On the growth and dissemination laws in a mathematical model of metastatic growth - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

On the growth and dissemination laws in a mathematical model of metastatic growth

Abstract

Metastasis represents one of the main clinical challenge in cancer treatment since it is associated with the majority of deaths. Recent technological advances allow quantification of the dynamics of the process by means of noninvasive techniques such as longitudinal tracking of bioluminescent cells. The metastatic process was simplified here into two essential components – dissemination and colonization – which were mathematically formalized in terms of simple quantitative laws. The resulting mathematical model was confronted to in vivo experimental data of spontaneous metastasis after primary tumor resection. We discuss how much information can be inferred from confrontation of theories to the data with emphasis on identifiability issues. It is shown that two mutually exclusive assumptions for the secondary growth law (namely same or different from the primary tumor growth law) could fit equally well the data. Similarly, the fractal dimension coefficient in the dissemination law could not be uniquely determined from data on total metastatic burden only. Together, these results delimitate the range of information that can be recovered from fitting data of metastatic growth to already simplified mathematical models.
Fichier principal
Vignette du fichier
benzekry_ebos.pdf (638.21 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01222088 , version 1 (30-10-2015)

Licence

Attribution - NonCommercial

Identifiers

Cite

Sébastien Benzekry, John M.L. Ebos. On the growth and dissemination laws in a mathematical model of metastatic growth. Workshop on Multiscale and Hybrid Modelling in Cell and Cell Population Biology, Mar 2015, Paris, France. ⟨10.1051/itmconf/20150500007⟩. ⟨hal-01222088⟩
286 View
131 Download

Altmetric

Share

Gmail Facebook X LinkedIn More