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Survival of living tumor cells underlies many influences such as nutrientsaturation, oxygen level, drug concentrations or mechanical forces.Data-supported mathematical modeling can be a powerful tool to get a betterunderstanding of cell behavior in different settings. However, underconsideration of numerous environmental factors mathematical modeling can getchallenging. We present an approach to model the separate influences of eachenvironmental quantity on the cells in a collective manner by introducing the"environmental stress level". It is an immeasurable auxiliary variable, whichquantifies to what extent viable cells would get in a stressed state, ifexposed to certain conditions. A high stress level can inhibit cell growth,promote cell death and influence cell movement. As a proof of concept, wecompare two systems of ordinary differential equations, which model tumor celldynamics under various nutrient saturations respectively with and withoutconsidering an environmental stress level. Particle-based Bayesian inversionmethods are used to quantify uncertainties and calibrate unknown modelparameters with time resolved measurements of in vitro populations of livercancer cells. The calibration results of both models are compared and thequality of fit is quantified. While predictions of both models show goodagreement with the data, there is indication that the model considering thestress level yields a better fitting. The proposed modeling approach offers aflexible and extendable framework for considering systems with additionalenvironmental factors affecting the cell dynamics.

Environmental stress level to model tumor cell growth and survival