arXiv subjects

Erik Blom

Publications and source records attributed to Erik Blom.

4 recordsLinked to original sources

arXiv2026

Towards Data-Driven Modeling of Cell Cycle and Wound Closure Processes

Effective wound repair treatments rely on a clear picture of how cell proliferation and migration are coordinated during tissue restoration. Fibroblasts are key contributors to tissue restoration in the dermis, and modern imaging tools allow their cell-cycle progression to be observed directly, enabling comparison between experiments and computational models. Here we investigate how different stages of the cell cycle influence fibroblast-driven wound closure using the Discrete Laplacian Cell Mechanics (DLCM) framework driven by time-lapse microscopy data. \textit{In vitro} assays provide cell positions, migration behaviour, and cycle-stage information, and we show that incorporating proliferation, migration, and cell cycle arrest allows the computational model to reproduce the essential experimental trends. The results reveal that arrest in the G1 phase notably impacts the cell cycle dynamics and that the initial spatial arrangement of cycle states significantly affects wound closure. By linking single-cell cycle dynamics with emergent tissue behaviour this work establishes a quantitative approach for exploring how intracellular processes shape repair processes. More broadly, it demonstrates the value of integrating high-resolution data with cell-based mechanical models and provides a foundation for systematic \textit{in silico} evaluation of therapeutic interventions.

q-bio.CB↗

arXiv2025

DLCM: a versatile multi-level solver for heterogeneous multicellular systems

Computational modeling of multicellular systems may aid in untangling cellular dynamics and emergent properties of biological cell populations. A key challenge is to balance the level of model detail and the computational efficiency, while using physically interpretable parameters to facilitate meaningful comparisons with biological data. For this purpose, we present the DLCM-solver (discrete Laplacian cell mechanics), a flexible and efficient computational solver for spatial and stochastic simulations of populations of cells, developed from first principle to support mechanistic investigations. The solver has been designed as a module in URDME, the unstructured reaction-diffusion master equation open software framework, to allow for the integration of intra-cellular models with extra-cellular features handled by the DLCM. The solver manages discrete cells on a fixed lattice and reaction-transport events in a continuous-time Markov chain. Space-continuous micro-environment quantities such as pressure and chemical substances are supported by the framework, permitting a variety of modeling choices concerning chemotaxis, mechanotaxis, nutrient-driven cell growth and death, among others. An essential and novel feature of the DLCM-solver is the coupling of cellular pressure to the curvature of the cell populations by elliptic projection onto the computational grid, with which we can include effects from surface tension between populations. We demonstrate the flexibility of the framework by implementing benchmark problems of cell sorting, cellular signaling, tumor growth, and chemotaxis models. We additionally formally analyze the computational complexity and show that it is theoretically optimal for systems based on pressure-driven cell migration. In summary, the solver balances efficiency and a relatively fine resolution, while supporting a high level of interpretability.

q-bio.QM↗

arXiv2024

Morphological stability for in silico models of avascular tumors

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principles silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework's flexibility, such additions can be readily included whenever the relevant data become available.

math.DS↗

arXiv2024

Modeling the hallmarks of avascular tumors

We present a stochastic computational model of avascular tumors, emphasizing the detailed implementation of the first four so-called hallmarks of cancer: self-sufficiency in growth factors, resistance to growth inhibitors, avoidance of apoptosis, and unlimited growth potential. Our goal is to provide a foundational understanding of the first steps of cancer malignancy while addressing modeling uncertainties, thus bringing us closer to a first-principles grasp of this process. Preliminary numerical simulations illustrate the comprehensiveness of our perspective.

q-bio.PE↗