07-28, 17:00–17:10 (UTC), Green
Mathematical models are crucial to build and predict the behaviour of new biological systems. However, selecting between plausible model candidates or estimate parameters is an arduous job, especially considering the different informative content of experiments. BOMBs.jl is a package to automate model simulations, pseudo-data generation, maximum likelihood estimation and Bayesian inference of parameters (Stan and Turing.jl), and design optimal experiments for model selection and inference.
We designed BOMBs.jl intending to contribute to the widespread of mathematical models in Biological sciences. Users only need basic Julia knowledge to use the package. The only requirement is to know how dictionaries work. Users can define in the contents of a dictionary the set of ordinary differential equations (and some other model information), and BOMBs.jl will automatically generate all the necessary scripts to simulate the model (including models with external time-varying inputs) and estimate parameters (MLE). The package also generates all the scripts required to perform Bayesian inference using Stan or Turing.jl, leaving as much freedom as possible in prior definitions. BOMBs will also generate any necessary scripts to perform optimal experimental design for model selection (drive pairs of competing model simulations as far as possible) and model inference (using model predictions uncertainty), aiming at reducing the time and resources allocated to in vivo experiments. The package is documented, with functions generating the dictionary structures for the user, complementary functions explaining what should be the contents and structures of the dictionaries, a document including a brief description of each function in the package and a set of Jupyter notebooks showing how to use all the package functionalities.
The Jupyter Notebook of this talk is included in the GitHub repository of the package at https://github.com/csynbiosysIBioEUoE/BOMBs.jl/blob/main/Examples/JuliaCon2022Notebook.ipynb
I am a Bioengineering PhD student at the University of Edinburgh working on optimal experimental design for automated model calibration and selection, both computationally and experimentaly. Coming from a pure experimental background (Microbiology), I discovered how powerfull modeling can be in not just predicting, but designing new biological systems, hence my transition to a hybrid type of work. I discovered Julia a couple of years ago, looking for faster environments to do my computational research (mostly Bayesian based), and now I completely shifted all my work to it!