Environmental stress level to model tumor cell growth and survival

Sabrina Schönfeld, Alican Ozkan, Laura Scarabosio, Marissa Nichole Rylander, Christina Kuttler

Research output: Contribution to journalArticlepeer-review


Survival of living tumor cells underlies many influences such as nutrient saturation, oxygen level, drug concentrations or mechanical forces. Data-supported mathematical modeling can be a powerful tool to get a better understanding of cell behavior in different settings. However, under consideration of numerous environmental factors mathematical modeling can get challenging. We present an approach to model the separate influences of each environmental quantity on the cells in a collective manner by introducing the "environmental stress level". It is an immeasurable auxiliary variable, which quantifies to what extent viable cells would get in a stressed state, if exposed to certain conditions. A high stress level can inhibit cell growth, promote cell death and influence cell movement. As a proof of concept, we compare two systems of ordinary differential equations, which model tumor cell dynamics under various nutrient saturations respectively with and without considering an environmental stress level. Particle-based Bayesian inversion methods are used to quantify uncertainties and calibrate unknown model parameters with time resolved measurements of in vitro populations of liver cancer cells. The calibration results of both models are compared and the quality of fit is quantified. While predictions of both models show good agreement with the data, there is indication that the model considering the stress level yields a better fitting. The proposed modeling approach offers a flexible and extendable framework for considering systems with additional environmental factors affecting the cell dynamics.

Original languageEnglish
Pages (from-to)5509-5545
Number of pages37
JournalMathematical Biosciences and Engineering
Issue number6
StatePublished - 2022


  • Bayesian inversion
  • mathematical model
  • parameter estimation
  • tumor growth
  • uncertainty quantification


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