Systems Biology in ModelingToolkit
2021-07-29, 12:40–12:50 (UTC), Purple

Systems Biology Markup Language (SBML) and CellML are extensible markup languages (XML) widely used throughout the biological modeling community. In this talk we showcase new packages (SBML.jl and CellMLToolkit.jl) for importing models from these languages to the ModelingToolkit.jl format for the full suite of SciML tools to simulate and analyze!

Back in my day, systems biologists used MATLAB and Python for RK4. But in 2021 we can now run downhill both ways and make our biological models zoom with CellMLToolkit.jl and SBMLToolkit.jl in Julia! We will demonstrate importing CellML and SBML models into ModelingToolkit and how we get these model analysis and simulation tools "for free" in an acausal symbolic component model. We will show a few examples of how (biological) researchers may benefit from the broader SciML ecosystem, including parameter estimation and global sensitivity analysis. Short comparisons with de facto SBML and CellML modeling programs will be drawn to demonstrate how a biologists’ workflow may differ with SciML. The audience will leave with a firm understanding of how the Julia simulation environments will lead the next generation of biological modeling and simulation.

I am interested in the intersection of mathematics and physiology. I am a computer science student at the University of Chicago on a gap year. I am working at Julia Computing on building surrogates of systems biological models for JuliaSim.

Shahriar Iravanian, MD, MSE, is a practicing cardiac electrophysiologist and biophysical researcher with an interest in non-linear dynamics and high-performance computing with a focus on the modeling of cardiac arrhythmias. He received a Master of Science in computer science from Johns Hopkins University and finished his cardiology and electrophysiology training at the Emory University in 2011.

I am a systems biology PhD student with background in molecular biology. My doctoral research at the University of Oxford aims at improving our mechanistic understanding of cell cycle dynamics with mathematical models. This involves (1) casting chemical reaction networks into executable models, (2) obtaining highly multiplexed snapshot measurements of cell cycle regulators, from which we reconstruct their time-courses, and (3) developing parameter optimisation and model reduction/selection toolboxes.