Intro Sentence:
UCLA researchers in the Department of Molecular and Medical Pharmacology have developed a programming language to automate biological experiments.
Background:
Manual labor is a common bottleneck in biological sciences, with automation technology still being unobtainable and impractical for most scientists in biomedical research. There are cost reasons as well as other issues associated with automation: One of these main issues is the inflexibility of automation software that uses graphical user interfaces (GUIs) to control and coordinate equipment for executing experiments. Conventional automation software lacks features such as fine control on a per-plate or per-well level. This limitation results in the inability to change experimental parameters quickly and efficiently when an experimenter tries to intervene. Rapid changes of condition in response to experimental data normally necessitates manual labor, with cell culture being a prime example. In addition, the data generated typically reside outside of the software, which makes it difficult to integrate generated data into the flow of experiments that can drive experiments to completion or enable optimization workflows. There is a need for a software solution that automates minute changes in biological experiments to reduce the need for cumbersome manual exertion.
Innovation:
UCLA researchers have designed a command line interpreter/wrapper that takes direct computer commands pertaining to experiments and translates them into actionable commands for automation equipment. This technology allows the experimenter to extract, visualize, analyze the results, and optimize experimental parameters without having to be in the laboratory. This system iterates over active labware and generates images, reports data into an associated SQL database from which results can be reported back to the scientist. The database can be directly queried, and experiments can be adjusted by writing directly into the records for each labware in the SQL database. Experiments can also be directed based on the rules set by the experimenter or modified with artificial intelligence algorithms, thereby freeing up more time for experimenters to work on other tasks. The backbone of this system is Python, thus allowing for seamless integration of machine learning algorithms, automation equipment and SQL database. This approach also lends itself to an open-source approach with commercialization opportunities for commercial entities.
Potential Applications:
- Lab work optimization
- Automation
- Biomedical research
Advantages:
- Opportunities to incorporate artificial intelligence
- Reduce manual labor and time present in lab
Development-To-Date:
Initial Conception at December 2022; initial prototyping in progress.
Reference:
UCLA Case No. 2024-049
Lead Inventor:
Robert Damoiseaux