Numerical investigation of the impact of neural stem cells on brain tumors.
6 affiliations
- 1Department of Biomedical Engineering, Kampala International University, Western Campus, Bushenyi Ishaka, Uganda.
- 2Department of Electrical and Electronics Engineering, Mbarara University of Science and Technology, Kihumuro, Uganda.
- 3Department of Electrical, Telecommunication, and Computer Engineering, Kampala International University, Western Campus, Bushenyi Ishaka, Uganda.
- 4Department of Microbiology and Immunology, Kampala International University, Bushenyi Ishaka, Uganda.
- 5Department of Pharmacology and Toxicology, Kampala International University, Western Campus, Bushenyi Ishaka, Uganda.
- 6Department of Nursing Science, Kampala International University, Western Campus, Ishaka, Uganda.
Abstract
Malignant brain tumors, including glioblastoma and medulloblastoma, remain highly treatment-resistant. Neural stem cells (NSCs) are being investigated as tumor-tropic therapeutic vectors, but the temporal interaction between tumor and NSC populations requires quantitative characterization before model-based treatment hypotheses can be interpreted reliably. This study aimed to develop and numerically evaluate a reproducible 2-population model of tumor-NSC dynamics and to quantify the influence of NSC persistence and NSC-mediated tumor killing on simulated tumor burden. A normalized system of 2 coupled nonlinear ordinary differential equations was used: dT/dt = rT(1 - T) - βTN and dN/dt = αN(1 - N) - γTN. The baseline simulation used r = 0.20000 day-1, α = 0.05000 day-1, β = 0.35000 day-1, γ = 0.30000 day-1, T0 = 0.50000, and N0 = 0.40000. Explicit Euler integration was performed with Δt = 0.01 day over 20 days. A descriptive nonlinear regression fit, Fourier spectral analysis, 1-parameter sensitivity analyses, and joint α-β sensitivity analysis were applied to the simulated trajectory. In the baseline simulation, tumor density decreased from 0.5000 to a minimum of 0.4772 at day 2.59 (4.57% below the initial value) and subsequently increased to T20 = 0.8273, while NSC density declined from 0.4000 to N20 = 0.0247. The descriptive regression reproduced the simulated tumor trajectory with R2 = 0.9973 and root mean square error = 0.00590. Increasing α from 0.05 to 0.10 day-1 reduced T20 by 12.6% relative to baseline, and increasing β from 0.35 to 0.50 day-1 reduced T20 by 19.9%. Jointly setting α = 0.10 and β = 0.50 day-1 yielded T20 = 0.4427, 46.5% below the baseline terminal tumor density. The Fourier spectrum was dominated by the zero and low-frequency components, with no distinct oscillatory peak over the 20-day window. The model indicates that NSC persistence and NSC-mediated tumor killing materially influence terminal tumor burden, but the baseline parameterization produces only transient early suppression followed by tumor regrowth as the NSC population declines. The results are population-level, model-based estimates rather than tumor-type-specific clinical efficacy measures. Tumour-specific calibration, spatial migration, immune interactions, and experimental validation are required before translational conclusions can be drawn.
Related
- CA2 numbers and the Riemann hypothesis: a numerical investigation of Ramanujan's inequalities
- Numerical Investigation of Soft-to-Hard Convergence: Discretization, Admissibility, and Spectral Regularity
- Numerical investigation of a homogenized model with effective interface conditions for Stokes flow through porous membranes
- Numerical investigation of a Navier-Stokes-Korteweg system through an extended Lagrangian formulation: nonclassical waves and phase transition problems
- The effects of lateralized set presentation on numerical and feature-based judgments: An investigation across ANS, OTS, and VWM performance
BibTeX
@article{bubu2026numerical,
title = {{Numerical investigation of the impact of neural stem cells on brain tumors.}},
author = {Pius Erheyovwe Bubu and Val Hyginus Udoka Eze and Kelechi John Ukagwu and Vidya Sankarapandian and Saidi Odoma and Awafung Emmanuel Adie and Nansamba Fridah Mirembe},
journal = {Medicine},
year = {2026},
month = oct,
doi = {10.1097/md.0000000000051105},
url = {https://europepmc.org/article/MED/42888611},
note = {CC BY},
}
RIS
TY - JOUR
TI - Numerical investigation of the impact of neural stem cells on brain tumors.
AU - Pius Erheyovwe Bubu
AU - Val Hyginus Udoka Eze
AU - Kelechi John Ukagwu
AU - Vidya Sankarapandian
AU - Saidi Odoma
AU - Awafung Emmanuel Adie
AU - Nansamba Fridah Mirembe
PY - 2026
DA - 2026/10/01
JO - Medicine
DO - 10.1097/md.0000000000051105
UR - https://europepmc.org/article/MED/42888611
AB - Malignant brain tumors, including glioblastoma and medulloblastoma, remain highly treatment-resistant. Neural stem cells (NSCs) are being investigated as tumor-tropic therapeutic vectors, but the temporal interaction between tumor and NSC populations requires quantitative characterization before model-based treatment hypotheses can be interpreted reliably. This study aimed to develop and numerically evaluate a reproducible 2-population model of tumor-NSC dynamics and to quantify the influence of NSC persistence and NSC-mediated tumor killing on simulated tumor burden. A normalized system of 2 coupled nonlinear ordinary differential equations was used: dT/dt = rT(1 - T) - βTN and dN/dt = αN(1 - N) - γTN. The baseline simulation used r = 0.20000 day-1, α = 0.05000 day-1, β = 0.35000 day-1, γ = 0.30000 day-1, T0 = 0.50000, and N0 = 0.40000. Explicit Euler integration was performed with Δt = 0.01 day over 20 days. A descriptive nonlinear regression fit, Fourier spectral analysis, 1-parameter sensitivity analyses, and joint α-β sensitivity analysis were applied to the simulated trajectory. In the baseline simulation, tumor density decreased from 0.5000 to a minimum of 0.4772 at day 2.59 (4.57% below the initial value) and subsequently increased to T20 = 0.8273, while NSC density declined from 0.4000 to N20 = 0.0247. The descriptive regression reproduced the simulated tumor trajectory with R2 = 0.9973 and root mean square error = 0.00590. Increasing α from 0.05 to 0.10 day-1 reduced T20 by 12.6% relative to baseline, and increasing β from 0.35 to 0.50 day-1 reduced T20 by 19.9%. Jointly setting α = 0.10 and β = 0.50 day-1 yielded T20 = 0.4427, 46.5% below the baseline terminal tumor density. The Fourier spectrum was dominated by the zero and low-frequency components, with no distinct oscillatory peak over the 20-day window. The model indicates that NSC persistence and NSC-mediated tumor killing materially influence terminal tumor burden, but the baseline parameterization produces only transient early suppression followed by tumor regrowth as the NSC population declines. The results are population-level, model-based estimates rather than tumor-type-specific clinical efficacy measures. Tumour-specific calibration, spatial migration, immune interactions, and experimental validation are required before translational conclusions can be drawn.
SN - 0304-5412
SN - 1536-5964
ER -