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Mathematical Modeling in Biology: Integrating Experimental and Computational Approaches

Charles University Department of Biophysics and Physical Chemistry, Faculty of Pharmacy
✓ Fully Funded 🎓 Mathematics computational biology pharmacology translational research mathematical modeling in vitro biology virology scientific computing

Explore the integration of mathematical models with experimental in vitro biology data. Choose applications in virology or pharmacology while working in a collaborative and interdisciplinary environment at Charles University.

AI-generated overview

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Why This Research Matters

This research fosters improved mathematical descriptions of biological processes that can enhance drug development and virus control strategies. By linking high-resolution experimental data with advanced models, it contributes to more predictive and effective pharmaceutical sciences, benefiting public health and translational medicine.

theoretical ecology virus dynamics quantitative pharmacology bioinformatics

Project Description

Project Overview

This project focuses on developing and optimizing mathematical models to describe in vitro biological processes, integrating high-resolution experimental data. It is part of the New Technologies for Translational Research in Pharmaceutical Sciences (NETPHARM) project, driven by the goal to enhance understanding of biological systems relevant in pharmaceutical sciences, particularly in virology or pharmacology applications.

What You Will Do

The PhD candidate will work within the Mathematical Pharmacy research group at Charles University. Tasks include modeling biological data, programming in MATLAB, Python, or R, and collaborating closely with experimentalists to validate and refine models. The student will select a specific application field—either virology or pharmacology—tailoring the modeling approach accordingly.

Expected Outcomes

The research aims to deliver innovative mathematical frameworks that improve the interpretation of in vitro biological data, supporting advances in drug development and experimental design. Outcomes include published research, improved predictive models for biological phenomena, and interdisciplinary skills in applied mathematics and biology.

Why This Matters

Mathematical modeling is essential for translating complex biological data into actionable scientific knowledge. Enhancing model fidelity through experimental integration can accelerate pharmaceutical innovation, improve understanding of infections and drug mechanisms, and contribute to public health solutions.

Entry Requirements

MSc degree in Mathematics, Physics, Computer Science, or related quantitative fields; experience with scientific computing and programming (MATLAB, Python, R, or similar); B2 or higher level of English; Czech/Slovak language knowledge is an advantage; highly self-motivated, organized, and a team player.

How to Apply

Send motivation letter, CV, and 1-2 referees' contacts to Dr. Veronika Bernhauerová at bernhauve@faf.cuni.cz. Informal queries encouraged. Evaluation ongoing until 2026-06-30 with online interviews.

Eligibility

UK/Home
EU
International

Supervisor Profile

DV
Dr. Veronika Bernhauerová
Charles University, Department of Biophysics and Physical Chemistry, Faculty of Pharmacy
400 Citations
15 h-index
Google Scholar

Dr. Veronika Bernhauerová is a leading researcher at the Department of Biophysics and Physical Chemistry, Faculty of Pharmacy, Charles University. Her expertise spans theoretical ecology, virus dynamics, quantitative pharmacology, and bioinformatics. She applies mathematical modeling to understand viral infections and host-pathogen interactions with an emphasis on experimental integration and translational relevance.

Key Publications

2018 86 citations
Influenza virus infection model with density dependence supports biphasic viral decay
2021 70 citations
Defective viral genomes as therapeutic interfering particles against flavivirus infection in mammalian and mosquito hosts
2021 54 citations
Defective viral genomes from chikungunya virus are broad-spectrum antivirals and prevent virus dissemination in mosquitoes
2018 42 citations
Density‐dependent selection on mate search and evolution of Allee effects
2016 27 citations
Male‐killing bacteria as agents of insect pest control

Research Contributions

Developed models and experimental insights into defective viral genomes acting as therapeutic agents against flavivirus and chikungunya virus infections in mammals and mosquitoes.
These findings contribute to novel antiviral strategies potentially controlling virus spread and aiding disease management.
Investigated the effects of density dependence on viral decay and mate-search evolution including Allee effects.
Improves understanding of virus-host dynamics and ecological factors influencing population survival and pest control.
Studied male-killing bacteria as biological control agents for insect pests.
Offers potential environmentally friendly pest control approaches reducing reliance on chemical pesticides.

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1363+ citations · h22
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