Statistiskt seminarium

Abstrakt finns enbart på engelska: Ordinary differential equations (ODEs) are commonly used to model dynamic processes such as biological networks. ODE models often contain unknown parameters that must be estimated from data. From a statistical viewpoint, this estimation is typically performed by computing a maximum likelihood estimate, which boils down to solving a nonlinear optimization problem. In simple cases, the likelihood function can be easily coded using existing libraries in programming languages like Python and Julia. However, for more complex scenarios—such as when the model includes events, data is collected under various simulation 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 computationally intensive, potentially taking hours to days, and the choice of ODE solver and gradient computation methods can drastically affect runtime.

In this talk, I will discuss our software package PEtab.jl, a Julia package for setting up parameter estimation problems for dynamic models. I will cover how PEtab.jl simplifies parameter estimation workflows and present extensive benchmark results on how the choice of gradient methods and ODE solvers affects runtime. Lastly, I will discuss how mechanistic models can be complemented with data-driven neural-network models to address the shortcomings of each model type.