for cat and human |
Extensive |
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5. Version control
| Current Conformance Level / Target Conformance Level |
| Extensive |
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Naming Conventions? |
Repository? |
Code Review? |
| individual modeler |
Yes |
Subversion (off site) |
Yes |
| within the lab |
NA |
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| collaborators |
NA |
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Yes |
6. Documentation
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Current Conformance Level / Target Conformance Level |
| Code commented? |
Extensive. Code commenting will follow normal guidelines. We have uploaded several models in NEURON to the ModelDB database of Yale/Duke |
| Scope and intended use described? |
Extensive. All documentation will have this. |
| User’s guide? |
Extensive. Every model will have detailed user's guide, as well as YouTube tutorials |
| Developer’s guide? |
Extensive. All documentation above will also highlight how the users can modify and/or use their own code for any component in the network model. Examples of all such scenarios will be provided, as appropriate, including using YouTube tutorials. |
7. Dissemination
| Current Conformance Level / Target Conformance Level |
| Extensive |
| Target Audience(s): |
“Inner circle” |
Scientific community |
Public |
| Simulations |
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| Models |
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Free access to all models for interested users via our server and for download via a GitHub site |
Free access to all models for interested users via our server and for download via a GitHub site |
| Software |
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Interested users can download from a GitHub site |
Interested users can download from a GitHub site |
| Results |
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Publications and website |
Publications and website |
| Implications of results |
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Publications and website |
Publication and website |
8. Independent reviews
| Current Conformance Level / Target Conformance Level |
| Extensive |
| Reviewer(s) name & affiliation: |
To Be Determined |
| When was review performed? |
To Be Determined |
| How was review performed and outcomes of the review? |
To Be Determined |
9. Test competing implementations
| Current Conformance Level / Target Conformance Level |
| Extensive |
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Yes or No (briefly summarize) |
| Were competing implementations tested? |
To Be Determined |
| Did this lead to model refinement or improvement? |
To Be Determined |
11. (optional) Additional information to support items 1-10
Models of neurons and networks continue to be developed at multiple levels by many Labs, including ours. Standards for assessment of the models typically starts with a match with biological data, i.e., validation. After that key criterion for assessing the utility of the models is whether it can provide testable predictions for the the neuroscientists, and for device-development teams. The focus of our project is to develop sand-box models to enable users to test 'what if' scenarios that they can design to explore testable predictions of interest to them.
