Table 2.

Features of different model formalisms analyzed.

Modeling framework	Realism	Time	Scalability	Computational cost	Complexity	Data usage	Examples
ABM	Phenomenological	Continuous	Large	High	High	Low	Spatial simulation of cell-2-cell interaction and movement in a lung alveolus during infection
Boolean	Phenomenological	Discrete	Medium	Low	Low	Medium	Analysis and simulation of the large regulatory network triggered in macrophages after bacteria detection
ODE	Mechanistic	Continuous	Small	High	Medium	High	Analysis of fine-tuning of NFkB signaling activation in lung epithelial cells after infection
Stochastic	Mechanistic	Continuous	Small	High	High	High	Simulation of dynamics of few bacteria initiating infection in a lung alveolus
Fuzzy Logic	Phenomenological	Discrete	Medium	Medium	Low	Medium	Simulation of lung epithelial cell phenotypes with uncertain uncomplete information of activators

Realism: How close from the real biological mechanism is the representation given by the model; time: Whether the model handle the time as a discrete or continuous variable; scalability: Number of compounds the model can on average handle (small: up to 20 compounds, medium: 20–100, large 100–1,000; computational cost: Time and computational resources demanded for model simulation and analysis; complexity of the models in terms of their structure; data usage: Whether the construction of the model requires low, medium or large amounts of quantitative experimental data for its characterization; examples: Possible applications for each formalism in the context of bacterial lung infection.