Constraint-based reconstruction and analysis (COBRA) refers to a vast collection of methods that build and scrutinise large metabolic networks, traditionally using the methodology of linear optimization. Many current analysis methods work with models that are personalised or customised to reflect genomic, proteomic and other measurements. At the same time, there is a natural need to simulate more complex phenomena that emerge from combinations of many individual models, such as multi-organ and multi-organism networks. The complexity of the computations that need to be organised to run such simulations naturally grows, up to a point where the analysis may become infeasible. 

In this course we will introduce COBREXA.jl, a toolkit for working with large constraint-based metabolic models and running very large parallel analysis tasks on these models. Its main purpose is to make the COBRA methods scale to problem sizes that require the use of large computer clusters and high performance computing (HPC). Visit COBREXA.jl website to find more information and guides.

The course will combine lectures and hands-on exercises using several new design patterns implemented in COBREXA.jl to organise many different model combinations, constraints, modifications and personalizations in a systematic way, allowing short and concise implementations of extremely complex, huge-scale analyses.