FLAME

Introduction

Currently genetic circuits in synthetic biology are complicated thus are difficult for biologists to design. In our project, we developed the Framework-based Layout And Metacircuit design Engine (FLAME), to simplify the whole design procedure by characterizing numerous published genetic circuits and abstract these circuits into 3 parts: inputs, outputs and logic relationships. A novel simulation module using ordinary differential equations is integrated into our software. Our simulation results were precisely validated by results from published literature and our self-designed wetlab experiments. In conclusion, the combination of a simplified design procedure, a new and effective simulation module and wetlab validation makes complex biological circuits more accessible to synthetic biologists.

Installation

Online version

You can just use FLAME online!

Prebuilt package

If you prefer to run the software on your computer, you can download the prebuilt package of FLAME on the releases page.

Manually build & install

If you prefer the hacker-way, you can build FLAME manually. You can build FLAME on Linux, MacOS and Windows with a proper build toolchain.

1. Build dependencies (the development packages of them are required. E.g. boost-devel, libev-devel and python-devel on openSUSE)

   • gcc-c++ 4.7 +
   • boost (only boost/numeric/odeint is required)
   • libev
   • Python 2.7

   And the following Python packages are required.

   • Cython
   • Flask
   • SQLAlchemy
   • Flask-SQLAlchemy
   • gevent
   • nose (for unit testing)
   • Flask-Testing (for unit testing)
   • coverage (for unit testing)

   You can install the Python packages using pip.

   pip install -r requirements.txt

2. Build

   python setup.py build_ext -i
   python init_db.py
   python runtests.py -v  # optional

3. Run

   python run.py

Deployment on your own server

You can deploy FLAME on you own server (VPS, AWS EC2 instance, etc). FLAME is just a Python WSGI application and the WSGI application object is server.app. You can deploy it with some common WSGI containers. In fact, run.py is an example of deploying FLAME with gevent.

We also prepare a Dockerfile for those who favour deploying with Docker, and Dockerrun.aws.json for those who want to deploy FLAME on AWS Elastic Beanstalk.