Juliacon 2024

Profile Likelihood-based Practical Identifiability in Julia
07-12, 11:40–11:50 (Europe/Amsterdam), While Loop (4.2)

Practical identifiabilty based on Profile Likelihood is a crucial step in determining how well the parameters of a mechanistic model can be recovered from the experimental data. What packages are available to address this problem in Julia and what does Julia SciML community lack? How Profile likelihood-based methods can be applied to predictability analysis? The talk will cover the packages LikelihoodProfiler.jl, ProfileLikelihood.jl and the desirable improvements to Julia SciML ecosystem.

Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question of models’ reliability: how accurately can the parameters of the model be recovered from the available experimental data. Noisy or incomplete data result in uncertainty in parameters estimations typically described by confidence intervals and confidence regions. The methods based on profiling the likelihood function are among the most reliable and widely used approaches to estimate parameters’ confidence intervals and state their practical identifiability. The application of Profile Likelihood-based methods can also be extended to predictability analysis and confidence bands estimation. With the growing interest of Systems Biology community to Julia and SciML package ecosystem it is fair to ask what methods are available to address the problem of practical identifiability and predictability in Julia. The talk will present the problem of practical identifiability and propose an overview of available packages (LikelihoodProfiler.jl and ProfileLikelihood.jl) and the desirable improvements to Julia SciML ecosystem.

Ivan earned his Master’s degree in Mechanical Engineering from the Department of Mathematics and Mechanics of Lomonosov Moscow State University. Ivan started his career at IBM East Europe/Asia providing customers with technical support and «proof of concept» implementation of IBM software products. In 2017, Ivan joined InSysBio as mathematician and software developer, where he continues to work until present as senior software developer. Ivan participates in the development of key InSysBio software tools for simulations and analysis of QSP models, namely Julia-based package for simulations and parameters estimation HetaSimulator.jl, practical identifiability toolkit LikelihoodProfiler.jl, packages for Virtual Patients generation, etc. Beyond software development, Ivan is engaged in the enhancement of mathematical and computational methods for QSP modeling.

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