BioCheck (bio-compose-server): A Biological Simulation Verification Service

This service utilizes separate containers for REST API management, job processing, and datastorage with MongoDB, ensuring scalable and robust performance.

**The REST API can be accessed via Swagger UI here: https://biochecknet.biosimulations.org/docs

For Developers:

This application (bio_check) uses a microservices architecture which presents the following libraries:

• api: This library handles all requests including saving uploaded files, pending job creation, fetching results, and contains the user-facing endpoints.
• storage: This library handles MongoDB configs as well as bucket-like storages for uploaded files.
• worker: This library handles all job processing tasks for verification services such as job status adjustment, job retrieval, and comparison execution.

The simulators used by this application consist of multiple python language bindings of C/C++ libraries. Given this fact, is it helpful to be aware of

the dependency network required by each simulator. See the following documentation for simulators used in this application:

• AMICI
• COPASI(basico)
• PySCes
• Tellurium
• Simulator-specific implementations of the Biosimulators-Utils interface
• Smoldyn
• (Coming soon:) ReaDDy

Dependency management scopes are handled as follows:

Locally/Dev:

• Python poetry via pyproject.toml - the most stable/reproducible method, yet the farthest from what is actually happening in the service containers as they use conda.
• Anaconda via environment.yml - the closest to local development at root level which micics what actually happens in the containers (conda deps tend to break more frequently than poetry.)

Remotely in microservice containers:

• Remote microservice container management is handled by conda via environment.yml files for the respective containers.

The installation process is outlined as follows:

1. git clone https://github.com/biosimulators/bio-check.git

2. cd assets

3. touch .env_dev

4. Enter the following fields into the .env_dev file:

    MONGO_URI=<uri of your mongo instance. In this case we use the standard mongodb image with the app name bio-check>
    GOOGLE_APPLICATION_CREDENTIALS=<path to your gcloud credentials .json file. Contact us for access>
    BUCKET_NAME=bio-check-requests-1  # name of the bucket used in this app
   

5. cd ..

6. Pull and run the latest version of Mongo from the Docker Hub. (docker run -d -it mongo:latest or similar.)

7. Run one of the following commands based on your preference:

    conda env create -n bio-compose-server-dev -f environment.yml
    
    # OR
    poetry env use 3.10 && poetry install
   

Notes:

• This application currently uses MongoDB as the database store in which jobs are read/written. Database access is given to both the api and worker libraries. Such database access is executed/implemented with the use of a Supervisor singleton.