Paul Lang works in developing comprehensive models of biochemical reaction systems and supporting software tools. He obtained a BSc degree in Molecular Biology at the University of Graz (Austria) and an MSc degree in Molecular Health Sciences at ETH Zurich (Switzerland). He then switched from experimental to computational research. During his PhD at the University of Oxford (UK), Paul developed a rule-based cell cycle model that explains location and dynamics of 16 observables in RPE1 cells. As visiting scholar at the Icahn School of Medicine at Mount Sinai in New York (USA), he co-developed BpForms, a toolkit for concretely describing non- canonical polymers to enable the construction of whole-cell models. Paul also co-developed SBMLToolkit.jl, which imports SBML models into the SciML ecosystem. Currently, Paul works for JuliaHub on developing a parameter optimization tool for quantitative systems pharmacology. He also helps clients to translate their models to ModelingToolkit.jl ODESystems.
The Scientific Machine Learning (SciML) ecosystem is rapidly gaining momentum within the field of systems biology. With this birds of feather discussion we want to bring the international community of systems biology tool developers and users at one table to (a) brainstorm promising routes for future developments, and (b) facilitate collaborative projects.
Julia has had the most developed ecosystem for differential equation modeling in simulation through the SciML organization for a while. Here we present a collection of talks by computational systems biologists in the community. The focus of the symposium will be to look at how SciML tools are being used in systems biology, how they can improve, and how we can take steps to increase collaboration throughout industry and academia.