Biology subjects

Kuba, S.

Publications and source records attributed to Kuba, S..

3 recordsLinked to original sources

bioRxiv2026

Mechanistic interpretation of biological tissue growth experiments with a computational model

The growth rates of biological tissues are influenced by the existing substrate geometry, mechanobiological processes and the interplay between them. Disentangling the contributions of geometry and mechanobiology experimentally is challenging, as mechanical properties are difficult to measure and tissue samples provide only static snapshot in time. However, the composition of a tissue preserves cues of the dynamic processes that shaped its architecture. In this work, we present a computational model of tissue growth that captures aspects of the interplay between geometry, mechanics, and stochastic biological processes, which we use to generate synthetic tissue compositions directly comparable with experimental samples. This framework enables quantitative analysis of tissue morphology, inference of underlying growth mechanisms, estimation of dynamic rates from single-time-point data, and investigation of how stochasticity contributes to emergent growth patterns. We demonstrate the applicability of the model to simulate the growth of different tissue types by applying this framework to two distinct tissue growth scenarios: (i) tissue grown within 3D-printed porous scaffolds, and (ii) bone formation in cortical pores.

bioengineering↗

bioRxiv2026

A mathematical model of curvature controlled tissue growth incorporating mechanical cell interactions

Many biological tissues grow at rates that depend strongly on the local geometry of the tissue interface, yet the cellular mechanisms underlying this dependence remain poorly understood. In this work, we develop a two-dimensional mathematical model of tissue growth to investigate how curvature-dependent growth emerges from the complex interactions between cell-scale crowding and mechanical interactions. The tissue interface is represented as a confluent layer of mechanically interacting cells that simultaneously produce new tissue. We formally derive a continuum limit of this discrete model, obtaining a system of partial differential equations governing the coupled evolution of cell density and tissue geometry. Although curvature is not explicitly represented in the discrete model, curvature-dependent tissue growth emerges naturally in the continuum limit through geometric crowding associated with tissue production. The continuum equations also establish a direct relationship between cellular mechanical properties and the effective diffusivity governing mechanical relaxation at the tissue scale. Numerical simulations illustrate that the competition between tissue production and mechanical relaxation can result in a variety of tissue evolutions, including tissue interface smoothing consistent with curvature-controlled in vitro growth in porous scaffolds and bone formation. Finally, the model predicts that the time required for scaffold pore closure scales with pore size but also depends on a pore shape factor, extending previous empirical observations of linear scaling with size. More broadly, this work provides an analytical framework linking cell-scale mechanics to tissue-scale growth and geometry.

biophysics↗

bioRxiv2022

Analysis of RNA polyadenylation in healthy and osteoarthritic human articular cartilage

An important transcript structural element is its 3 polyadenylated (polyA) tail, which defines the 3 boundary of the transcripts genetic information and is necessary for transcript stability. The position of the polyA tail can vary, with multiple alternatively polyadenylated (APA) transcripts existing for a single gene. This can lead to different length transcripts which can vary in their 3 regulatory domains and even by inclusion or exclusion of protein-coding introns. The distribution of polyA tail location on articular chondrocyte transcripts has not been examined before and this study aimed to be the first to define polyadenylation events in human chondrocytes using age-matched healthy and osteoarthritic knee articular cartilage samples. Total RNA was isolated from frozen tissue samples and analysed using the QuantSeqReverse 3 RNA Sequencing approach, where each read runs 3 to 5 from within the polyA tail into the transcript and will contains a distinct polyA site. Initial analysis of differential expression of overall transcript abundance identified by the reads showed significant disruption to transcript levels when healthy samples were compared to osteoarthritic ones. As we expected, differentially regulated genes were enriched with functionalities that were strongly associated with joint pathology. As part of this analysis, we also identified a substantial number of differentially expressed long non-coding RNAs that had not been linked to osteoarthritis before. Subsequent examination of polyA site data allowed us to deifne the extent of site usage across all the samples. This included identification of chondrocyte genes that exhibited the greatest amount polyA site variation. When comparing healthy and osteoarthritic samples, we found that differential use of polyadenylation sites was modest. However, of the small number of genes affected, there was clear potential for the change in polyadenylation site usage elicited by pathology to have functional relevance. We examined two genes, OSMR and KMT2A, in more detail, defining how APA affects transcript turnover and then, in the case of OSMR, identifying that APA is sensitive to inflammatory cytokine stimulation. Overall, we have characterised the polyadenylation landscape of human knee articular chondrocytes but can conclude that osteoarthritis does not elicit a widespread change in their polyadenylation site usage. This finding differentiates knee osteoarthritis from pathologies such as cancer where APA is more commonly observed.

molecular biology↗