Local Controllability Analysis for a Tumor Dynamics Model

Local Controllability Analysis for a Tumor Dynamics Model

Authors

DOI:

https://doi.org/10.5433/1679-0375.2025.v46.53579

Keywords:

cancer modeling, controllability theory, Kalman rank condition, mathematical oncology

Abstract

This study investigates the local controllability of a mathematical model describing tumor dynamics, incorporating cellular competition and external interventions represented by a control variable. The model analyzed is a modified version of the formulation proposed by Gatenby (1996), and its dynamics are explored through linearization around equilibrium points. Using the Kalman rank condition, we establish the circumstances under which the system exhibits local controllability. The results provide a theoretical foundation for understanding how external stimuli can steer the system between distinct biological states, particularly in the context of interactions between healthy and tumor cells. The work bridges concepts from control theory and mathematical oncology, aiming to support the development of more effective clinical intervention strategies. Our analysis offers insights into identifying scenarios in which treatment, modeled as an external control input, can effectively stabilize or even reverse tumor growth.

Downloads

Download data is not yet available.

Author Biography

Francis Felix Cordova Puma, Universidade Federal de Santa Catarina

Prof. Dr., Department of Mathematics, Federal University of Santa Catarina (UFSC), Blumenau Campus, Santa Catarina, Brazil

References

Baumeister, J., & Leitão, A. (2014). Introdução à teoria de controle e programação dinâmica. Instituto Nacional de Matemática Pura e Aplicada.

Bellomo, N., Li, N., & Maini, P. K. (2008). On the foundations of cancer modelling: Selected topics, speculations, and perspectives. Mathematical Models and Methods in Applied Sciences, 18(4), 593–646. https://doi.org/10.1142/S0218202508002796

Bray, F., Laversanne, M., Weiderpass, E., & Soerjomataram, I. (2021). The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer, 127(16), 3029–3030. https://doi.org/10.1002/cncr.33587

Byrne, H. M., & Chaplain, M. A. J. (1995). Growth of nonnecrotic tumors in the presence and absence of inhibitors. Mathematical Biosciences, 130(2), 151–181. https://doi.org/10.1016/0025-5564(94)00117-3

Coron, J. M. (2007). Control and nonlinearity. American Mathematical Soc. https://www.ljll.fr/~coron/Documents/Coron-book.pdf

Cristini, V., & Lowengrub, J. (2010). Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach. https://doi.org/10.1017/CBO9780511781452

De Pillis, L. G., & Radunskaya, A. E. (2003). The dynamics of an optimally controlled tumor model: A case study. Mathematical and Computer Modelling, 37(11), 1221–1244. https://doi.org/10.1016/S0895-7177(03)00133-X

Doering, C. I., & Lopes, A. O. (2016). Equações Diferenciais Ordinárias (Coleção Matemática Universitária). IMPA.

El-Gohary, A., & Yassen, M. (2001). Optimal control and synchronization of Lotka–Volterra model. Chaos, Solitons & Fractals, 12(11), 2087–2093. https://doi.org/10.1016/S0960-0779(00)00023-0

Gatenby, R. A. (1996). Application of competition theory to tumour growth: Implications for tumour biology and treatment. European Journal of Cancer, 32(4), 722–726. https://doi.org/10.1016/0959-8049(95)00658-3

Gatenby, R. A., & Gawlinski, E. T. (1996). A Reaction-Diffusion Model of Cancer Invasion [PubMed PMID: 8971186]. Cancer Research, 56(24), 5745–5753. https://pubmed.ncbi.nlm.nih.gov/8971186/

Lotka, A. J. (1925). Elements of Physical Biology. Williams & Wilkins. https://archive.org/details/elementsofphysic017171mbp

Murray, J. D. (2002). Mathematical Biology I: An Introduction (3rd ed.). Springer. https://doi.org/10.1007/b98868

Nave, O. (2022). A mathematical model for treatment using chemo-immunotherapy. Heliyon, 8(4), e09288. https://doi.org/10.1016/j.heliyon.2022.e09288

Puma, F. F. C., & Henarejsos, A. W. (2024). Controlabilidade local para um modelo Lotka-Volterra. REMAT: Revista Eletrônica da Matemática, 10(1), e3003. https://doi.org/10.35819/remat2024v10i1id6923

Rodrigues, D. S., Mancera, P. F. A., & Pinho, S. T. R. (2011). Modelagem Matemática em Câncer e Quimioterapia: Uma Introdução (Vol. 58). SBMAC. https://www.sbmac.org.br/wp-content/uploads/2022/08/livro_58.pdf

Roose, T., Chapman, S. J., & Maini, P. K. (2007). Mathematical Models of Avascular Tumor Growth. SIAM Review, 49(2), 179–208. https://doi.org/10.1137/S0036144504446291

Salvador, J. A., & Arenales, S. (2022). Modelagem Matemática Ambiental. Editora da Universidade Federal de São Carlos.

Volterra, V. (1931). Leçons sur la Théorie Mathématique de la Lutte pour la Vie. Gauthier-Villars.

Wang, X., Zanette, L., & Zou, X. (2016). Modelling the fear effect in predator–prey interactions. Mathematical Models and Methods in Applied Sciences, 73, 1179–1204. https://doi.org/10.1007/s00285-016-0989-1

Zhang, S., Yuan, S., & Zhang, T. (2022). A predator-prey model with different response functions to juvenile and adult prey in deterministic and stochastic environments. Applied Mathematics and Computation, 413, 126598. https://doi.org/10.1016/j.amc.2021.126598

Downloads

Published

2025-12-03

How to Cite

Puma, F. F. C. (2025). Local Controllability Analysis for a Tumor Dynamics Model. Semina: Ciências Exatas E Tecnológicas, 46, e53579. https://doi.org/10.5433/1679-0375.2025.v46.53579

Issue

Section

Biomathematics (Special section)
Loading...