BioCaL
iOS Universel / Education
Experimental research in molecular biology requires fundamental techniques such as restriction enzyme digestion and DNA ligation, which are essential for recombinant DNA technology and widely practiced in laboratories worldwide. Mastering these techniques is crucial for progress in biological research.
Our app simplifies these processes by providing precise volumes for each component required in restriction digestion and ligation reactions. Users no longer need to calculate DNA volumes manually; instead, they input the desired DNA concentration, and the app calculates the necessary volumes for your reactions. The app then conveniently provides these volumes, so you can quickly set up your reactions ready for incubation at the appropriate temperatures in Eppendorf tubes.
Additionally, our app offers the necessary DNA volumes for DNA assembly reactions, accommodating up to six different DNA fragment assemblies when constructing and using two or more DNA molecules.
This user-friendly app ensures seamless operation, allowing instant use without the need for detailed instructions. It also guides users in sampling volumes to normalize mass and molar concentration across tubes with containing different DNA concentrations and sizes. Furthermore, it provides options for calculating various physical quantities of DNA and RNA of interest.
Please note that all calculations involving DNA experiments are performed on double-stranded DNA molecules in BioCaL.
The app also includes features for normalizing cell optical density to adjust the number of cells taken from different samples, as well as evaluating plasmid DNA copy number per cell and cosmid packaging efficiency, facilitating thorough analysis and efficient experiments.
Quoi de neuf dans la dernière version ?
1. Added lag time estimation to predict when cells will begin exponential growth after inoculation in the bacterial growth timer ODAlarm.
2. Introduced a growth curve generator in the growth tracking option for visualising culture progression.
3. Added a promoter classification feature to identify whether an input sequence is a promoter or non-promoter.
4. Added a ρ-independent terminator classification feature to identify whether an input sequence is a terminator or non-terminator.
