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The Lotka-Volterra model is widely used to model interactions between twospecies. Here, we generate synthetic data mimicking competitive, mutualisticand antagonistic interactions between two tumor cell lines, and then use theLotka-Volterra model to infer the interaction type. Structural identifiabilityof the Lotka-Volterra model is confirmed, and practical identifiability isassessed for three experimental designs: (a) use of a single data set, with amixture of both cell lines observed over time, (b) a sequential design wheregrowth rates and carrying capacities are estimated using data from experimentsin which each cell line is grown in isolation, and then interaction parametersare estimated from an experiment involving a mixture of both cell lines, and(c) a parallel experimental design where all model parameters are fitted todata from two mixtures simultaneously. In addition to assessing each design forpractical identifiability, we investigate how the predictive power of themodel-i.e., its ability to fit data for initial ratios other than those towhich it was calibrated-is affected by the choice of experimental design. Theparallel calibration procedure is found to be optimal and is further tested onin silico data generated from a spatially-resolved cellular automaton model,which accounts for oxygen consumption and allows for variation in the intensitylevel of the interaction between the two cell lines. We use this study tohighlight the care that must be taken when interpreting parameter estimates forthe spatially-averaged Lotka-Volterra model when it is calibrated against dataproduced by the spatially-resolved cellular automaton model, since baselinecompetition for space and resources in the CA model may contribute to adiscrepancy between the type of interaction used to generate the CA data andthe type of interaction inferred by the LV model.

Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types