Table 1.
Ten “not-yet-considered” motivations to use mathematical modeling in bacterial lung infections.
| Motivation | Usage in bacterial lung infection | Article of example |
|---|---|---|
| Discover new questions | Combination of model derivation, simulation and analysis used to formulate in a consistent manner a new hypothesis on the early steps of bacteria lung infection mechanism | Gillard et al., 2014 |
| Guide data collection | Model simulations used to (a) decide the design of the which most suited experiments to test the above hypothesis and (b) select relevant public available data | Thakar et al., 2007 |
| Explain (very distinct from predict) | Customized model simulations used to (a) illustrate the experimental results and (b) discuss/extrapolate the consequences of the proved or disproved hypothesis | Smith et al., 2011 |
| Illuminate core dynamics | A model comprising the core of the network controlling inflammation used to point the key molecules and processes controlling it | Krishna et al., 2006 |
| Reveal the apparently simple to be complex | The analysis of a model representing the apparently simple and small network controlling early bacterial lung infection used to suggest the existence of non-linear behavior associated to feedback loops circuits | Nikolov et al., 2010 |
| Reveal the apparently complex to be simple | Model reductions techniques on a large network representing bacterial lung infection applied to detect the few key processes and molecules controlling the process | Guo et al., 2011 |
| Expose prevailing wisdom as incompatible with available data | Simulations of a mathematical model encoding the current knowledge on molecular interactions controlling initiation of inflammation employed to show inconsistencies with new data | Hoffmann et al., 2002 |
| Bound outcomes to plausible ranges | Comparison between model simulations and available data used to establish the interval of biologically feasible features (parameters) for bacteria proliferation and spread in the lung alveoli | Mochan et al., 2014 |
| Offer crisis options in near-real time | For a patient entering the Intensive Care, personalized model simulations used to predict the course of the host-pathogen interactions and near-real time decide on the therapeutic alternatives | Dix et al., 2016 |