BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sebastian Persson (University of Gothenburg) DTSTART:20241127T121500Z DTEND:20241127T130000Z DTSTAMP:20260422T155052Z UID:gbgstats/72 DESCRIPTION:Title: PEtab.jl - Efficient parameter estimation for dynamic models\nby Seb astian Persson (University of Gothenburg) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nOrdinary differential equa tions (ODEs) are commonly used to model dynamic processes such as biologic al networks. ODE models often contain unknown parameters that must be esti mated from data. From a statistical viewpoint\, this estimation is typical ly performed by computing a maximum likelihood estimate\, which boils down to solving a nonlinear optimization problem. In simple cases\, the likeli hood function can be easily coded using existing libraries in programming languages like Python and Julia. However\, for more complex scenarios—su ch as when the model includes events\, data is collected under various sim ulation conditions\, or the model should be at a steady state at time zero —correctly coding a likelihood function becomes time-consuming and error -prone. Moreover\, numerically fitting an ODE model to data can be computa tionally intensive\, potentially taking hours to days\, and the choice of ODE solver and gradient computation methods can drastically affect runtime . \n\n \nIn this talk\, I will discuss our software package PEtab.jl\, a J ulia package for setting up parameter estimation problems for dynamic mode ls. I will cover how PEtab.jl simplifies parameter estimation workflows an d present extensive benchmark results on how the choice of gradient method s and ODE solvers affects runtime. Lastly\, I will discuss how mechanistic models can be complemented with data-driven neural-network models to addr ess the shortcomings of each model type.\n LOCATION:https://researchseminars.org/talk/gbgstats/72/ END:VEVENT END:VCALENDAR