12–17 Jul 2026
University of Graz
Europe/Vienna timezone

Bridging Reaction–Diffusion Models and Empirical Data: Reparameterization for Transporter Kinetics

16 Jul 2026, 18:30
2h
University of Graz

University of Graz

Poster Numerical, Computational, and Data-Driven Methods Poster Presentations

Speaker

Se Jun Ahn (Graduate School of Data Science, KAIST, Daejeon, Republic of Korea)

Description

Transporter-mediated drug delivery is a crucial factor in pharmacokinetic and pharmacodynamic (PK/PD) studies. Transporter kinetics are typically evaluated using the Michaelis–Menten (MM) model, which estimates the maximum reaction rate($V_{\max}$) and the Michaelis constant($K_M$). However, the MM model assumes a well-mixed environment, whereas transporters operate in localized spatial settings, which can lead to estimation inaccuracies. While previous studies addressed this spatial discrepancy, they introduced an additional spatial parameter that is difficult to determine experimentally. To overcome this practical limitation, we reformulated the reaction–diffusion equations in terms of more accessible parameters. Furthermore, we rigorously evaluated the accuracy and practical identifiability of these transformed parameters with real experimental data. By transitioning to practically measurable variables, this study bridges the gap between complex spatial models and empirical laboratory data, offering a robust and experimentally interpretable framework for analyzing transporter kinetics.

Authors

Hyeong Jun Jang (Graduate School of AI for Math, KAIST, Daejeon, Republic of Korea) Se Jun Ahn (Graduate School of Data Science, KAIST, Daejeon, Republic of Korea)

Co-authors

Jae Kyoung Kim (Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea) Junghyun Lee (Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea) Suein Choi (Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea)

Presentation materials

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