PetriDish

PetriDish

PetriDish is an open-source platform for automating biology experiments. It provides software and hardware tools for designing, running, and analyzing experiments involving microfluidics, microscopy, and image analysis.
PetriDish image
opensource biology experiments microfluidics microscopy image-analysis

PetriDish: Open-Source Biology Experiment Automation Platform

Automate biology experiments with PetriDish, a software platform for designing, running, and analyzing experiments involving microfluidics, microscopy, and image analysis.

What is PetriDish?

PetriDish is an open-source platform aimed at automating biology experiments. It includes both software and hardware components to help researchers design, execute, and analyze a wide range of life science experiments.

On the hardware side, PetriDish leverages microfluidics and optics to build compact devices for handling liquids and imaging cells and organisms. These devices automate tedious and error-prone manual work, allowing experiments to run 24/7.

The PetriDish software includes tools for designing microfluidic chips, controlling fluid flows during an experiment, capturing microscopy images and videos, and analyzing the resulting data. The platform is optimized for common experimental techniques in cell biology, microbiology, and synthetic biology.

All of the PetriDish software and hardware designs are open source, allowing customization and extension by end users. Researchers can even design their own reactorchips for specific experimental needs. The goal of PetriDish is to make automation accessible to labs without engineering expertise, facilitating reproducible and scalable science.

PetriDish Features

Features

  1. Automated experiment design and execution
  2. Microfluidics and microscopy integration
  3. Image analysis and data visualization tools
  4. Customizable hardware and software modules
  5. Collaborative experiment management

Pricing

  • Open Source

Pros

Open-source and customizable

Streamlines complex biology experiments

Reduces manual labor and human error

Facilitates reproducible and scalable research

Enables remote experiment monitoring and control

Cons

Steep learning curve for non-technical users

Requires investment in specialized hardware

Limited commercial support and documentation

Potential compatibility issues with legacy systems

Ongoing maintenance and updates required


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