Lenka Backová is a PhD student at the University of the Basque Country (UPV/EHU), affiliated with the Biofisika Institute, under the supervision of Dr. Ignacio Arganda-Carreras and Dr. Jérôme Solon. She holds a Master’s degree in Bioinformatics (summa cum laude), as well as Bachelor’s degrees in Bioinformatics and General Physics, all from Charles University in Prague.

Before starting her PhD, she worked as a bioimage analyst at the Imaging Methods Core Facility (BIOCEV), where she developed custom image analysis pipelines for multidimensional microscopy data, trained neural networks for segmentation, and supported users with Fiji, Python, and commercial software such as Imaris and Huygens.

Her current research focuses on the deep learning-based analysis and biophysical modeling of cell behavior during wound healing in Drosophila embryos. She studies how chromatin compaction in cells near the wound affects actomyosin accumulation and tissue repair dynamics. For this, she developed an advanced segmentation and tracking pipeline to quantify chromatin behavior and nuclear motion across experimental conditions including cytoskeletal, mechanical, and calcium perturbations.

In parallel, she is developing a modified Cellular Potts Model to simulate wound closure kinetics and tissue deformation under varying contractility and elasticity. She combines experimental data with machine learning approaches, including video prediction networks, to infer model parameters and improve biological interpretability.

Now in the final year of her PhD, Lenka aims to integrate experimental quantifications with biophysical modeling to uncover the mechanistic underpinnings of epithelial wound healing. After defending her thesis, she plans to pursue a postdoctoral position in computational biology with a focus on quantitative bioimage analysis and model-driven biology, continuing her commitment to open-source tools and interdisciplinary research.