Modeling

Citation Link Keywords Zitzmann C and Kaderali L (2018) Mathematical Analysis of Viral Replication Dynamics and Antiviral Treatment Strategies: From Basic Models to Age-Based Multi-Scale Modeling. Front. Microbiol. 9:1546. doi: 10.3389/fmicb.2018.01546 https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.01546/full mathematical modeling, viral kinetics, viral replication, human immunodeficiency virus, Hepatitis C virus, Influenza A virus, antiviral therapy, immune response Almansour S, Dunster JL, Crofts JJ, Nelson MR (2024) Modelling the continuum of macrophage phenotypes and their role in inflammation,
Mathematical Biosciences, Volume 377, 109289,
ISSN 0025-5564, https://doi.org/10.1016/j.mbs.2024.109289. https://www.sciencedirect.com/science/article/pii/S0025556424001494 mathematical modeling, macrophages and inflammation, Bifurcation analysis, PDE Chathoth K, Fostier L, Martin B, Baysse C, Mahé F (2022) A Multi-Skilled Mathematical Model of Bacterial Attachment in Initiation of Biofilms. Microorganisms,10(4):686. https://doi.org/10.3390/microorganisms10040686 https://www.mdpi.com/2076-2607/10/4/686 biofilm, bacterial attachment, mathematical model, stochastic, 2D and 3D Schmid N, Fernandes Del Pozo D, Waegeman W, Hasenauer J (2025) Assessment of uncertainty quantification in universal differential equations. Philos Trans A Math Phys Eng Sci; 383(2293):20240444. doi:10.1098/rsta.2024.0444 https://pubmed.ncbi.nlm.nih.gov/40172556/ uncertainty quantification, universal differential equations, scientific machine learning Maddu S, Cheeseman BL, Sbalzarini IF, Müller CL (2022) Stability selection enables robust learning of differential equations from limited noisy data. Proc. A; 478 (2262): 20210916. https://doi.org/10.1098/rspa.2021.0916 https://royalsocietypublishing.org/rspa/article/478/2262/20210916/54488/Stability-selection-enables-robust-learning-of stability selection, sparse regression, PDE identification Heinrich V, Simpson WD 3rd, Francis EA (2017) Analytical Prediction of the Spatiotemporal Distribution of Chemoattractants around Their Source: Theory and Application to Complement-Mediated Chemotaxis. Front Immunol.; 8:578. Published 2017 May 26. doi:10.3389/fimmu.2017.00578 https://pmc.ncbi.nlm.nih.gov/articles/PMC5445147/ chemotaxis, reaction–diffusion, mathematical model, single-cell, host–pathogen Niemann J-H, Klus S, Schütte C (2021) Data-driven model reduction of agent-based systems using the Koopman generator. PLoS ONE 16(5): e0250970. https://doi.org/10.1371/journal.pone.0250970 https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0250970 ABM, PDEs, data-driven reduction Lorenzi T, Painter KJ (2025) Pattern formation within phenotype-structured chemotactic populations. Proc. A 1; 481 (2324): 20250483. https://doi.org/10.1098/rspa.2025.0483 https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2025.0483 PDEs, pattern formation, chemotaxis, non-local advection-diffusion-reaction eqs.

Kejie C, Kai-Rong O (2021) Random Walks of a Cell With Correlated Speed and Persistence Influenced by the Extracellular Topography, Frontiers in Physics, Volume 9, 10.3389/fphy.2021.719293

https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.719293/full Random walks, complex environments, PRWs, Cell migration

Ohno K, Kobayashi Y, Uesaka M et al. (2021) A computational model of the epidermis with the deformable dermis and its application to skin diseases. Sci Rep 11, 13234. https://doi.org/10.1038/s41598-021-92540-1

https://www.nature.com/articles/s41598-021-92540-1 ABM, skin modelling, skin disease, cellular layer