FindSiminterface

After entering into the website of FindSim Interface following window appears

Landing Page:

Login with your username and password;

For example:

User name: abc

Password: your password

C: FindSimDB, a collection of structured specification of experiment and its mapping to the simulation, implemented as a findsim files.

What else should be done on FindSim Interface

FindSim: A Framework for Integrating Neuronal Data and Signaling Models

ModelList

NonCurated

Projects

FindSim Experiments

Create New Account by logging into main window of FindSim interface

The link of one time password has been sent to the registered email.

Click on the link and change the password.

Menu Bar

The menu bar appears at the top of the main window.

The Home menu otion provides general information about FindSim interface.

The ExpModmap menu option provides the following sub options

a) ExpModmap: Experiment Model Mapping List

This list shows title of experiment performed, cells types used in experiment, type of experiment (Dose response, Time series, Bar Chart or Direct Parameters) and species used for generation of experimental data.

b) Non curated Models

The following models are not part of the curated branch of FindSim interface. While the syntax of the models has been verified, their semantics remains unchecked. We are doing our best to incorporate these models into the main curated branch as soon as possible.

c) Upload models

We can create, uplaod or download TSV file by using this option

In generation of new FindSim experiment this menu is used.

Create FindSim form

Title of experiment to be performed

Project: You can add name of project or disease for which model has been generated.

By using create FindSim Form; we will perform new FindSim experiment.

Experimental details

It consists of five parts

1. Experimental Metadata

Scientific data are generated by experiments or observations. In order to be interpreted, or even accessed, they must be accompanied by auxiliary information, ranging perhaps from the experimenter, year of publication, name of journal and place that the experiment was conducted to arcane calibration details. This auxiliary information constitutes the metadata for the experiment. Notably it is likely to be more extensive and less standardised.

Considerable effort must be made to capture all this information if the data are to be retained for posterity or made available to a wider community of users.

For example:

Transcriber: A Transcriber transliterates whenever possible the original model format into the tsv format for FindSim interface.

Organization: National Centre for Biological Sciences

Experiment Source: In this tab you have to select source of experimental data.

Experimental Source has three options a) Paper b) Inhouse c) Experiment

Citation ID: Foreign key identifier of the citation being extended; For Exaple: PMID: 10486198

Email: vinod.ugale@rediffmail.com

Authors: Eric C. Yuen and William C. Mobley

Journal: Experimental Neurology

2. Experimental Contexts

a) Experiment Type: This list shows title of experiment performed, cells types used in experiment, type of experiment (Dose response, Time series, Bar Chart or Direct Parameters), and species used for genration of experimental data and temperature ("C).

Include pathways: Pathways included in experiment

For example: BDNF,TrKB,PLCg

Notes: Any additonal information about experiment; For example: Replication of results of Pragati's paper

3. Model Mapping

A mapping model contains the information that defines the relationships between the source and target data that you specified, including experimental conditions, pathways invloved, items to delete, and annotations.

In model mapping you will find these different sections:

1. Model Source: It can be a local path, DOCQS (Database of Quantitative Cellular Signaling; https://doqcs.ncbs.res.in/) or Biomodels (BioModels Database is a repository of computational models of biological processes; https://www.ebi.ac.uk/biomodels/).

2. File Name: We have to select appropriate model which involves subset for experimental data.

In this section we have to fill auxiliary information constitutes the metadata for the experiment.

3. Scoring Formula: Add the formula as: abs((expt-sim)/(expt+sim+1e-9))

4. Solver: It includes different methods of numerical analysis such as Runge–Kutta methods; stochastic simulation; Exponential Euler Method.

5. Model Subset: Select the set of group for which experiment has been performed. For example: kinetics/BDNF,kinetics/TrKB,kinetics/PLC_g.

6. Model Lookup: Write a Pathways which is taken into consideration; For example: BDNF:BDNF/BDNF,pPLCg:PLC_g_p.

7. Parameter change: Select appropriate parameter such as Conclnit, nlint, Kf, Km, Kcat, Kb, etc. Add value for selected parameter.

Stimuli

Time units: Specify the time units as sec, milisec, microsec, nanosec, min, hrs, days.

Quantity units: Specify the quantity units as M, nM, pM, uM, mM, mV, uV, V, mA, uA, pA, number, ratio.

Field: Indicate the field as conc, n, conclnit, nlnit, inject, Vclamp.

Entities: Add the name of entity for which signalling pathway should be identified.

In data add time and value of entities.

Readouts: The concentration of substrates used for conversion into products

Time units: Specify the time units as sec, milisec, microsec, nanosec, min, hrs, days.

Quantity units: Specify the quantity units as M, nM, pM, uM, mM, mV, uV, V, mA, uA, pA, number, ratio.

UseXlog: Indicate whether Yes or No

UseYlog: Indicate whether Yes or No

useNormalization: Indicate whether Yes or No

Settle Time: Incubation period mentioned in research paper

Ratio Reference Time:

ratio Reference Dose:

Ratio reference entities:

Model Ratio reference entities:

Model Layout

Select the model containing pathways converting substrate to product to display in model layout.

For example: Displaying synSynthMar2.xml

FindSim Experiment Results

File:/var/www/findsimweb/Findsim_compute/media/uploads/synSynthMar2_UjmpwtW.xml

(Level 3, version 1)

Score = 0.150 for FindSim_Yuen_and_Mobley_replication_Fig_4_OQSEkdh.tsv Elapsed Time = 0.1 s time to convert 0.2167518138885498

ModelSource

DOQCS: The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It includes reaction schemes, concentrations, rate constants, as well as annotations on the models. The database provides a range of search, navigation, and comparison functions.

BioModels: It is a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats. Our mission is to provide the systems modelling community with reproducible, high-quality, freely-accessible models published in the scientific literature.

Runge–Kutta methods

In numerical analysis, the Runge–Kutta methods are a family of implicit and explicit iterative methods, which include the well-known routine called the Euler Method, used in temporal discretization for the approximate solutions of ordinary differential equations. These methods were developed around 1900 by the German mathematicians Carl Runge and Wilhelm Kutta.