Biology subjects

Menkovski, V.

Publications and source records attributed to Menkovski, V..

2 recordsLinked to original sources

bioRxiv2025

Micellangelo: a Generative Model of Cell-Topography Interactions

Understanding how materials influence cell behavior is central to biomaterial engineering, yet experimental approaches are limited by throughput and complexity. Here, we present Micellangelo, a generative AI model that simulates high-resolution fluorescence images of cells cultured on micro-topographical surfaces. By conditioning on surface topographies, Micellangelo generates realistic cell morphologies, enabling in silico exploration of cell-material interactions. We fabricated a dataset of human dermal fibroblasts stained for DNA, F-actin, and the mechanosensitive transcription factor YAP across ten distinct topographies. Micellangelo was trained using the flow matching framework, and demonstrated strong visual and quantitative alignment with real microscopy data in terms of morphology, intensity patterns, and mechanotransductive features. Beyond image generation, we demonstrate two proof-of-concept in silico experiments: tuning the strength of topographical conditioning and perturbing subcellular structures to assess phenotypic consequences. The model captures both topography-induced morphological trends and structure-function relationships such as actin-YAP coupling. These results suggest that Micellangelo functions as a digital twin of experimental cell imaging, taking a step towards a scalable platform for hypothesis generation, virtual screening, and design of biomaterial interfaces. This work bridges the fields of biomaterials and generative modeling, and introduces a generalizable framework for conducting biologically meaningful in silico experiments.

bioengineering↗

bioRxiv2019

Detection of pre-microRNA with Convolutional Neural Networks

MicroRNAs (miRNAs) are small non-coding RNA sequences that have been implicated in many physiological processes and diseases. The experimental discovery of miRNAs is complicated because both miRNAs and their targets need to be expressed for the confirmation of functional interactions, but expression is under spatiotemporal control. This has motivated the development of computational methods for miRNA detection. This typically involves feature design by domain experts followed by machine learning. While handcrafted features can encode domain knowledge, feature engineering is a time-consuming task. Additionally, some of the currently most successful features for pre-miRNA detection, such as p-value based ones, require comparably large computations. In contrast, advances of representation learning methods such as deep learning can discover relevant features directly from data. Here, we propose a method that uses domain knowledge to create an efficient graphical representation of pre-miRNAs, encoding sequence, structure, and implicitly some thermodynamic information. A suitable convolutional neural network architecture for pre-miRNA detection was used to train a model. This model achieves state-of-the-art performance on all previously used datasets. Additionally, computations succeed in real time thereby overcoming current speed limitations. Finally, our strategy promises future interpretability of the trained models and in turn novel biological interpretations of pre-miRNA characteristics.

bioinformatics↗