Karlstad Applied Analysis Seminar (KAAS)

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Future seminars:

Talk-148

When: 24 March 2025, 13:30-15:00
What: Quantitative Adverse Outcome Pathway Modeling: An ODE-Based Framework Applied to Platinum-Induced Nephrotoxicity
Who: Filippo Di Tillio, Division of Cell Systems and Drug Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
Where: 3D415 online: https://kau-se.zoom.us/j/61616693592
Abstract:

The Adverse Outcome Pathway (AOP) framework describes biological mechanisms connecting molecular initiating events (MIEs) through intermediate key events (KEs) to adverse outcomes (AOs). Despite its broad adoption, establishing a robust quantitative AOP (qAOP) modeling framework to enable accurate predictive risk assessment remains challenging.

Here, we introduce an ordinary differential equation (ODE)-based qAOP framework designed to quantitatively describe KEs and their relationships over time. Bayesian inference and cross-validation techniques are employed for parameter estimation, model calibration, and validation.

To demonstrate this framework, we focus on platinum-induced nephrotoxicity, a significant limitation in chemotherapy. Two complementary ODE-based qAOP models were developed: one utilizing newly generated in vitro data from RPTEC/TERT1 cells and another built upon published in vivo rat kidney data exposed to cisplatin. Analysis of these models revealed distinct immune system dynamics, highlighting how rapid clearance of necrotic cells initially protects against immediate kidney damage, whereas sustained inflammation contributes to cumulative nephrotoxicity. Moreover, we perform quantitative in vitro to in vivo extrapolation (QIVIVE) to link the two models. With this approach, in vivo adverse outcome predictions can be made in the future not only for platinum-based compounds but also for the safety assessment of other chemicals and drugs, reducing the need for animal testing.

Talk-149

When: 25 March 2025, 10:30-11:15
What: Practical Synthetic Data Generation for Statistical Inference
Who: Lotte Pater, Dienst Uitvoering Onderwijs, Ministry of Education, Culture and Science, and University of Groningen, NL 
Where: online: https://kau-se.zoom.us/j/61616693592
Abstract:

In the present information age governments, universities, hospitals and many others collect large amounts of personal data. These data can be very useful for research and decision making, but often is not shared due to legal and ethical privacy implications. Synthetic data is a technique used to combine data usage with data privacy. A synthetic version of a dataset is created, ideally with the same statistical distributions as the real dataset (so that it is still useful) but without any personal information (so that there are no privacy risks). I work with an interdisciplinary team in the Dutch government to implement synthetic data operations. In this talk I will tackle two questions:

1. How can you generate potentially useful synthetic data? We use Classification and Regression Trees (CART) as implemented in the R package synthpop. This technique consistently comes out as generating the most useful synthetic data  (i.e. similar to the real data) in the comparative literature. I’ll explain what makes this technique tick and contrast it with the more popular General Adversional Networks (GAN) class of models. I will also compare it with the conceptually similar technique by Nicklas Jävergård et al. (2024).
2. How can you make potentially useful synthetic data actually useful? Generating synthetic data might seem like mainly a statistical problem. But in actually applying it, we also encountered legal, ethical, software development, governance, political and communication challenges. In many cases, combining mathematics with other skills was necessary to solve a problem. I will highlight some general experiences and talk about how we set down a privacy approach for synthetic data – a problem that combines mathematics, law, ethics and communication. 

Talk-150

When: T.B.D
What: Nonsmooth mechanics of fiber network microstructures
Who: Mykola Tkachuk, Karlstad University, Karlstad, Sweden.
Where: T.B.D and online: https://kau-se.zoom.us/j/61616693592
Abstract:

We wish to present the case that many problems in mechanics can be posed as mathematical programming.

Equilibrium of various structures can be determined via minimization of the potential energy or other suitable mechanical functional. We have developed a new discrete network model [2] for nonwoven materials.

The rate-independent sliding and the quasi-static equilibrium loading of such network structure are constituted within the theory of standard dissipative systems. A minimum principle for incremental potential is formulated with respect to the displacement-based variables: nodal coordinates, segment end-to-end vectors, segment lengths and incremental fiber slidings. It takes the form of second-order cone programming (SOCP) similarly to the case of elastic cable networks. A pure complementary energy principle is derived as the dual formulation in terms of stress-like variables: nodal reactions, fiber force vectors, axial forces and friction forces.

The model capture large irreversible deformations of nonwoven materials. The damage mechanism is implemented in the form of fiber pull-out. The network becomes disjoint as more and more sections of free tails are pulled through the end knots.